TouchPaper - An Augmented Reality Application with Cloud-Based Image Recognition Service

Size: px
Start display at page:

Download "TouchPaper - An Augmented Reality Application with Cloud-Based Image Recognition Service"

Transcription

1 TouchPaper - An Augmented Reality Application with Cloud-Based Image Recognition Service Feng Tang, Daniel R. Tretter, Qian Lin HP Laboratories HPL R1 Keyword(s): image recognition; cloud service; augmented reality; Abstract: Augmented reality applications are increasingly used to enhance physical objects with digital information. In this paper, we present a TouchPaper system that consists of a mobile application and a cloud-based image recognition service. Through an authoring system, images can be linked to online content that will be invoked when a printed version of the image is captured and "touched" on a mobile phone screen. We describe a sbip (Structured Binary Intensity Pattern) algorithm that performs the matching of the images and evaluate the performance of the overall TouchPaper system. External Posting Date: September 6, 2012 [Fulltext] Approved for External Publication Internal Posting Date: September 6, 2012 [Fulltext] Published in ICIMCS 2012: 4th International Conference on Internet Multimedia Computing and Service Copyright ICIMCS 2012: 4th International Conference on Internet Multimedia Computing and Service

2 TouchPaper An Augmented Reality Application with Cloud-Based Image Recognition Service Feng Tang Daniel R. Tretter Qian Lin ABSTRACT Augmented reality applications are increasingly used to enhance physical objects with digital information. In this paper, we present a TouchPaper system that consists of a mobile application and a cloud-based image recognition service. Through an authoring system, images can be linked to online content that will be invoked when a printed version of the image is captured and touched on a mobile phone screen. We describe a sbip (Structured Binary Intensity Pattern) algorithm that performs the matching of the images and evaluate the performance of the overall TouchPaper system. Categories and Subject Descriptors J.7. [Computer Application] Computers in Other Systems - Publishing General Terms Management, Design, Experimentation. Keywords multimedia, digital photography, image analysis, augmented reality. 1. Introduction Barcodes are gaining popularity as a way to link physical objects to digital content. This is largely driven by the rapid growth of SmartPhones with cameras. More sophisticated technologies based on image recognition have been developed to compute image features of the physical object, and match them to images in a database to provide the linking. Not using explicit barcodes dramatically improves the flexibility and visual appeal of the experience. In addition, augmented reality applications can be developed that track image features and adapt the display of digital augmentations accordingly. One such example is the Aurasma application 1. To create the best user experience, the matching of captured scenes with target images needs to be as fast as possible. For this, we need compact image features with a low computation complexity. In this paper, we describe an efficient image feature, called sbip (Structured Binary Intensity Pattern). This feature Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ICIMCS 12, September 9 11, 2012, Wuhan, Hubei, China. Copyright 2012 ACM /12/09 $ descriptor is highly efficient to compute as it only involves pixel intensity comparisons. It is also highly compact as the descriptor is formed by a set of binary comparison outputs. These properties make it very fast to compute and highly compact, which makes it very suitable for mobile applications. As an application of the proposed descriptor, we describe the concept of Touchpaper to craft more compelling and richer interactive media experiences directly from traditional printed materials. Printed materials such as books, magazines, reports and marketing collateral are often created from digital components. While it is straightforward to embed static content (e.g. text, photos, and illustrations), it is difficult to embed dynamic content (e.g. audio, video, and animation) into these materials. Thus, such dynamic content is left out when prints are created from digital content, and the resulting collateral lacks any direct mapping to more dynamic material. With the proposed Touchpaper system, the printed content (e.g. greeting card, photobook, etc.) itself serves as a visual trigger when placed in front of a device s camera to an interactive interface where the user can click on regions of interest for more information. We created am authoring interface so that the user can upload their photos and specify the active regions and their associated links (for example, the corresponding Facebook page of the photo for comments.). The interactive interface and information can also be generated automatically during the document creation process. 2. Structured Binary Intensity Pattern (sbip) One of core problems of an image recognition system is how to represent the image. We use a local feature based approach where the image is represented by a set of sparse local keypoints. The feature ensemble collectively captures the most salient parts of the image. The local feature has two components: feature detector and feature descriptor. The feature detector locates the distinctive keypoints in an image, such as corners or edges. A descriptor is usually computed from a local patch centered at each keypoint to describe the local visual appearance. This descriptor is designed to be invariant to perspective changes, illumination changes, JPEG compression, and other common distortions. We use the FAST (Features from Accelerated Segment Test) [3] corner detector for feature detection due to its low computational complexity. Popular feature descriptor SIFT [4] is effective in image matching and recognition, but the high computational complexity prevents it from being useful in mobile applications. SURF [5] is a significant improvement in terms of speed compared to SIFT, but it is still not fast enough for near-realtime mobile recognition. BRIEF [6] compares randomly selected pixels to generate a descriptor for image matching, which is very fast to compute. However, the random selection of the pixels ignores the

