Mesh segmentation and 3D object recognition. Florent Lafarge Inria Sophia Antipolis  Mediterranee


 Teresa Fletcher
 1 years ago
 Views:
Transcription
1 Mesh segmentation and 3D object recognition Florent Lafarge Inria Sophia Antipolis  Mediterranee
2 What is mesh segmentation?
3 What is mesh segmentation? M = {V,E,F} is a mesh S is either V, E or F (usually F) A Segmentation is the set of submeshes induced by a partition of S into k disjoint subsets segmentation can also be called partitioning or clustering
4 Formulating a mesh segmentation problem Usually specifies by two key elements: a function measuring the quality of a partition, eventually under a set of constraints a mechanism for finding an optimal partition.
5 Contents Attributes and constraints Segmentation algorithms
6 How to measure the quality of a segmentation?
7 What are we looking for? Planar clusters?
8 What are we looking for? Planar clusters? Smooth clusters?
9 What are we looking for? Planar clusters? Smooth clusters? Round clusters?
10 What are we looking for? Planar clusters? Smooth clusters? Round clusters? Small/large clusters?
11 What are we looking for? Planar clusters? Smooth clusters? Round clusters? Small/large clusters? Small number of clusters?
12 What are we looking for? Planar clusters? Smooth clusters? Round clusters? Small/large clusters? Small number of clusters? Smooth boundary?
13 What are we looking for? Planar clusters? Smooth clusters? Round clusters? Small/large clusters? Small number of clusters? Smooth boundary? structural clusters?
14 Attributes and constraints Attributes: criteria used to measure the quality of a partition with respect to the input mesh attributes are usually embedded into some metrics
15 Attributes and constraints Attributes: criteria used to measure the quality of a partition with respect to the input mesh attributes are usually embedded into some metrics Constraints: cardinality, geometry or topology of the clusters must be preserved (hard) or favored (soft)
16 Attributes and constraints example of problems without constraints with hard constraints with soft constraints min P f(p,attributes) min P f(p,attributes) under g(p)=0 min P f(p,attributes)α.g(p)
17 Attributes Distance and Geodesic distance
18 Attributes Distance and Geodesic distance Planarity, normal direction
19 Attributes Distance and Geodesic distance Planarity, normal direction Smoothness, curvature
20 Attributes Distance and Geodesic distance Planarity, normal direction Smoothness, curvature Distance to complex geometric primitives
21 Attributes Distance and Geodesic distance Planarity, normal direction Smoothness, curvature Distance to complex geometric primitives Symmetry
22 Attributes Distance and Geodesic distance Planarity, normal direction Smoothness, curvature Distance to complex geometric primitives Symmetry Medial Axis, Shape diameter
23 Attributes Distance and Geodesic distance Planarity, normal direction Smoothness, curvature Distance to complex geometric primitives Symmetry Medial Axis, Shape diameter Texture
24 Constraints Cardinality Not too small/large or a given number of clusters Overall balanced partition,.. Geometry Size: area, diameter, radius Convexity, Roundness Boundary smoothness,.. Connectivity Boundary shape, Semantic constraints,..
