Scale-space image processing
|
|
|
- Lawrence Hunt
- 7 years ago
- Views:
Transcription
1 Scale-space image processing Corresponding image features can appear at different scales Like shift-invariance, scale-invariance of image processing algorithms is often desirable. Scale-space representation is useful to process an image in a manner that is both shift-invariant and scale-invariant Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 1
2 Scale-space image processing Scale-space theory Laplacian of Gaussian (LoG) and Difference of Gaussian (DoG) Scale-space edge detection Scale-space keypoint detection Harris-Laplacian SIFT detector SURF detector Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 2
3 Scale-space representation of a signal Parametric family of signals f t (x) where fine-scale information is successively attenuated scale t Successive smoothing with a Gaussian filter Zero-crossings of 2 nd derivative Fewer edges at coarser scales ( ) f t x Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 3
4 Scale-space representation of images Parametric family of images smoothed by Gaussian filter t Coarser scales Shift-invariance Original image f (x,y) Rotation-invariance Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 4
5 Scale-space representation of images (cont.) Commutative semigroup property Separability Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 5
6 Scale-space representation of images (cont.) Non-creation of local extrema (for f (x,y) and all of its partial derivatives) since and unimodal. Solution to diffusion equation (heat equation) Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 6
7 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 7
8 Laplacian of Gaussian LoG vs. DoG Difference of Gaussians t = σ 2 = 1 t = σ 2 = 1, k = f t ( x, y)= t f t x, y ( ) Y -4-4 X Y X Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 8
9 LoG vs. DoG (cont.) Laplacian of Gaussian Difference of Gaussians t = σ 2 = 1 t = σ 2 = 1, k = H 0.4 H ω y ω x ω y ω x Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 9
10 Scale space: Laplacian images t = 1 t = 4 t = 16 t = 64 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 10
11 Scale space: Binarized Laplacian images t = 1 t = 4 t = 16 t = 64 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 11
12 Scale space: edge detection Zero crossings of Laplacian images t = 1 t = 4 t = 16 t = 64 Low-gradient-magnitude edges removed Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 12
13 Laplacian zero-crossings Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 13
14 Suppose we filter with the following 3x3 Laplacian kernel. How many arithmetic operations per pixel are required????( ) Alternatively, suppose we have and f [x, y] and we compute the difference f ( 2t) [x, y] f t [x, y] to approximate the LoG by a DoG. How much do we save relative to the arithmetic operations required for the LoG computation? 2t Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 14
15 Keypoint detection with automatic scale selection Scale-space representation provides all scales; which scale is best for keypoint detection? Harris-Laplacian 1. Detect Harris corners at some initial scale 2. For each Harris corner detect characteristic scale t h = argmax t t 2 f t ( x h, y ) h 3. Apply Harris detector in a spatial neighborhood at scale t h to refine keypoint location 4. Repeat 2. and 3. until convergence scale t y Harris Harris Harris x Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 15
16 Keypoint detection with automatic scale selection Harris-Laplacian example (150 strongest peaks) Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 16
17 Keypoint detection with automatic scale selection Harris-Laplacian example (200 strongest peaks) Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 17
18 Scale (next octave) t = 16 t = 8 2 t = 8 t = 4 2 t = 4 SIFT keypoint detection SIFT - Scale-Invariant Feature Transform Decompose image into DoG scale-space representation Detect minima and maxima locally and across scales Fit 3-d quadratic function to localize extrema with subpixel/sub-scale accuracy [Brown, Lowe, 2002] Eliminate edge responses based on Hessian t = 4 t = 2 2 Scale (first octave) t = 2 t = 2 Scale t = 1 Gaussian Difference of Gaussian (DoG) Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 18 [Lowe, 1999, 2004]