3 structure of the image patch which makes it less robust. The OSID feature descriptor [7] is designed to be invariant to brightness changes by using ordinal pixel information, but the ordering of pixels makes it less efficient. In this paper, we develop a feature descriptor that is very robust to image changes while remaining highly efficient to compute. Unlike OSID which compares a pixel to a set of intensity values (ordinal bin boundaries), our approach compares a pixel to only one intensity value, relaxing the ordering constraint while retaining the invariance property. The proposed feature descriptor divides an image patch into structured subpatches and compares each subpatch to a predefined set of anchor points to form a binary descriptor for the patch. We call our descriptor Structured Binary Intensity Pattern(sBIP). More specifically, given an image patch centered at a keypoint, the proposed descriptor is computed as follows: 1) Anchor points selection: we select a set of K (for example 16) anchor points within the patch (denoted as orange dots in Fig 1. 2) For each anchor point, a small subpatch centered on that anchor point is selected and the average/sum of the pixels in this subpatch is computed. 3) Divide the image patch into m n (for example 4 4 subpatches as shown in the figure below. 4) Compare every subpatch to each of the anchor point patches. If it is brighter, output 1; otherwise, output 0. For each anchor point, this will result in an m n sequence of 0s and 1s. 5) Concatenate the binary sequences formed from all the anchor points into a long binary vector as the descriptor for this patch. This process is illustrated in Figure 1. The final feature dimension will be K m n = 16 * 4 * 4 = 256. Compared to the SURF descriptor [3], our feature is almost 10 times faster to compute while requiring 1/8 the storage space. Our feature descriptor has the following advantages: 1. It is highly efficient to compute because all computations only involve integer comparisons. 2. It is highly compact in that the descriptor is a binary string instead of floating points. This makes it efficient for storage and transmission. 3. It is invariant to arbitrary monotonically increasing brightness changes. Figure 1. Our feature descriptor computation Figure 2. An example of matching feature points between the captured print image and the digital image. 3. Fast image matching using sbip When the user captures a photo of a printed page, features are extracted and sent to the server to match to a database of images. Since there may be a large number of photos in the database, the naïve approach of matching the query image to each of the database images using bi-partite matching is often too slow for a quick response. Instead, we use a bag-of-features approach [8] for fast search. In addition, we use geometric verification [9] as an additional step to filter out the incorrectly matched images from the top ranked photos to increase the recognition accuracy. More specifically, the algorithm works as follows: Feature extraction: for each photo in the database, sbip features are extracted. For a VGA resolution photo, features are detected. Feature clustering: all the features detected across images in the database are aggregated to form the codewords using descriptor clustering to form the bag-of-features representation. These codewords are computed by using an approximate K-means approach. The approximate K-means works by accelerating the traditional K-means using a randomized KD tree algorithm [10]. In each iteration of the traditional K-means, the distances between all the features and each of the cluster centers are computed. This process is the major bottleneck for the clustering. In each iteration of the approximate K-means, a randomized tree structure is constructed on all the cluster centers, so that the distance computation between a feature descriptor and the cluster centers can be computed very efficiently. These clusters form the visual codewords for all the images of the database. In our experiments, fix the number of clusters to be Bag-of-features representation: For each image in the database and also the query image, the extracted features are compared with the codewords. Each feature will find its nearest codewords to form the term frequency histogram. This histogram is a fixed length sparse representation of the feature statistics. To make it more robust, an inverse document frequency (idf) [11] is used to weight the term frequency with the intuition that more frequent features are less useful for the search. Image search: For a query image, the term frequency vector is computed and compare with the database to obtain a top K most similar images. This is done using the Euclidean distance measure. In our experiments, we set K to be 5. Geometric verification: After the top K images are found, a geometric verification is applied to check the consistency of the feature distribution between the query and the candidate image. RANSAC [12] is used to estimate the homography transformation and the number of in-lier matches.

4 4. TouchPaper System We implemented the sbip algorithm in a hybrid mobile cloud system called TouchPaper. Starting with an image, we use the TouchPaper online portal to mark up the regions on the image for augmentation, as well as to specify the associated URLs. After the image is printed, the TouchPaper mobile app captures the image, runs sbip, and sends the features to the TouchPaper online server. The online server performs feature matching, and highlights the marked regions on the mobile device screen. The user can touch each of the highlighted regions to view the online content. Our system consists of two major components as shown in Figure 3: the image recognition cloud service that recognizes photos captured by mobile device and the mobile client that enables the interactive experience. On the mobile client, when the user captures an image of a print, a fingerprint is extracted and sent to the server for recognition. The server maintains a database of fingerprints and the links associated with each photo. Once the query fingerprint is recognized, additional content such as active regions and URLs are sent back to the client. The user then can click hotspots to view linked content. The online service software is a custom-built, high-performance, in-memory database that runs in the cloud. The search system is highly-scalable and efficient allowing large reference libraries. It also supports real time workflows to add and remove signatures from the database. 5. User interface Our system has two main user interfaces: one is the mobile client (iphone app) where the user can point to the print and access the additional content; the other is the authoring portal where the user can upload their own photos and specify the active regions and their corresponding links. 5.1 Mobile interface One of the design principles of the user interface is simplicity. The user points the smartphone at a photobook page or collage and the system recognizes the page. The recognition process happens in the cloud and the image ID together with the estimated homography are transmitted to the client. Active regions associated with the page are highlighted with transparent overlays, as shown in Fig. 4. The user can then click within each region to see the additional information (for example, comments on Facebook). In this process, the homography is used to map the user clicked coordinates to the original image space. After browsing the augmented content, the user can return to the captured page and click other regions if desired. The user can return to the capture mode by closing the current capture using the icon at the top right corner, as shown in Figure 4. Figure 3. Touchpaper System Architecture Figure 4. User interface for the Touchpaper mobile client. 5.2 Authoring portal We also provide an authoring portal where the user can upload their own photos and specify the active regions and their links. An example of this authoring process is shown in Figure 5. This online portal also supports addition and deletion of existing images from the database as well as addition/removal/editing of the active regions and their links. These links can be arbitrary URLs on the web, like a Facebook link for the photo so that the user can read comments, or a Youtube video associated with the photo.