25 Contents Attributes and constraints Segmentation algorithms
26 Segmentation algorithms Very large variety of «mechanisms» in the literature Common with image processing/computer vision Focus on some of them: Thresholding Region growing Hierarchical partitioning Markov Random Fields
27 Thresholding simple one parameter for a binary segmentation h(i) i
28 Hysteresis thresholding idea: to keep the sites connected (favor large clusters) two parameters for a binary segmentation t l < t h scheme threshold the sites with t l consider as cluster only the set of connected sites all > t l with at least one site > t h
29 Region growing see primitivebased surface reconstruction slides Mechanism while the mesh is not entirely segmented choose an unlabeled site (a facet) assign label k to its similar adjacent sites iterate on the new assigned sites.. when propagation of cluster k finished, update k=k+1
30 hierarchical partitioning start from one region representing the entire mesh divide into several regions when the criteria are not valid, and iterate on the new regions
31 hierarchical partitioning
32 hierarchical partitioning
33 hierarchical partitioning
34 hierarchical partitioning
35 Markov Random Fields (MRF) set of random variables having a Markov property described by an undirected graph
36 Markov property in 1D (Markov chain) X 0 X 1 X n1 X n n usually corresponds to time
37 Markov property in 2D or on a manifold in 3D (Markov field) P[ X k X {Xk} ]= P[ X k (X n(k) )] with n(k) neighbors of k X n X k1 X k3 X m Xk2 X k X k4 X j
38 Markov property in 2D or on a manifold in 3D (Markov field) P[ X k X {Xk} ]= P[ X k (X n(k) )] with n(k) neighbors of k X n X k1 X k3 X m X k2 X k X k4 X j
39 Markov property notion of neighborhood a MRF is always associated to a neighborhood system defining the dependency between graph nodes
40 Markov property Gibbs energy (HammersleyClifford theorem)
41 Markov property Why is the markovian property important? graph with 1M nodes if each node is adjacent to every other nodes: 1M*(999,999)/2 edges ~ 500 G edges each random variable cannot be dependent to all the other ones complexity needs to be reduced by spatial considerations
42 Markov Random Fields (MRF) set of random variables having a Markov property described by an undirected graph for an image, two common graphs nodes = pixel centers edges = adjacent pixels (4connexity) nodes = pixel corners edges = pixel borders =
43 Markov Random Fields (MRF) set of random variables having a Markov property described by an undirected graph for a mesh: Graph nodes = vertices & graph edges = edges Graph nodes = facets & graph edges = edges Graph nodes = edges & graph edges = vertices
44 Bayesian formulation Let y, the data (attributes) x, the label we want to model the probability of having x knowing y Bayes law
45 Standard assumption: the conditional independence of the observation: (X Y) S I S I X X Pr Pr p p p D
46 From probability to energy data term : local dependency hypothesis regularization : soft constraints
47 Optimal configuration We search for the label configuration x that maximizes P(X=x /Y=y) x = arg max Pr(X=x Y=y) x = arg min U(x) x
48 exercise: binary segmentation or? Graph structure Graph nodes = facets Graph edges = common edges Attributes on facet: [0,1] (y) labels: {white, black} (l) Energy: with yi if li ' white' Di ( li) 1 yi otherwise V i, j ( l, l i j 0 ) 1 if l l otherwise i j
49 jji exercise: binary segmentation or? Graph structure Graph nodes = facets Graph edges = common edges Attributes on facet: [0,1] (y) labels: {white, black} (l) Energy: with yi if li ' white' Di ( li) 1 yi otherwise V i, j ( l, l i j 0 ) 1 if l l otherwise i j Q1: what is the optimal configuration l if β =0? What is its energy? Q2: what is the optimal configuration if β inf? Q3: what are the other possible optimal configurations in function of β?
50 exercise: binary segmentation or?
51 Finding the optimal configuration of labels Graphcut based approches fast but limitations on energy formulation Monte Carlo sampling slow but no limitation
52 Example: mesh segmentation with principal curvature attributes & soft geometric constraints Multilabel energy model of the form with V, set of vertices of the input mesh E, set of edges in the mesh l i, the label of the vertex i among : planar (1), developable convex (2), developable concave (3) and non developable (4)