19 SIFT scale space pyramid: octave Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 19
20 SIFT scale space pyramid: octave Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 20
21 SIFT scale space pyramid: octave Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 21
22 SIFT scale space pyramid: octave Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 22
23 SIFT scale space pyramid: octave Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 23
24 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 24 SIFT keypoints
25 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 25 SIFT keypoints
26 Robustness against scaling Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 26 [Mikolajczyk, Schmid, 2001]
27 Which of the following statements are true? (a) Local extrema in LoG scale space can occur along edges. (a) Multiple extrema in LoG scale space can occur at the same [x,y] location, but at different scales.??? (b) The SIFT keypoint detector only detects dark blobs in an image. The detector must be applied to a negative of the image to detect bright blobs. (d) The SIFT keypoint detector finds more keypoints at small scales than at large scales in a typical image. Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 27
28 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 28 Hessian keypoints in scale space
![SURF keypoint detection SURF Speeded Up Robust Features [Bay, Tuytelaars, Van Gool, ECCV 2006] No subsampling all resolution levels at full spatial resolution Simple approximation of scale space](/docs-images/57/41054601/images/29-0.png "Gaussian derivatives using integral images Determinant of Hessian D t xy Non-maximum suppression in 3x3x3 [x,y,t] neighborhood Interpolation of maximum of det(h) in image space x,y and scale t")
29 SURF keypoint detection SURF Speeded Up Robust Features [Bay, Tuytelaars, Van Gool, ECCV 2006] No subsampling all resolution levels at full spatial resolution Simple approximation of scale space Gaussian derivatives using integral images Determinant of Hessian D t xy Non-maximum suppression in 3x3x3 [x,y,t] neighborhood Interpolation of maximum of det(h) in image space x,y and scale t Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 29
30 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 30 SURF keypoints
31 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 31 SIFT keypoints
32 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 32 SURF keypoints
33 Digital Image Processing: Bernd Girod, 2013 Stanford University -- Scale Space 33 SIFT keypoints
Convolution. 1D Formula: 2D Formula: Example on the web: http://www.jhu.edu/~signals/convolve/
Basic Filters (7) Convolution/correlation/Linear filtering Gaussian filters Smoothing and noise reduction First derivatives of Gaussian Second derivative of Gaussian: Laplacian Oriented Gaussian filters
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
Image Segmentation and Registration
Image Segmentation and Registration Dr. Christine Tanner (tanner@vision.ee.ethz.ch) Computer Vision Laboratory, ETH Zürich Dr. Verena Kaynig, Machine Learning Laboratory, ETH Zürich Outline Segmentation
Distinctive Image Features from Scale-Invariant Keypoints
Distinctive Image Features from Scale-Invariant Keypoints David G. Lowe Computer Science Department University of British Columbia Vancouver, B.C., Canada lowe@cs.ubc.ca January 5, 2004 Abstract This paper
Feature Tracking and Optical Flow
02/09/12 Feature Tracking and Optical Flow Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem Many slides adapted from Lana Lazebnik, Silvio Saverse, who in turn adapted slides from Steve
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
SURF: Speeded Up Robust Features
SURF: Speeded Up Robust Features Herbert Bay 1, Tinne Tuytelaars 2, and Luc Van Gool 12 1 ETH Zurich {bay, vangool}@vision.ee.ethz.ch 2 Katholieke Universiteit Leuven {Tinne.Tuytelaars, Luc.Vangool}@esat.kuleuven.be
Edge detection. (Trucco, Chapt 4 AND Jain et al., Chapt 5) -Edges are significant local changes of intensity in an image.