5 Figure 5. An example of a photobook image (left), and the regions marked for detection, as well as the associated URLs (right). 6. Results We conducted extensive testing of the computation time for the sbip algorithm, the computation time for the TouchPaper system in a realistic mobile environment, as well as the accuracy of the matching. We will discuss our results in this section. 6.1 sbip Computation Time and Feature Size Since the sbip algorithm only involves additions and subtractions, it is very fast to compute. Table 1 shows the performance of the sbip algorithm compared with SURF on extracting features and matching two VGA resolution images. Figure 6: Lighting variation tests. 7. Conclusion In this paper, we proposed a system called TouchPaper which can enable multiple active regions on a printed page such that when viewed using a mobile device, the user can click on different regions on the screen to access augmented content. This system is demonstrated through an iphone app coupled with a cloud computing infrastructure. References milliseconds Feature extraction Matching Total [1] J. He, et.al Mobile Product Search with Bag of Hash Bits, In Proceedings of ACM Multimedia., sbip feature SURF(64) [2] Q. Liu, et al, Embedded media markers: marks on paper that signify associated media. In Proceedings of the 15th international conference on Intelligent user interfaces (IUI '10). ACM, New York, NY, USA, Table 1: sbip performance compared with SURF for 640x480 images on a 2.8 GHz CPU Another advantage of sbip is that the feature size is very compact. It takes 32 Bytes to describe a feature, as compared with 256 Bytes for SURF. 6.2 TouchPaper System Performance We tested with a database of 2000 images by computing their sbip features and storing them on the server. 101 images in this database were printed out as 4 x6 prints and captured with our TouchPaper iphone client. With the combination of global features, sbip features, and verification, we were able to correctly match 97 images with the corresponding images in the database. The accuracy is 96.03%, with 0% false positives. The average system speed on an ipodtouch 4 is as follows: feature detector: 0.12s; feature descriptor: 0.11s; transmission plus matching: 1.10s; total time: 1.33s. The fast response time we were able to accomplish made the user interaction experience very good. 6.3 Lighting Variation Tests One of the challenges of augmented reality applications is the unknown lighting environment. Figure 6(a) shows the original image in the database. Figure 6(b) shows the image of the printout captured by the iphone. Figure 6(c) shows the same image captured under low light conditions. Figure 6(d)(e)(f) shows three examples of correct image matching by our TouchPaper system under the low lighting conditions. [3] E.Rosten, T. Drummond: Machine learning for highspeed corner detection. In Proceedings of the ECCV- Volume Part I, Springer-Verlag, Berlin, Heidelberg, [4] Lowe, David G Object Recognition from Local ScaleInvariant Features. In Proceedings of ICCV, Vol. 2. IEEE Computer Society, Washington, DC, USA, [5] H. Bay et al., Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110,3, [6] M. Calonder, V. Lepetit, C. Strecha, P. Fua: BRIEF: binary robust independent elementary features. In Proceedings of ECCV, Springer-Verlag, Berlin, Heidelberg, [7] F. Tang, S.H. Lim, N.L. Chang, and H. Tao, 2009 A novel feature descriptor invariant to complex brightness changes. In Proceedings of CVPR., [8] J. Sivic and A. Zisserman Video Google: A Text Retrieval Approach to Object Matching in Videos. In Proceedings of the ICCV- Vol [9] J. Philbin, O. Chum, M. Isard, J. Sivic, A. Zisserman: "Object retrieval with large vocabularies and fast spatial matching," CVPR, IEEE Conference on, vol., no., pp.1-8 [10] Silpa-Anan, C.; Hartley, R.; 2008, "Optimised KD-trees for fast image descriptor matching," CVPR. IEEE Conference on, vol., no., pp.1-8, [11] K. Sparck Jones A statistical interpretation of term specificity and its application in retrieval. In Document retrieval systems, Peter Willett (Ed.). Taylor Graham Series In Foundations Of Information Science, Vol. 3. Taylor Graham Publishing, London, UK, UK

Fast Matching of Binary Features

Fast Matching of Binary Features Fast Matching of Binary Features Marius Muja and David G. Lowe Laboratory for Computational Intelligence University of British Columbia, Vancouver, Canada {mariusm,lowe}@cs.ubc.ca Abstract There has been

More information

Recognition. Sanja Fidler CSC420: Intro to Image Understanding 1 / 28

Recognition. Sanja Fidler CSC420: Intro to Image Understanding 1 / 28 Recognition Topics that we will try to cover: Indexing for fast retrieval (we still owe this one) History of recognition techniques Object classification Bag-of-words Spatial pyramids Neural Networks Object

More information

Build Panoramas on Android Phones

Build Panoramas on Android Phones Build Panoramas on Android Phones Tao Chu, Bowen Meng, Zixuan Wang Stanford University, Stanford CA Abstract The purpose of this work is to implement panorama stitching from a sequence of photos taken

More information

A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow

A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow , pp.233-237 http://dx.doi.org/10.14257/astl.2014.51.53 A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow Giwoo Kim 1, Hye-Youn Lim 1 and Dae-Seong Kang 1, 1 Department of electronices

More information

FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION

FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION Marius Muja, David G. Lowe Computer Science Department, University of British Columbia, Vancouver, B.C., Canada mariusm@cs.ubc.ca,

More information

3D Model based Object Class Detection in An Arbitrary View

3D Model based Object Class Detection in An Arbitrary View 3D Model based Object Class Detection in An Arbitrary View Pingkun Yan, Saad M. Khan, Mubarak Shah School of Electrical Engineering and Computer Science University of Central Florida http://www.eecs.ucf.edu/

More information

Augmented Reality Tic-Tac-Toe

Augmented Reality Tic-Tac-Toe Augmented Reality Tic-Tac-Toe Joe Maguire, David Saltzman Department of Electrical Engineering jmaguire@stanford.edu, dsaltz@stanford.edu Abstract: This project implements an augmented reality version

More information

BRIEF: Binary Robust Independent Elementary Features

BRIEF: Binary Robust Independent Elementary Features BRIEF: Binary Robust Independent Elementary Features Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua CVLab, EPFL, Lausanne, Switzerland e-mail: firstname.lastname@epfl.ch Abstract.

More information

The use of computer vision technologies to augment human monitoring of secure computing facilities

The use of computer vision technologies to augment human monitoring of secure computing facilities The use of computer vision technologies to augment human monitoring of secure computing facilities Marius Potgieter School of Information and Communication Technology Nelson Mandela Metropolitan University

More information

ENHANCED WEB IMAGE RE-RANKING USING SEMANTIC SIGNATURES

ENHANCED WEB IMAGE RE-RANKING USING SEMANTIC SIGNATURES International Journal of Computer Engineering & Technology (IJCET) Volume 7, Issue 2, March-April 2016, pp. 24 29, Article ID: IJCET_07_02_003 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=7&itype=2

More information

GPS-aided Recognition-based User Tracking System with Augmented Reality in Extreme Large-scale Areas

GPS-aided Recognition-based User Tracking System with Augmented Reality in Extreme Large-scale Areas GPS-aided Recognition-based User Tracking System with Augmented Reality in Extreme Large-scale Areas Wei Guan Computer Graphics and Immersive Technologies Computer Science, USC wguan@usc.edu Suya You Computer