53 Data term with
54 Data term with
55 Soft constraints Label smoothness Edge preservation with
56 Optimization
57 Some results
58 What is object recognition?
59 What is object recognition? chairs tables
60 What is object recognition? Input: CAD databases (eg 3D warehouse) or point clouds (eg kinect, laser) Assumption: objects have been segmented. We want to find the nature of each object
61 Contents Feature extraction (local and/or global) Classification
62 Local features (one data point = one feature) Distance and Geodesic distance Planarity, normal direction Smoothness, curvature Distance to complex geometric primitives Symmetry Medial Axis, Shape diameter Texture
63 Global features (one object = one feature) distribution measures of local features (eg histograms)
64 Keypoint features (one object= several keypoints) Local features detected at some specific locations of the object
65 Contents Feature extraction (local and/or global) Classification
66 Machine learning techniques (overview)
67 Machine learning techniques (overview)
68 Example: object recognition from point cloud databases via planar abstraction Provides robustness against defective data Rely on robust shape detection methods Invariance to rotation and scale Up vector not required Meaningful global descriptor Function constrains object shape
69 Example: object recognition from point cloud databases via planar abstraction Object descriptor Feature vector Geometric features Histograms Training and classification via Random Forest
70 Example: object recognition from point cloud databases via planar abstraction Features Area fragmentation Many small shapes Few large shapes 1 Area 1 Area 0,8 0,8 0,6 0,6 0,4 0,4 0,2 0, Shape size Shape size
71 Example: object recognition from point cloud databases via planar abstraction Features Pairwise orientation of shapes Relative orientation Histogram 090 degrees Pairwise angles of adjacent shapes 1 0,8 0,6 0,4 0,2 0 Area Angle 1 0,8 0,6 0,4 0,2 0 Area Angle
72 Example: object recognition from point cloud databases via planar abstraction Features Histogram of slope Angle between shape and reference vector Major axis of oriented bounding box 1 Area 1 Area 0,5 0, Angle Angle
73 Example: object recognition from point cloud databases via planar abstraction Multiscale Different kind of detail for objects Non planar shapes depend on detection parameters Histogram features performed on 3 scales 2x and 4x of basis scale
74 Example: object recognition from point cloud databases via planar abstraction Confution matrix Increasing defects Planar abstraction Pointbased (Osada) PCL ESFfeature
Computer Vision  part II
Computer Vision  part II Review of main parts of Section B of the course School of Computer Science & Statistics Trinity College Dublin Dublin 2 Ireland www.scss.tcd.ie Lecture Name Course Name 1 1 2
More informationBildverarbeitung und Mustererkennung Image Processing and Pattern Recognition
Bildverarbeitung und Mustererkennung Image Processing and Pattern Recognition 710.080 2VO 710.081 1KU 1 Bildverarbeitung und Mustererkennung Image Processing and Pattern Recognition MRF and Variational
More informationSegmentation and labeling of objects. Digital Image Processing Lecture 7. Labeling, 2 different algorithms. Methods for segmentation
Digital mage Processing Lecture 7 p. 1 Segmentation and labeling of objects p. Segmentation and labeling Region growing Region splitting and merging Labeling Watersheds MSER (extra, optional) More morphological
More informationGraph Theory and Metric
Chapter 2 Graph Theory and Metric Dimension 2.1 Science and Engineering Multi discipline teams and multi discipline areas are words that now a days seem to be important in the scientific research. Indeed
More informationRepresentation and Description
Representation and Description Representation and Description After Segmentation We need to represent (or describe) the segmented region Representation is used for further processing Often desire a compact
More informationClustering with Application to Fast Object Search
03/08/12 Clustering with Application to Fast Object Search Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem This section Clustering: grouping together similar points, images, feature
More informationEdge Contour Representation
Edge Contour Representation (Jain et al., Chapt 6, Gonzalez et al., Chapt 8) (1) The simplest representation of a contour is using an ordered list of its edge points. *Asaccurate as the location estimates
More information(Robust?) 3D symmetry extraction with applications to robot navigation
(Robust?) 3D symmetry extraction with applications to robot navigation Allen Y. Yang, Shankar Rao, Jie Lai and Professor Yi Ma Perception and Decision Lab, part of Coordinated Science Lab, UIUC Dec. 12,
More informationA Contour Based Descriptor for Object Recognition
A Contour Based Descriptor for Object Recognition Cezar Popescu Politehnica University of Timişoara, Faculty of Automation and Computers, Department of Automation and Industrial Informatics, Bd.V. Pârvan