Edge detection (Trucco, Chapt 4 AND Jain et al., Chapt 5) Definition of edges -Edges are significant local changes of intensity in an image. -Edges typically occur on the boundary between two different
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
Stitching of X-ray Images
IT 12 057 Examensarbete 30 hp November 2012 Stitching of X-ray Images Krishna Paudel Institutionen för informationsteknologi Department of Information Technology Abstract Stitching of X-ray Images Krishna
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,
jorge s. marques image processing
image processing images images: what are they? what is shown in this image? What is this? what is an image images describe the evolution of physical variables (intensity, color, reflectance, condutivity)
VEHICLE TRACKING USING FEATURE MATCHING AND KALMAN FILTERING
VEHICLE TRACKING USING FEATURE MATCHING AND KALMAN FILTERING Kiran Mantripragada IBM Research Brazil and University of Sao Paulo Polytechnic School, Department of Mechanical Engineering Sao Paulo, Brazil
Sharpening through spatial filtering
Sharpening through spatial filtering Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Elaborazione delle immagini (Image processing I) academic year 2011 2012 Sharpening The term
AN EFFICIENT HYBRID REAL TIME FACE RECOGNITION ALGORITHM IN JAVA ENVIRONMENT ABSTRACT
AN EFFICIENT HYBRID REAL TIME FACE RECOGNITION ALGORITHM IN JAVA ENVIRONMENT M. A. Abdou*, M. H. Fathy** *Informatics Research Institute, City for Scientific Research and Technology Applications (SRTA-City),
A Comparative Study between SIFT- Particle and SURF-Particle Video Tracking Algorithms
A Comparative Study between SIFT- Particle and SURF-Particle Video Tracking Algorithms H. Kandil and A. Atwan Information Technology Department, Faculty of Computer and Information Sciences, Mansoura University,El-Gomhoria
Image Gradients. Given a discrete image Á Òµ, consider the smoothed continuous image ܵ defined by
Image Gradients Given a discrete image Á Òµ, consider the smoothed continuous image ܵ defined by ܵ Ü ¾ Ö µ Á Òµ Ü ¾ Ö µá µ (1) where Ü ¾ Ö Ô µ Ü ¾ Ý ¾. ½ ¾ ¾ Ö ¾ Ü ¾ ¾ Ö. Here Ü is the 2-norm for the
A Comparative Study of SIFT and its Variants
10.2478/msr-2013-0021 MEASUREMENT SCIENCE REVIEW, Volume 13, No. 3, 2013 A Comparative Study of SIFT and its Variants Jian Wu 1, Zhiming Cui 1, Victor S. Sheng 2, Pengpeng Zhao 1, Dongliang Su 1, Shengrong
Feature Point Selection using Structural Graph Matching for MLS based Image Registration
Feature Point Selection using Structural Graph Matching for MLS based Image Registration Hema P Menon Department of CSE Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu - 641 112, India K A Narayanankutty
Computational Foundations of Cognitive Science
Computational Foundations of Cognitive Science Lecture 15: Convolutions and Kernels Frank Keller School of Informatics University of Edinburgh keller@inf.ed.ac.uk February 23, 2010 Frank Keller Computational
FREAK: Fast Retina Keypoint
FREAK: Fast Retina Keypoint Alexandre Alahi, Raphael Ortiz, Pierre Vandergheynst Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland Abstract A large number of vision applications rely on matching
An Iterative Image Registration Technique with an Application to Stereo Vision
An Iterative Image Registration Technique with an Application to Stereo Vision Bruce D. Lucas Takeo Kanade Computer Science Department Carnegie-Mellon University Pittsburgh, Pennsylvania 15213 Abstract
A Comparison of Keypoint Descriptors in the Context of Pedestrian Detection: FREAK vs. SURF vs. BRISK
A Comparison of Keypoint Descriptors in the Context of Pedestrian Detection: FREAK vs. SURF vs. BRISK Cameron Schaeffer Stanford University CS Department camerons@stanford.edu Abstract The subject of keypoint
Bildverarbeitung und Mustererkennung Image Processing and Pattern Recognition
Bildverarbeitung und Mustererkennung Image Processing and Pattern Recognition 1. Image Pre-Processing - Pixel Brightness Transformation - Geometric Transformation - Image Denoising 1 1. Image Pre-Processing
J. P. Oakley and R. T. Shann. Department of Electrical Engineering University of Manchester Manchester M13 9PL U.K. Abstract
A CURVATURE SENSITIVE FILTER AND ITS APPLICATION IN MICROFOSSIL IMAGE CHARACTERISATION J. P. Oakley and R. T. Shann Department of Electrical Engineering University of Manchester Manchester M13 9PL U.K.
Canny Edge Detection
Canny Edge Detection 09gr820 March 23, 2009 1 Introduction The purpose of edge detection in general is to significantly reduce the amount of data in an image, while preserving the structural properties
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.
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
High Quality Image Magnification using Cross-Scale Self-Similarity
High Quality Image Magnification using Cross-Scale Self-Similarity André Gooßen 1, Arne Ehlers 1, Thomas Pralow 2, Rolf-Rainer Grigat 1 1 Vision Systems, Hamburg University of Technology, D-21079 Hamburg
Czech Technical University in Prague Faculty of Electrical Engineering MASTER S THESIS. Karel Lenc
Czech Technical University in Prague Faculty of Electrical Engineering MASTER S THESIS Karel Lenc Evaluation and Improvements of Image Interest Regions Detectors and Descriptors Thesis supervisor: Prof.