More information

Local features and matching. Image classification & object localization

Local features and matching. Image classification & object localization Overview Instance level search Local features and matching Efficient visual recognition Image classification & object localization Category recognition Image classification: assigning a class label to

More information

CODING MODE DECISION ALGORITHM FOR BINARY DESCRIPTOR CODING

CODING MODE DECISION ALGORITHM FOR BINARY DESCRIPTOR CODING CODING MODE DECISION ALGORITHM FOR BINARY DESCRIPTOR CODING Pedro Monteiro and João Ascenso Instituto Superior Técnico - Instituto de Telecomunicações ABSTRACT In visual sensor networks, local feature

More information

MusicGuide: Album Reviews on the Go Serdar Sali

MusicGuide: Album Reviews on the Go Serdar Sali MusicGuide: Album Reviews on the Go Serdar Sali Abstract The cameras on mobile phones have untapped potential as input devices. In this paper, we present MusicGuide, an application that can be used to

More information

FACE RECOGNITION BASED ATTENDANCE MARKING SYSTEM

FACE RECOGNITION BASED ATTENDANCE MARKING SYSTEM Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 2, February 2014,

More information

Image Compression through DCT and Huffman Coding Technique

Image Compression through DCT and Huffman Coding Technique International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2015 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Rahul

More information

siftservice.com - Turning a Computer Vision algorithm into a World Wide Web Service

siftservice.com - Turning a Computer Vision algorithm into a World Wide Web Service siftservice.com - Turning a Computer Vision algorithm into a World Wide Web Service Ahmad Pahlavan Tafti 1, Hamid Hassannia 2, and Zeyun Yu 1 1 Department of Computer Science, University of Wisconsin -Milwaukee,

More information

Distributed Kd-Trees for Retrieval from Very Large Image Collections

Distributed Kd-Trees for Retrieval from Very Large Image Collections ALY et al.: DISTRIBUTED KD-TREES FOR RETRIEVAL FROM LARGE IMAGE COLLECTIONS1 Distributed Kd-Trees for Retrieval from Very Large Image Collections Mohamed Aly 1 malaa@vision.caltech.edu Mario Munich 2 mario@evolution.com

More information

Video Authentication for H.264/AVC using Digital Signature Standard and Secure Hash Algorithm

Video Authentication for H.264/AVC using Digital Signature Standard and Secure Hash Algorithm Video Authentication for H.264/AVC using Digital Signature Standard and Secure Hash Algorithm Nandakishore Ramaswamy Qualcomm Inc 5775 Morehouse Dr, Sam Diego, CA 92122. USA nandakishore@qualcomm.com K.

More information

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic

More information

A Novel Way of Deduplication Approach for Cloud Backup Services Using Block Index Caching Technique

A Novel Way of Deduplication Approach for Cloud Backup Services Using Block Index Caching Technique A Novel Way of Deduplication Approach for Cloud Backup Services Using Block Index Caching Technique Jyoti Malhotra 1,Priya Ghyare 2 Associate Professor, Dept. of Information Technology, MIT College of

More information

Android Ros Application

Android Ros Application Android Ros Application Advanced Practical course : Sensor-enabled Intelligent Environments 2011/2012 Presentation by: Rim Zahir Supervisor: Dejan Pangercic SIFT Matching Objects Android Camera Topic :

More information

Cloud-Based Image Coding for Mobile Devices Toward Thousands to One Compression

Cloud-Based Image Coding for Mobile Devices Toward Thousands to One Compression IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 4, JUNE 2013 845 Cloud-Based Image Coding for Mobile Devices Toward Thousands to One Compression Huanjing Yue, Xiaoyan Sun, Jingyu Yang, and Feng Wu, Senior

More information

Face Recognition in Low-resolution Images by Using Local Zernike Moments

Face Recognition in Low-resolution Images by Using Local Zernike Moments Proceedings of the International Conference on Machine Vision and Machine Learning Prague, Czech Republic, August14-15, 014 Paper No. 15 Face Recognition in Low-resolution Images by Using Local Zernie

More information

Big Data: Image & Video Analytics

Big Data: Image & Video Analytics Big Data: Image & Video Analytics How it could support Archiving & Indexing & Searching Dieter Haas, IBM Deutschland GmbH The Big Data Wave 60% of internet traffic is multimedia content (images and videos)

More information

Image Retrieval for Image-Based Localization Revisited

Image Retrieval for Image-Based Localization Revisited SATTLER et al.: IMAGE RETRIEVAL FOR IMAGE-BASED LOCALIZATION REVISITED 1 Image Retrieval for Image-Based Localization Revisited Torsten Sattler 1 tsattler@cs.rwth-aachen.de Tobias Weyand 2 weyand@vision.rwth-aachen.de

More information

Classifying Manipulation Primitives from Visual Data

Classifying Manipulation Primitives from Visual Data Classifying Manipulation Primitives from Visual Data Sandy Huang and Dylan Hadfield-Menell Abstract One approach to learning from demonstrations in robotics is to make use of a classifier to predict if

More information

Randomized Trees for Real-Time Keypoint Recognition

Randomized Trees for Real-Time Keypoint Recognition Randomized Trees for Real-Time Keypoint Recognition Vincent Lepetit Pascal Lagger Pascal Fua Computer Vision Laboratory École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne, Switzerland Email:

More information

Cees Snoek. Machine. Humans. Multimedia Archives. Euvision Technologies The Netherlands. University of Amsterdam The Netherlands. Tree.