More informationEUROPEAN PATENT OFFICE U.S. PATENT AND TRADEMARK OFFICE CPC NOTICE OF CHANGES 354 DATE: JANUARY 1, 2017 PROJECT RP0409. Action Subclass Group(s)
EUROPEAN PATENT OFFICE U.S. PATENT AND TRADEMARK OFFICE CPC NOTICE OF CHANGES 354 The following classification changes will be effected by this Notice of Changes: Action Subclass Group(s) Symbols deleted:
More informationAdvanced Introduction to Machine Learning, CMU10715
Advanced Introduction to Machine Learning, CMU10715 Manifold Learning Barnabás Póczos Motivation Find meaningful lowdimensional structures hidden in highdimensional observations. The human brain confronts
More informationMorphological Image Processing
Morphological Image Processing Morphology Identification, analysis, and description of the structure of the smallest unit of words Theory and technique for the analysis and processing of geometric structures
More informationChapter 9: Image Visualization
Projects for Chapter 9: Image Visualization 1 PROJECT 1 Implement the mean shift segmentation algorithm of Comaniciu and Meer described in Chapter 9, Section 9.4.2. For this, proceed as follows: 1. As
More informationSegmentation. Image Processing  Lesson 12
Image Processing  Lesson 12 Segmentation Threshold Segmentation Local thresholding Edge thresholding Threshold using averaging Gradient Detectors Region Growing Split& Merge Shape Matching Shape Representation
More informationSemisupervised learning
Semisupervised learning Learning from both labeled and unlabeled data Semisupervised learning Learning from both labeled and unlabeled data Motivation: labeled data may be hard/expensive to get, but
More informationSTRAWBERRY REPORT KMeans Clustering.
STRAWBERRY REPORT 1. Abstract This research applies shape analysis to images of strawberries for the purpose of acquiring information about the target strawberry s spatial orientation. The research goal
More information~150k triangles ~80k triangles
Mesh Simplification Applications Oversampled 3D scan data ~150k triangles ~80k triangles 2 Applications Overtessellation: E.g. isosurface surface extraction 3 Applications Multiresolution hierarchies
More informationObject Detection  Basics 1
Object Detection  Basics 1 Lecture 28 See Sections 10.1.1, 10.1.2, and 10.1.3 in Reinhard Klette: Concise Computer Vision SpringerVerlag, London, 2014 1 See last slide for copyright information. 1 /
More informationExact Cell Decomposition Methods
Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 1 ] Exact Cell Decomposition Methods Acknowledgement: arts of these course notes are based on notes from
More informationImage Representation and Description
Digital Image Processing, 2nd ed. Digital Image Processing Chapter 11 Image Representation and Description Dr. Kai Shuang Department of Electronic Engineering China University of Petroleum shuangkai@cup.edu.cn
More information1. INTRODUCTION. Thinning plays a vital role in image processing and computer vision. It
1 1. INTRODUCTION 1.1 Introduction Thinning plays a vital role in image processing and computer vision. It is an important preprocessing step in many applications such as document analysis, image compression,
More informationGeometric Modeling in Graphics
Geometric Modeling in Graphics Part 5: Mesh repairing Martin Samuelčík www.sccg.sk/~samuelcik samuelcik@sccg.sk Mesh repairing Joining identical vertices Removing degenerated (empty) polygons and edges
More informationObject Detection and Recognition. Face Recognition. Face Detection. Object Detection
Object Detection and Recognition Object detection and recognition are two important computer vision tasks. Face Recognition Slide 1 Object detection determines the presence of an object and/or its scope,
More informationOn the Automated Drawing of Graphs
Proceedings of the Third Caribbean Conference on Combinatorics and Computing The University of the West Indies, Barbados, pp 43 55, 1981 Abstract On the Automated Drawing of Graphs Margaret A. Bernard
More informationGeometrical Segmentation of Point Cloud Data using Spectral Clustering
Geometrical Segmentation of Point Cloud Data using Spectral Clustering Sergey Alexandrov and Rainer Herpers University of Applied Sciences BonnRheinSieg 15th July 2014 1/29 Addressed Problem Given: a
More informationA Learning Based Method for SuperResolution of Low Resolution Images
A Learning Based Method for SuperResolution 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 informationImage Representation and Description
Lecture 4.4.7 Image Representation and Description Shahram Ebadollahi 4/5/8 DIP ELEN E483 Image Description Recognition Highlevel Image Representation Image Understanding 4/5/8 Lecture Outline Image Description
More informationMAVIparticle Modular Algorithms for 3D Particle Characterization
MAVIparticle Modular Algorithms for 3D Particle Characterization version 1.0 Image Processing Department Fraunhofer ITWM Contents Contents 1 Introduction 2 2 The program 2 2.1 Framework..............................