Automatic 3D Mapping for Infrared Image Analysis
Automatic 3D Mapping for Infrared Image Analysis i r f m c a d a r a c h e V. Martin, V. Gervaise, V. Moncada, M.H. Aumeunier, M. irdaouss, J.M. Travere (CEA) S. Devaux (IPP), G. Arnoux (CCE) and JET-EDA
Evaluation of Interest Point Detectors
International Journal of Computer Vision 37(2), 151 172, 2000 c 2000 Kluwer Academic Publishers. Manufactured in The Netherlands. Evaluation of Interest Point Detectors CORDELIA SCHMID, ROGER MOHR AND
A Performance Evaluation of Volumetric 3D Interest Point Detectors
DOI 10.1007/s11263-012-0563-2 A Performance Evaluation of Volumetric 3D Interest Point Detectors Tsz-Ho Yu Oliver J. Woodford Roberto Cipolla Received: 4 November 2011 / Accepted: 17 August 2012 Springer
Efficient Belief Propagation for Early Vision
Efficient Belief Propagation for Early Vision Pedro F. Felzenszwalb and Daniel P. Huttenlocher Department of Computer Science, Cornell University {pff,dph}@cs.cornell.edu Abstract Markov random field models
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:
SECOND DERIVATIVE TEST FOR CONSTRAINED EXTREMA
SECOND DERIVATIVE TEST FOR CONSTRAINED EXTREMA This handout presents the second derivative test for a local extrema of a Lagrange multiplier problem. The Section 1 presents a geometric motivation for the
Feature Detection with Automatic Scale Selection
Feature Detection with Automatic Scale Selection Tony Lindeberg Computational Vision and Active Perception Laboratory (CVAP) Department of Numerical Analysis and Computing Science KTH (Royal Institute
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
Automatic georeferencing of imagery from high-resolution, low-altitude, low-cost aerial platforms
Automatic georeferencing of imagery from high-resolution, low-altitude, low-cost aerial platforms Amanda Geniviva, Jason Faulring and Carl Salvaggio Rochester Institute of Technology, 54 Lomb Memorial
Scale-Invariant Object Categorization using a Scale-Adaptive Mean-Shift Search
in DAGM 04 Pattern Recognition Symposium, Tübingen, Germany, Aug 2004. Scale-Invariant Object Categorization using a Scale-Adaptive Mean-Shift Search Bastian Leibe ½ and Bernt Schiele ½¾ ½ Perceptual Computing
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,
Admin stuff. 4 Image Pyramids. Spatial Domain. Projects. Fourier domain 2/26/2008. Fourier as a change of basis
Admin stuff 4 Image Pyramids Change of office hours on Wed 4 th April Mon 3 st March 9.3.3pm (right after class) Change of time/date t of last class Currently Mon 5 th May What about Thursday 8 th May?