Cees Snoek. Machine. Humans. Multimedia Archives. Euvision Technologies The Netherlands. University of Amsterdam The Netherlands. Tree. Visual search: what's next? Cees Snoek University of Amsterdam The Netherlands Euvision Technologies The Netherlands Problem statement US flag Tree Aircraft Humans Dog Smoking Building Basketball Table

More information

Social Media Mining. Data Mining Essentials

Social Media Mining. Data Mining Essentials Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

More information

International Journal of Advanced Information in Arts, Science & Management Vol.2, No.2, December 2014

International Journal of Advanced Information in Arts, Science & Management Vol.2, No.2, December 2014 Efficient Attendance Management System Using Face Detection and Recognition Arun.A.V, Bhatath.S, Chethan.N, Manmohan.C.M, Hamsaveni M Department of Computer Science and Engineering, Vidya Vardhaka College

More information

AN ENHANCED MECHANISM FOR SECURE DATA TRANSMISSION USING STEGANOGRAPHY MERGED WITH VISUAL CRYPTOGRAPHY

AN ENHANCED MECHANISM FOR SECURE DATA TRANSMISSION USING STEGANOGRAPHY MERGED WITH VISUAL CRYPTOGRAPHY AN ENHANCED MECHANISM FOR SECURE DATA TRANSMISSION USING STEGANOGRAPHY MERGED WITH VISUAL CRYPTOGRAPHY Chandan Mohapatra 1, Manajusha Pandey 2 1 PG Student in Computer Science Dept., KIIT University, Bhubaneswar,

More information

Speed Performance Improvement of Vehicle Blob Tracking System

Speed Performance Improvement of Vehicle Blob Tracking System Speed Performance Improvement of Vehicle Blob Tracking System Sung Chun Lee and Ram Nevatia University of Southern California, Los Angeles, CA 90089, USA sungchun@usc.edu, nevatia@usc.edu Abstract. A speed

More information

So today we shall continue our discussion on the search engines and web crawlers. (Refer Slide Time: 01:02)

So today we shall continue our discussion on the search engines and web crawlers. (Refer Slide Time: 01:02) Internet Technology Prof. Indranil Sengupta Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Lecture No #39 Search Engines and Web Crawler :: Part 2 So today we

More information

Recognizing Cats and Dogs with Shape and Appearance based Models. Group Member: Chu Wang, Landu Jiang

Recognizing Cats and Dogs with Shape and Appearance based Models. Group Member: Chu Wang, Landu Jiang Recognizing Cats and Dogs with Shape and Appearance based Models Group Member: Chu Wang, Landu Jiang Abstract Recognizing cats and dogs from images is a challenging competition raised by Kaggle platform

More information

Euler Vector: A Combinatorial Signature for Gray-Tone Images

Euler Vector: A Combinatorial Signature for Gray-Tone Images Euler Vector: A Combinatorial Signature for Gray-Tone Images Arijit Bishnu, Bhargab B. Bhattacharya y, Malay K. Kundu, C. A. Murthy fbishnu t, bhargab, malay, murthyg@isical.ac.in Indian Statistical Institute,

More information

Assessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall

Assessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall Automatic Photo Quality Assessment Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall Estimating i the photorealism of images: Distinguishing i i paintings from photographs h Florin

More information

K-means Clustering Technique on Search Engine Dataset using Data Mining Tool

K-means Clustering Technique on Search Engine Dataset using Data Mining Tool International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 6 (2013), pp. 505-510 International Research Publications House http://www. irphouse.com /ijict.htm K-means

More information

CATEGORIZATION OF SIMILAR OBJECTS USING BAG OF VISUAL WORDS AND k NEAREST NEIGHBOUR CLASSIFIER

CATEGORIZATION OF SIMILAR OBJECTS USING BAG OF VISUAL WORDS AND k NEAREST NEIGHBOUR CLASSIFIER TECHNICAL SCIENCES Abbrev.: Techn. Sc., No 15(2), Y 2012 CATEGORIZATION OF SIMILAR OBJECTS USING BAG OF VISUAL WORDS AND k NEAREST NEIGHBOUR CLASSIFIER Piotr Artiemjew, Przemysław Górecki, Krzysztof Sopyła

More information

Oracle8i Spatial: Experiences with Extensible Databases

Oracle8i Spatial: Experiences with Extensible Databases Oracle8i Spatial: Experiences with Extensible Databases Siva Ravada and Jayant Sharma Spatial Products Division Oracle Corporation One Oracle Drive Nashua NH-03062 {sravada,jsharma}@us.oracle.com 1 Introduction

More information

How To Make Sense Of Data With Altilia

How To Make Sense Of Data With Altilia HOW TO MAKE SENSE OF BIG DATA TO BETTER DRIVE BUSINESS PROCESSES, IMPROVE DECISION-MAKING, AND SUCCESSFULLY COMPETE IN TODAY S MARKETS. ALTILIA turns Big Data into Smart Data and enables businesses to

More information

MIFT: A Mirror Reflection Invariant Feature Descriptor

MIFT: A Mirror Reflection Invariant Feature Descriptor MIFT: A Mirror Reflection Invariant Feature Descriptor Xiaojie Guo, Xiaochun Cao, Jiawan Zhang, and Xuewei Li School of Computer Science and Technology Tianjin University, China {xguo,xcao,jwzhang,lixuewei}@tju.edu.cn

More information

Image Classification for Dogs and Cats

Image Classification for Dogs and Cats Image Classification for Dogs and Cats Bang Liu, Yan Liu Department of Electrical and Computer Engineering {bang3,yan10}@ualberta.ca Kai Zhou Department of Computing Science kzhou3@ualberta.ca Abstract

More information

Cloud-Empowered Multimedia Service: An Automatic Video Storytelling Tool

Cloud-Empowered Multimedia Service: An Automatic Video Storytelling Tool Cloud-Empowered Multimedia Service: An Automatic Video Storytelling Tool Joseph C. Tsai Foundation of Computer Science Lab. The University of Aizu Fukushima-ken, Japan jctsai@u-aizu.ac.jp Abstract Video

More information

Probabilistic Latent Semantic Analysis (plsa)

Probabilistic Latent Semantic Analysis (plsa) Probabilistic Latent Semantic Analysis (plsa) SS 2008 Bayesian Networks Multimedia Computing, Universität Augsburg Rainer.Lienhart@informatik.uni-augsburg.de www.multimedia-computing.{de,org} References

More information

LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE. indhubatchvsa@gmail.com

LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE. indhubatchvsa@gmail.com LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE 1 S.Manikandan, 2 S.Abirami, 2 R.Indumathi, 2 R.Nandhini, 2 T.Nanthini 1 Assistant Professor, VSA group of institution, Salem. 2 BE(ECE), VSA