More informationFast Matching Of Binary Features By Marius Muja and David G. Lowe
Fast Matching Of Binary Features By Marius Muja and David G. Lowe Presented By: Patricia Oeking & David Zhang Abstract Binary features: fast to compute, compact to store and efficient to compare with each
More informationImage Processing  Lesson 13. Binary Images. Threshold Binary Image  Definition Connected Components Euler Number Chain Code Edge Following
Image Processing  Lesson 13 Binary Images Threshold Binary Image  Definition Connected Components Euler Number Chain Code Edge Following Output of the segmentation process How many objects Size of objects
More informationImage segmentation beyond thresholding (or how to understand Andy Warhols inspiration)
Image segmentation beyond thresholding (or how to understand Andy Warhols inspiration) An introduction Lars Aurdal, Norsk Regnesentral, November 13th 2006 Plan 1. Region and edge based approaches. 2. Region
More informationChapter 6. Chapter 6. Chapter 6. 3D modeling 6.1 Introduction 6.2 Planar surface modeling 6.3 Solid modeling. Computer Graphics
Chapter 6 Chapter 6 Chapter 6. 3D modeling 6.1 Introduction 6.2 Planar surface modeling 6.3 Solid modeling Computer Graphics Chapter 6. 3D modeling 1 6.1 Introduction An scene can contain different type
More informationV. Adamchik 1. Graph Theory. Victor Adamchik. Fall of There are different ways to draw the same graph. Consiser the following two graphs.
V. Adamchik 1 Graph Theory Victor Adamchik Fall of 2005 Plan 1. Graph Isomorphism 2. Graph Enumeration 3. Planar Graphs Graphs Isomorphism There are different ways to draw the same graph. Consiser the
More informationPeople Detection with DSIFT Algorithm By Bing Han, Dingyi Li and Jia Ji
1 Introduction People Detection with Algorithm By Bing Han, Dingyi Li and Jia Ji People detection is an interesting computer vision topic. Locating people in images and videos have many potential applications,
More informationImage Segmentation using kmeans clustering, EM and Normalized Cuts
Image Segmentation using kmeans clustering, EM and Normalized Cuts Suman Tatiraju Department of EECS University Of California  Irvine Irvine, CA 92612 statiraj@uci.edu Avi Mehta Department of EECS University
More informationObject Detection and Recognition
Object Detection and Recognition Object detection and recognition are two important computer vision tasks. Object detection determines the presence of an object and/or its scope, and locations in the image.