Surgical Tools Recognition and Pupil Segmentation for Cataract Surgical Process Modeling
Surgical Tools Recognition and Pupil Segmentation for Cataract Surgical Process Modeling David Bouget, Florent Lalys, Pierre Jannin To cite this version: David Bouget, Florent Lalys, Pierre Jannin. Surgical
Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections
Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections Maximilian Hung, Bohyun B. Kim, Xiling Zhang August 17, 2013 Abstract While current systems already provide
Nonlinear Edge Preserving Smoothing and Segmentation of 4-D Medical Images Via Scale-Space Fingerprint Analysis
IPMI 2001: 17th International Conference, pages 431 437 (preprint) Nonlinear Edge Preserving Smoothing and Segmentation of 4-D Medical Images Via Scale-Space Fingerprint Analysis Bryan W. Reutter 1,2,
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
MVA ENS Cachan. Lecture 2: Logistic regression & intro to MIL Iasonas Kokkinos Iasonas.kokkinos@ecp.fr
Machine Learning for Computer Vision 1 MVA ENS Cachan Lecture 2: Logistic regression & intro to MIL Iasonas Kokkinos Iasonas.kokkinos@ecp.fr Department of Applied Mathematics Ecole Centrale Paris Galen
EECS 556 Image Processing W 09. Interpolation. Interpolation techniques B splines
EECS 556 Image Processing W 09 Interpolation Interpolation techniques B splines What is image processing? Image processing is the application of 2D signal processing methods to images Image representation
ASSET IDENTIFICATION USING IMAGE DESCRIPTORS. Reena Ursula Friedel. A Thesis Submitted to the Faculty of
ASSET IDENTIFICATION USING IMAGE DESCRIPTORS by Reena Ursula Friedel A Thesis Submitted to the Faculty of The College of Engineering and Computer Science in Partial Fulfillment of the Requirements for
FPGA-BASED PARALLEL HARDWARE ARCHITECTURE FOR REAL-TIME OBJECT CLASSIFICATION. by Murad Mohammad Qasaimeh
FPGA-BASED PARALLEL HARDWARE ARCHITECTURE FOR REAL-TIME OBJECT CLASSIFICATION by Murad Mohammad Qasaimeh A Thesis Presented to the Faculty of the American University of Sharjah College of Engineering in
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector Geert Willems 1, Tinne Tuytelaars 1, and Luc Van Gool 1,2 1 ESAT-PSI, K.U. Leuven, Belgium, {gwillems,tuytelaa,vangool}@esat.kuleuven.be
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/
Object tracking & Motion detection in video sequences
Introduction Object tracking & Motion detection in video sequences Recomended link: http://cmp.felk.cvut.cz/~hlavac/teachpresen/17compvision3d/41imagemotion.pdf 1 2 DYNAMIC SCENE ANALYSIS The input to
A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA
A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA N. Zarrinpanjeh a, F. Dadrassjavan b, H. Fattahi c * a Islamic Azad University of Qazvin - nzarrin@qiau.ac.ir
Retina Mosaicing Using Local Features
Retina Mosaicing Using Local Features hilippe C. Cattin, Herbert Bay, Luc Van Gool, and Gábor Székely ETH Zurich - Computer Vision Laboratory Sternwartstrasse 7, CH - 8092 Zurich {cattin,bay,vangool,szekely}@vision.ee.ethz.ch
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,
Digital Imaging and Multimedia. Filters. Ahmed Elgammal Dept. of Computer Science Rutgers University
Digital Imaging and Multimedia Filters Ahmed Elgammal Dept. of Computer Science Rutgers University Outlines What are Filters Linear Filters Convolution operation Properties of Linear Filters Application
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
Digital image processing
746A27 Remote Sensing and GIS Lecture 4 Digital image processing Chandan Roy Guest Lecturer Department of Computer and Information Science Linköping University Digital Image Processing Most of the common
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
Section 3.2 Polynomial Functions and Their Graphs
Section 3.2 Polynomial Functions and Their Graphs EXAMPLES: P(x) = 3, Q(x) = 4x 7, R(x) = x 2 +x, S(x) = 2x 3 6x 2 10 QUESTION: Which of the following are polynomial functions? (a) f(x) = x 3 +2x+4 (b)
Cloud based object recognition: A system proposal
Cloud based object recognition: A system proposal Daniel LORENČÍK 1, Peter SINČÁK 2 Abstract In this chapter, we will present a proposal for the cloud based object recognition system. The system will extract
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
Microeconomic Theory: Basic Math Concepts
Microeconomic Theory: Basic Math Concepts Matt Van Essen University of Alabama Van Essen (U of A) Basic Math Concepts 1 / 66 Basic Math Concepts In this lecture we will review some basic mathematical concepts