More information

Email Spam Detection Using Customized SimHash Function

Email Spam Detection Using Customized SimHash Function International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Volume 1, Issue 8, December 2014, PP 35-40 ISSN 2349-4840 (Print) & ISSN 2349-4859 (Online) www.arcjournals.org Email

More information

Real Time Target Tracking with Pan Tilt Zoom Camera

Real Time Target Tracking with Pan Tilt Zoom Camera 2009 Digital Image Computing: Techniques and Applications Real Time Target Tracking with Pan Tilt Zoom Camera Pankaj Kumar, Anthony Dick School of Computer Science The University of Adelaide Adelaide,

More information

Mean-Shift Tracking with Random Sampling

Mean-Shift Tracking with Random Sampling 1 Mean-Shift Tracking with Random Sampling Alex Po Leung, Shaogang Gong Department of Computer Science Queen Mary, University of London, London, E1 4NS Abstract In this work, boosting the efficiency of

More information

Clustering Big Data. Anil K. Jain. (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012

Clustering Big Data. Anil K. Jain. (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012 Clustering Big Data Anil K. Jain (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012 Outline Big Data How to extract information? Data clustering

More information

Practical Tour of Visual tracking. David Fleet and Allan Jepson January, 2006

Practical Tour of Visual tracking. David Fleet and Allan Jepson January, 2006 Practical Tour of Visual tracking David Fleet and Allan Jepson January, 2006 Designing a Visual Tracker: What is the state? pose and motion (position, velocity, acceleration, ) shape (size, deformation,

More information

CSISE: CLOUD-BASED SEMANTIC IMAGE SEARCH ENGINE. A THESIS IN Computer Science

CSISE: CLOUD-BASED SEMANTIC IMAGE SEARCH ENGINE. A THESIS IN Computer Science CSISE: CLOUD-BASED SEMANTIC IMAGE SEARCH ENGINE A THESIS IN Computer Science Presented to the Faculty of the University Of Missouri Kansas City In partial fulfillment Of the requirements for the degree

More information

A Method of Caption Detection in News Video

A Method of Caption Detection in News Video 3rd International Conference on Multimedia Technology(ICMT 3) A Method of Caption Detection in News Video He HUANG, Ping SHI Abstract. News video is one of the most important media for people to get information.

More information

Character Image Patterns as Big Data

Character Image Patterns as Big Data 22 International Conference on Frontiers in Handwriting Recognition Character Image Patterns as Big Data Seiichi Uchida, Ryosuke Ishida, Akira Yoshida, Wenjie Cai, Yaokai Feng Kyushu University, Fukuoka,

More information

Smartness of Image Classification in Mobile Network Marketing

Smartness of Image Classification in Mobile Network Marketing 2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) A HYBRID APPROACH TO OFFLOADING MOBILE IMAGE CLASSIFICATION J. Hauswald, T. Manville, Q. Zheng, R. Dreslinski, C. Chakrabarti

More information

Automatic 3D Reconstruction via Object Detection and 3D Transformable Model Matching CS 269 Class Project Report

Automatic 3D Reconstruction via Object Detection and 3D Transformable Model Matching CS 269 Class Project Report Automatic 3D Reconstruction via Object Detection and 3D Transformable Model Matching CS 69 Class Project Report Junhua Mao and Lunbo Xu University of California, Los Angeles mjhustc@ucla.edu and lunbo

More information

Image Search by MapReduce

Image Search by MapReduce Image Search by MapReduce COEN 241 Cloud Computing Term Project Final Report Team #5 Submitted by: Lu Yu Zhe Xu Chengcheng Huang Submitted to: Prof. Ming Hwa Wang 09/01/2015 Preface Currently, there s

More information

Index Terms Domain name, Firewall, Packet, Phishing, URL.

Index Terms Domain name, Firewall, Packet, Phishing, URL. BDD for Implementation of Packet Filter Firewall and Detecting Phishing Websites Naresh Shende Vidyalankar Institute of Technology Prof. S. K. Shinde Lokmanya Tilak College of Engineering Abstract Packet

More information

The Delicate Art of Flower Classification

The Delicate Art of Flower Classification The Delicate Art of Flower Classification Paul Vicol Simon Fraser University University Burnaby, BC pvicol@sfu.ca Note: The following is my contribution to a group project for a graduate machine learning

More information

Keyframe-Based Real-Time Camera Tracking

Keyframe-Based Real-Time Camera Tracking Keyframe-Based Real-Time Camera Tracking Zilong Dong 1 Guofeng Zhang 1 Jiaya Jia 2 Hujun Bao 1 1 State Key Lab of CAD&CG, Zhejiang University 2 The Chinese University of Hong Kong {zldong, zhangguofeng,

More information

Machine Learning using MapReduce

Machine Learning using MapReduce Machine Learning using MapReduce What is Machine Learning Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous

More information

Data Mining in Web Search Engine Optimization and User Assisted Rank Results

Data Mining in Web Search Engine Optimization and User Assisted Rank Results Data Mining in Web Search Engine Optimization and User Assisted Rank Results Minky Jindal Institute of Technology and Management Gurgaon 122017, Haryana, India Nisha kharb Institute of Technology and Management

More information

PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS.

PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS. PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS Project Project Title Area of Abstract No Specialization 1. Software

More information

Dynamically Partitioning Applications between Weak Devices and Clouds

Dynamically Partitioning Applications between Weak Devices and Clouds Dynamically Partitioning Applications between Weak Devices and Clouds Mobile Cloud Computing and Services Workshop 2010 Byung-Gon Chun, Petros Maniatis Intel Labs Berkeley Weak devices Weak devices» Smartphones»

More information

Object Recognition. Selim Aksoy. Bilkent University saksoy@cs.bilkent.edu.tr

Object Recognition. Selim Aksoy. Bilkent University saksoy@cs.bilkent.edu.tr Image Classification and Object Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Image classification Image (scene) classification is a fundamental

More information

FACE RECOGNITION FOR SOCIAL MEDIA WITH MOBILE CLOUD COMPUTING

FACE RECOGNITION FOR SOCIAL MEDIA WITH MOBILE CLOUD COMPUTING FACE RECOGNITION FOR SOCIAL MEDIA WITH MOBILE CLOUD COMPUTING Prasetyawidi Indrawan 1, Slamet Budiyatno 2, Nur Muhammad Ridho 3, and Riri Fitri Sari 4 1,2,3,4 Department of Electrical Engineering, University