More informationA 3D Object Retrieval System Based on MultiResolution Reeb Graph
A 3D Object Retrieval System Based on MultiResolution Reeb Graph DingYun Chen and Ming Ouhyoung Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
More informationSIFT and Object Recognition Dan O Shea Prof. Fei Fei Li, COS 598B
SIFT and Object Recognition Dan O Shea Prof. Fei Fei Li, COS 598B Distinctive image features from scaleinvariant keypoints David Lowe. International Journal of Computer Vision, 2004. Towards a Computational
More informationGRAPH THEORY STUDY GUIDE
GRAPH THEORY STUDY GUIDE 1. Definitions Definition 1 (Partition of A). A set A = A 1,..., A k of disjoint subsets of a set A is a partition of A if A of all the sets A i A and A i for every i. Definition
More informationAn Edgebased Method for Registering a Graph onto an Image with Application to Cadastre Registration
An Edgebased Method for Registering a Graph onto an Image with Application to Cadastre Registration Roger TriasSanz March 2004 Rapport technique 2004/1 du laboratoire SIP, CRIP5 Université de Paris 5
More informationBinary Image Analysis
Binary Image Analysis Segmentation produces homogenous regions each region has uniform graylevel each region is a binary image (0: background, 1: object or the reverse) more intensity values for overlapping
More informationObject Detection Using Hausdorff Distance and Multiclass Discriminative Field
Object Detection Using Hausdorff Distance and Multiclass Discriminative Field Xiaofeng Zhang 1, Hong Ding 1 and Rengui Cheng 2 1 School of Computer Science and Technology, Nantong University, Nantong,
More informationMedial Surface Extraction for 3D Shape Representation
Medial Surface Extraction for 3D Shape Representation di Rossi Luca Università Ca Foscari di Venezia A.A. 2009/2010 Skeleton: Definition In the seminal paper of Blum the skeleton is defined as the locus
More informationMarkov chains and Markov Random Fields (MRFs)
Markov chains and Markov Random Fields (MRFs) 1 Why Markov Models We discuss Markov models now. This is the simplest statistical model in which we don t assume that all variables are independent; we assume
More informationLearning Image Patch Similarity
Chapter 6 Learning Image Patch Similarity The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization.
More informationRandomized Motion Planning Nancy Amato Fall 04, Univ. of Padova Configuration Space [ 1 ] Configuration Space
Randomized Motion Planning Nancy mato Fall 04, Univ. of Padova Configuration Space [ 1 ] Configuration Space cknowledgement: Parts of these course notes are based on notes from courses given by JeanClaude
More informationSIFT: SCALE INVARIANT FEATURE TRANSFORM BY DAVID LOWE. Presented by: Jason Clemons
SIFT: SCALE INVARIANT FEATURE TRANSFORM BY DAVID LOWE Presented by: Jason Clemons Overview Motivation of Work Overview of Algorithm Scale Space and Difference of Gaussian Keypoint Localization Orientation
More informationVehicle Detection Using Dynamic Bayesian Networks
Vehicle Detection Using Dynamic Bayesian Networks Sourabh Dekate BhushanRade Dipak Mahajan Sangita Chaudhari A B S T R A C T We present an automatic vehicle detection system for aerial or ground surveillance.
More informationGeometricGuided Label Propagation for Moving Object Detection
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com GeometricGuided Label Propagation for Moving Object Detection Kao, J.Y.; Tian, D.; Mansour, H.; Ortega, A.; Vetro, A. TR2016005 March 2016
More informationSCAN: A Structural Clustering Algorithm for Networks
SCAN: A Structural Clustering Algorithm for Networks Xiaowei Xu, Nurcan Yuruk, Zhidan Feng (University of Arkansas at Little Rock) Thomas A. J. Schweiger (Acxiom Corporation) Networks scaling: #edges connected
More informationShape Decomposition Scheme by Combining Mathematical Morphology and Convex Partitioning
ACCV2002: The 5th Asian Conference on Computer Vision, 23 25 January 2002, Melbourne, Australia 1 Shape Decomposition Scheme by Combining Mathematical Morphology and Convex Partitioning Duck Hoon Kim School
More informationExact Inference: Variable Elimination 2. Complexity of Variable Elimination
Exact Inference: Variable Elimination 2 1 of Variable Elimination 2 1 Exponential size of the factors dominates the complexity If each variable has no more than v values And a factor i has a scope that
More informationImage 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
More informationMOBILE TECHNIQUES LOCALIZATION. Shyam Sunder Kumar & Sairam Sundaresan
MOBILE TECHNIQUES LOCALIZATION Shyam Sunder Kumar & Sairam Sundaresan LOCALISATION Process of identifying location and pose of client based on sensor inputs Common approach is GPS and orientation sensors
More informationFigure 2.1: An example of a convex set and a nonconvex one.