521466S Machine Vision Assignment #7 Hough transform
521466S Machine Vision Assignment #7 Hough transform Spring 2014 In this assignment we use the hough transform to extract lines from images. We use the standard (r, θ) parametrization of lines, lter the
Lecture 5 Principal Minors and the Hessian
Lecture 5 Principal Minors and the Hessian Eivind Eriksen BI Norwegian School of Management Department of Economics October 01, 2010 Eivind Eriksen (BI Dept of Economics) Lecture 5 Principal Minors and
Part-Based Recognition
Part-Based Recognition Benedict Brown CS597D, Fall 2003 Princeton University CS 597D, Part-Based Recognition p. 1/32 Introduction Many objects are made up of parts It s presumably easier to identify simple
Point Matching as a Classification Problem for Fast and Robust Object Pose Estimation
Point Matching as a Classification Problem for Fast and Robust Object Pose Estimation Vincent Lepetit Julien Pilet Pascal Fua Computer Vision Laboratory Swiss Federal Institute of Technology (EPFL) 1015
Tracking of Small Unmanned Aerial Vehicles
Tracking of Small Unmanned Aerial Vehicles Steven Krukowski Adrien Perkins Aeronautics and Astronautics Stanford University Stanford, CA 94305 Email: spk170@stanford.edu Aeronautics and Astronautics Stanford
2.2 Creaseness operator
2.2. Creaseness operator 31 2.2 Creaseness operator Antonio López, a member of our group, has studied for his PhD dissertation the differential operators described in this section [72]. He has compared
Robert Collins CSE598G. More on Mean-shift. R.Collins, CSE, PSU CSE598G Spring 2006
More on Mean-shift R.Collins, CSE, PSU Spring 2006 Recall: Kernel Density Estimation Given a set of data samples x i ; i=1...n Convolve with a kernel function H to generate a smooth function f(x) Equivalent
Numerical Methods For Image Restoration
Numerical Methods For Image Restoration CIRAM Alessandro Lanza University of Bologna, Italy Faculty of Engineering CIRAM Outline 1. Image Restoration as an inverse problem 2. Image degradation models:
Virtual Mouse Using a Webcam
1. INTRODUCTION Virtual Mouse Using a Webcam Since the computer technology continues to grow up, the importance of human computer interaction is enormously increasing. Nowadays most of the mobile devices
3. Reaction Diffusion Equations Consider the following ODE model for population growth
3. Reaction Diffusion Equations Consider the following ODE model for population growth u t a u t u t, u 0 u 0 where u t denotes the population size at time t, and a u plays the role of the population dependent
Image Stitching based on Feature Extraction Techniques: A Survey
Image Stitching based on Feature Extraction Techniques: A Survey Ebtsam Adel Information System Dept. Faculty of Computers and Information, Mansoura University, Egypt Mohammed Elmogy Information Technology
Jiří Matas. Hough Transform
Hough Transform Jiří Matas Center for Machine Perception Department of Cybernetics, Faculty of Electrical Engineering Czech Technical University, Prague Many slides thanks to Kristen Grauman and Bastian
Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture.
Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture. Chirag Gupta,Sumod Mohan K cgupta@clemson.edu, sumodm@clemson.edu Abstract In this project we propose a method to improve
REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING
REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING Ms.PALLAVI CHOUDEKAR Ajay Kumar Garg Engineering College, Department of electrical and electronics Ms.SAYANTI BANERJEE Ajay Kumar Garg Engineering
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
Automatic Traffic Estimation Using Image Processing
Automatic Traffic Estimation Using Image Processing Pejman Niksaz Science &Research Branch, Azad University of Yazd, Iran Pezhman_1366@yahoo.com Abstract As we know the population of city and number of
Interpolation of RGB components in Bayer CFA images
Interpolation of RGB components in Bayer CFA images Demosaicing of Bayer-sampled color images Problem: Most digital color cameras, capture only one color component at each spatial location. The remaining
Lecture 3. Linear Programming. 3B1B Optimization Michaelmas 2015 A. Zisserman. Extreme solutions. Simplex method. Interior point method
Lecture 3 3B1B Optimization Michaelmas 2015 A. Zisserman Linear Programming Extreme solutions Simplex method Interior point method Integer programming and relaxation The Optimization Tree Linear Programming