More information

The Visual Internet of Things System Based on Depth Camera

The Visual Internet of Things System Based on Depth Camera The Visual Internet of Things System Based on Depth Camera Xucong Zhang 1, Xiaoyun Wang and Yingmin Jia Abstract The Visual Internet of Things is an important part of information technology. It is proposed

More information

False alarm in outdoor environments

False alarm in outdoor environments Accepted 1.0 Savantic letter 1(6) False alarm in outdoor environments Accepted 1.0 Savantic letter 2(6) Table of contents Revision history 3 References 3 1 Introduction 4 2 Pre-processing 4 3 Detection,

More information

Automatic Grocery Shopping Assistant

Automatic Grocery Shopping Assistant Automatic Grocery Shopping Assistant Linda He Yi Department of Electrical Engineering Stanford University Stanford, CA heyi@stanford.edu Feiqiao Brian Yu Department of Electrical Engineering Stanford University

More information

Online Learning of Patch Perspective Rectification for Efficient Object Detection

Online Learning of Patch Perspective Rectification for Efficient Object Detection Online Learning of Patch Perspective Rectification for Efficient Object Detection Stefan Hinterstoisser 1, Selim Benhimane 1, Nassir Navab 1, Pascal Fua 2, Vincent Lepetit 2 1 Department of Computer Science,

More information

Bags of Binary Words for Fast Place Recognition in Image Sequences

Bags of Binary Words for Fast Place Recognition in Image Sequences IEEE TRANSACTIONS ON ROBOTICS, VOL., NO., MONTH, YEAR. SHORT PAPER 1 Bags of Binary Words for Fast Place Recognition in Image Sequences Dorian Gálvez-López and Juan D. Tardós, Member, IEEE Abstract We

More information

DYNAMIC RANGE IMPROVEMENT THROUGH MULTIPLE EXPOSURES. Mark A. Robertson, Sean Borman, and Robert L. Stevenson

DYNAMIC RANGE IMPROVEMENT THROUGH MULTIPLE EXPOSURES. Mark A. Robertson, Sean Borman, and Robert L. Stevenson c 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or

More information

Face Model Fitting on Low Resolution Images

Face Model Fitting on Low Resolution Images Face Model Fitting on Low Resolution Images Xiaoming Liu Peter H. Tu Frederick W. Wheeler Visualization and Computer Vision Lab General Electric Global Research Center Niskayuna, NY, 1239, USA {liux,tu,wheeler}@research.ge.com

More information

SiteCelerate white paper

SiteCelerate white paper SiteCelerate white paper Arahe Solutions SITECELERATE OVERVIEW As enterprises increases their investment in Web applications, Portal and websites and as usage of these applications increase, performance

More information

PHOTO BASED INTERACTION AND QUERY FOR END-USER APPLICATIONS - THE GEOPILOT PROJECT

PHOTO BASED INTERACTION AND QUERY FOR END-USER APPLICATIONS - THE GEOPILOT PROJECT PHOTO BASED INTERACTION AND QUERY FOR END-USER APPLICATIONS - THE GEOPILOT PROJECT Volker Paelke IKG, Institute for Cartography and Geoinformatics Leibniz Universität Hannover Appelstr. 9a, D-30167 Hannover,

More information

Adobe Marketing Cloud Sharpening images in Scene7 Publishing System and on Image Server

Adobe Marketing Cloud Sharpening images in Scene7 Publishing System and on Image Server Adobe Marketing Cloud Sharpening images in Scene7 Publishing System and on Image Server Contents Contact and Legal Information...3 About image sharpening...4 Adding an image preset to save frequently used

More information

Similarity Search in a Very Large Scale Using Hadoop and HBase

Similarity Search in a Very Large Scale Using Hadoop and HBase Similarity Search in a Very Large Scale Using Hadoop and HBase Stanislav Barton, Vlastislav Dohnal, Philippe Rigaux LAMSADE - Universite Paris Dauphine, France Internet Memory Foundation, Paris, France

More information

Simultaneous Gamma Correction and Registration in the Frequency Domain

Simultaneous Gamma Correction and Registration in the Frequency Domain Simultaneous Gamma Correction and Registration in the Frequency Domain Alexander Wong a28wong@uwaterloo.ca William Bishop wdbishop@uwaterloo.ca Department of Electrical and Computer Engineering University

More information

Behavior Analysis in Crowded Environments. XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011

Behavior Analysis in Crowded Environments. XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011 Behavior Analysis in Crowded Environments XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011 Behavior Analysis in Sparse Scenes Zelnik-Manor & Irani CVPR

More information

Intelligent Database Monitoring System using ARM9 with QR Code

Intelligent Database Monitoring System using ARM9 with QR Code Intelligent Database Monitoring System using ARM9 with QR Code Jyoshi Niklesh 1, Dhruva R. Rinku 2 Department of Electronics and Communication CVR College of Engineering, JNTU Hyderabad Hyderabad, India

More information

EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set

EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set Amhmed A. Bhih School of Electrical and Electronic Engineering Princy Johnson School of Electrical and Electronic Engineering Martin

More information

A Learning Based Method for Super-Resolution of Low Resolution Images

A Learning Based Method for Super-Resolution of Low Resolution Images A Learning Based Method for Super-Resolution of Low Resolution Images Emre Ugur June 1, 2004 emre.ugur@ceng.metu.edu.tr Abstract The main objective of this project is the study of a learning based method

More information

Laser Gesture Recognition for Human Machine Interaction

Laser Gesture Recognition for Human Machine Interaction International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-04, Issue-04 E-ISSN: 2347-2693 Laser Gesture Recognition for Human Machine Interaction Umang Keniya 1*, Sarthak

More information

The Big Data methodology in computer vision systems

The Big Data methodology in computer vision systems The Big Data methodology in computer vision systems Popov S.B. Samara State Aerospace University, Image Processing Systems Institute, Russian Academy of Sciences Abstract. I consider the advantages of