Vision based Vehicle Tracking using a high angle camera
Vision based Vehicle Tracking using a high angle camera Raúl Ignacio Ramos García Dule Shu gramos@clemson.edu dshu@clemson.edu Abstract A vehicle tracking and grouping algorithm is presented in this work
Lectures 6&7: Image Enhancement
Lectures 6&7: Image Enhancement Leena Ikonen Pattern Recognition (MVPR) Lappeenranta University of Technology (LUT) leena.ikonen@lut.fi http://www.it.lut.fi/ip/research/mvpr/ 1 Content Background Spatial
Models of Cortical Maps II
CN510: Principles and Methods of Cognitive and Neural Modeling Models of Cortical Maps II Lecture 19 Instructor: Anatoli Gorchetchnikov dy dt The Network of Grossberg (1976) Ay B y f (
An Intelligent Wellness Keeper for Food Nutrition with Graphical Icons
An Intelligent Wellness Keeper for Food Nutrition with Graphical Icons Taeyoung Choi 1 and Seongah Chin 2 Division of Multimedia, College of Engineering Sungkyul University, Anyang, South Korea 1 tychic@naver.com,
Combining an Alternating Sequential Filter (ASF) and Curvelet for Denoising Coronal MRI Images
Contemporary Engineering Sciences, Vol. 5, 2012, no. 2, 85-90 Combining an Alternating Sequential Filter (ASF) and Curvelet for Denoising Coronal MRI Images Mohamed Ali HAMDI Ecole Nationale d Ingénieur
Correlation and Convolution Class Notes for CMSC 426, Fall 2005 David Jacobs
Correlation and Convolution Class otes for CMSC 46, Fall 5 David Jacobs Introduction Correlation and Convolution are basic operations that we will perform to extract information from images. They are in
3D OBJECT MODELING AND RECOGNITION IN PHOTOGRAPHS AND VIDEO
3D OBJECT MODELING AND RECOGNITION IN PHOTOGRAPHS AND VIDEO Fredrick H. Rothganger, Ph.D. Computer Science University of Illinois at Urbana-Champaign, 2004 Jean Ponce, Adviser This thesis introduces a
Polynomials. Dr. philippe B. laval Kennesaw State University. April 3, 2005
Polynomials Dr. philippe B. laval Kennesaw State University April 3, 2005 Abstract Handout on polynomials. The following topics are covered: Polynomial Functions End behavior Extrema Polynomial Division
Object class recognition using unsupervised scale-invariant learning
Object class recognition using unsupervised scale-invariant learning Rob Fergus Pietro Perona Andrew Zisserman Oxford University California Institute of Technology Goal Recognition of object categories
Classification of Fingerprints. Sarat C. Dass Department of Statistics & Probability
Classification of Fingerprints Sarat C. Dass Department of Statistics & Probability Fingerprint Classification Fingerprint classification is a coarse level partitioning of a fingerprint database into smaller
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
Interactive Robust Multitudinous Visual Search on Mobile Devices
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. 12, December 2014,
Two-Frame Motion Estimation Based on Polynomial Expansion
Two-Frame Motion Estimation Based on Polynomial Expansion Gunnar Farnebäck Computer Vision Laboratory, Linköping University, SE-581 83 Linköping, Sweden gf@isy.liu.se http://www.isy.liu.se/cvl/ Abstract.
Jitter Measurements in Serial Data Signals
Jitter Measurements in Serial Data Signals Michael Schnecker, Product Manager LeCroy Corporation Introduction The increasing speed of serial data transmission systems places greater importance on measuring
Computer Vision & Digital Image Processing. Edge linking and boundary detection
Computer Vision & Digital Image Processing Edge Linking and Boundary Detection Dr. D. J. Jackson Lecture 17-1 Edge linking and boundary detection Ideally, edge detection techniques yield pixels lying only
Math 241, Exam 1 Information.
Math 241, Exam 1 Information. 9/24/12, LC 310, 11:15-12:05. Exam 1 will be based on: Sections 12.1-12.5, 14.1-14.3. The corresponding assigned homework problems (see http://www.math.sc.edu/ boylan/sccourses/241fa12/241.html)
EFFICIENT VEHICLE TRACKING AND CLASSIFICATION FOR AN AUTOMATED TRAFFIC SURVEILLANCE SYSTEM
EFFICIENT VEHICLE TRACKING AND CLASSIFICATION FOR AN AUTOMATED TRAFFIC SURVEILLANCE SYSTEM Amol Ambardekar, Mircea Nicolescu, and George Bebis Department of Computer Science and Engineering University
MiSeq: Imaging and Base Calling
MiSeq: Imaging and Page Welcome Navigation Presenter Introduction MiSeq Sequencing Workflow Narration Welcome to MiSeq: Imaging and. This course takes 35 minutes to complete. Click Next to continue. Please