More information

Segmentation of building models from dense 3D point-clouds

Segmentation of building models from dense 3D point-clouds Segmentation of building models from dense 3D point-clouds Joachim Bauer, Konrad Karner, Konrad Schindler, Andreas Klaus, Christopher Zach VRVis Research Center for Virtual Reality and Visualization, Institute

More information

Interactive person re-identification in TV series

Interactive person re-identification in TV series Interactive person re-identification in TV series Mika Fischer Hazım Kemal Ekenel Rainer Stiefelhagen CV:HCI lab, Karlsruhe Institute of Technology Adenauerring 2, 76131 Karlsruhe, Germany E-mail: {mika.fischer,ekenel,rainer.stiefelhagen}@kit.edu

More information

Mobile Storage and Search Engine of Information Oriented to Food Cloud

Mobile Storage and Search Engine of Information Oriented to Food Cloud Advance Journal of Food Science and Technology 5(10): 1331-1336, 2013 ISSN: 2042-4868; e-issn: 2042-4876 Maxwell Scientific Organization, 2013 Submitted: May 29, 2013 Accepted: July 04, 2013 Published:

More information

Multimedia Document Authentication using On-line Signatures as Watermarks

Multimedia Document Authentication using On-line Signatures as Watermarks Multimedia Document Authentication using On-line Signatures as Watermarks Anoop M Namboodiri and Anil K Jain Department of Computer Science and Engineering Michigan State University East Lansing, MI 48824

More information

Machine Learning with MATLAB David Willingham Application Engineer

Machine Learning with MATLAB David Willingham Application Engineer Machine Learning with MATLAB David Willingham Application Engineer 2014 The MathWorks, Inc. 1 Goals Overview of machine learning Machine learning models & techniques available in MATLAB Streamlining the

More information

Optimization of Search Results with Duplicate Page Elimination using Usage Data A. K. Sharma 1, Neelam Duhan 2 1, 2

Optimization of Search Results with Duplicate Page Elimination using Usage Data A. K. Sharma 1, Neelam Duhan 2 1, 2 Optimization of Search Results with Duplicate Page Elimination using Usage Data A. K. Sharma 1, Neelam Duhan 2 1, 2 Department of Computer Engineering, YMCA University of Science & Technology, Faridabad,

More information

Data Mining With Big Data Image De-Duplication In Social Networking Websites

Data Mining With Big Data Image De-Duplication In Social Networking Websites Data Mining With Big Data Image De-Duplication In Social Networking Websites Hashmi S.Taslim First Year ME Jalgaon faruki11@yahoo.co.in ABSTRACT Big data is the term for a collection of data sets which

More information

Similarity Search in a Very Large Scale Using Hadoop and HBase

Similarity Search in a Very Large Scale Using Hadoop and HBase http:/cedric.cnam.fr Similarity Search in a Very Large Scale Using Hadoop and HBase Rapport de recherche CEDRIC 2012 http://cedric.cnam.fr Stanislav Barton (CNAM), Vlastislav Dohnal (Masaryk University),

More information

MetropoGIS: A City Modeling System DI Dr. Konrad KARNER, DI Andreas KLAUS, DI Joachim BAUER, DI Christopher ZACH

MetropoGIS: A City Modeling System DI Dr. Konrad KARNER, DI Andreas KLAUS, DI Joachim BAUER, DI Christopher ZACH MetropoGIS: A City Modeling System DI Dr. Konrad KARNER, DI Andreas KLAUS, DI Joachim BAUER, DI Christopher ZACH VRVis Research Center for Virtual Reality and Visualization, Virtual Habitat, Inffeldgasse

More information

Outdoors Augmented Reality on Mobile Phone using Loxel-Based Visual Feature Organization

Outdoors Augmented Reality on Mobile Phone using Loxel-Based Visual Feature Organization Outdoors Augmented Reality on Mobile Phone using Loxel-Based Visual Feature Organization Gabriel Takacs Stanford University gtakacs@stanford.edu Yingen Xiong yingen.xiong@nokia.com Radek Grzeszczuk radek.grzeszczuk@nokia.com

More information

Tracking and Recognition in Sports Videos

Tracking and Recognition in Sports Videos Tracking and Recognition in Sports Videos Mustafa Teke a, Masoud Sattari b a Graduate School of Informatics, Middle East Technical University, Ankara, Turkey mustafa.teke@gmail.com b Department of Computer

More information

Search Result Optimization using Annotators

Search Result Optimization using Annotators Search Result Optimization using Annotators Vishal A. Kamble 1, Amit B. Chougule 2 1 Department of Computer Science and Engineering, D Y Patil College of engineering, Kolhapur, Maharashtra, India 2 Professor,

More information

WATERMARKING FOR IMAGE AUTHENTICATION

WATERMARKING FOR IMAGE AUTHENTICATION WATERMARKING FOR IMAGE AUTHENTICATION Min Wu Bede Liu Department of Electrical Engineering Princeton University, Princeton, NJ 08544, USA Fax: +1-609-258-3745 {minwu, liu}@ee.princeton.edu ABSTRACT A data

More information

Journal of Industrial Engineering Research. Adaptive sequence of Key Pose Detection for Human Action Recognition

Journal of Industrial Engineering Research. Adaptive sequence of Key Pose Detection for Human Action Recognition IWNEST PUBLISHER Journal of Industrial Engineering Research (ISSN: 2077-4559) Journal home page: http://www.iwnest.com/aace/ Adaptive sequence of Key Pose Detection for Human Action Recognition 1 T. Sindhu

More information

Make and Model Recognition of Cars

Make and Model Recognition of Cars Make and Model Recognition of Cars Sparta Cheung Department of Electrical and Computer Engineering University of California, San Diego 9500 Gilman Dr., La Jolla, CA 92093 sparta@ucsd.edu Alice Chu Department

More information

Distributed forests for MapReduce-based machine learning

Distributed forests for MapReduce-based machine learning Distributed forests for MapReduce-based machine learning Ryoji Wakayama, Ryuei Murata, Akisato Kimura, Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi Chubu University, Japan. NTT Communication

More information