Convex Hulls 2.1 Definitions 2 Convexity is the key to understanding and simplifying geometry, and the convex hull plays a role in geometry akin to the sorted order for a collection of numbers. So what
More informationChapter 6: Random rounding of semidefinite programs
Chapter 6: Random rounding of semidefinite programs Section 6.1: Semidefinite Programming Section 6.: Finding large cuts Extra (not in book): The max sat problem. Section 6.5: Coloring 3colorabale graphs.
More informationMarked point processes for object extraction in high resolution images: Application to Earth observation and cartography
Marked point processes for object extraction in high resolution images: Application to Earth observation and cartography J. Zerubia Joint work with X. Descombes, C. Lacoste, M. Ortner, G. Perrin, R. Stoica,
More informationOptical Character Segmentation and Recognition from a Rochester Flag. Thomas Kollar and Jonathan Schmid 05/07/02
Optical Character Segmentation and Recognition from a Rochester Flag Thomas Kollar and Jonathan Schmid 05/07/02 Abstract This project investigated segmentation and recognition of characters from a Rochester
More information5.1 Bipartite Matching
CS787: Advanced Algorithms Lecture 5: Applications of Network Flow In the last lecture, we looked at the problem of finding the maximum flow in a graph, and how it can be efficiently solved using the FordFulkerson
More informationInternational Journal of Advancements in Research & Technology, Volume 2, Issue4, April ISSN
International Journal of Advancements in Research & Technology, Volume 2, Issue4, April2013 167 A NEW APPORACH TO OPTICAL CHARACTER RECOGNITION BASED ON TEXT RECOGNITION IN OCR Dr. Rajnesh Kumar (rajnesh.gcnagina@gmail.com)
More informationSimple 2D and 3D Shape Descriptors
Simple 2D and 3D Shape Descriptors Tutorial by Jorge Márquez Flores CCADETUNAM 202 Bounding Box (also: bounding rectangle) and Natural Bounding Box of a 2D shape S (also Region Of Interest ROI). The first
More informationSurface Reconstruction
WDS'09 Proceedings of Contributed Papers, Part I, 204 209, 2009. ISBN 9788073781019 MATFYZPRESS Surface Reconstruction P. Surynková Charles University in Prague, Faculty of Mathematics and Physics,
More informationBayesian Machine Learning (ML): Modeling And Inference in Big Data. Zhuhua Cai Google, Rice University caizhua@gmail.com
Bayesian Machine Learning (ML): Modeling And Inference in Big Data Zhuhua Cai Google Rice University caizhua@gmail.com 1 Syllabus Bayesian ML Concepts (Today) Bayesian ML on MapReduce (Next morning) Bayesian
More informationOverlay Text Detection in Complex Video Background
Overlay Text Detection in Complex Video Background ChunCheng Lin 1 1 Department of Electrical Engineering National ChinYi University of Technology Taichung, Taiwan cclin@ncut.edu.tw BoMin Yen 2 2 Department
More information3D OBJECT RECOGNITION AND POSE ESTIMATION USING AN RGBD CAMERA. Daniel Bray
3D OBJECT RECOGNITION AND POSE ESTIMATION USING AN RGBD CAMERA by Daniel Bray Presented to the Graduate Faculty of The University of Texas at San Antonio in Partial Fulfillment of the Requirements for
More informationImage Segmentation: Definitions. Image Segmentation. Image Segmentation: Definitions. Image Segmentation: Definitions
Image Segmentation How do we know which groups of pixels in a digital image correspond to the objects to be analyzed? Objects may be uniformly darker or brighter than the background against which they
More informationIndex. C Capacity, 314 Chroma Subsampling, 44 Color, 67
Index 3D Animation Watermarking, 363, 364 3D Data Acquisition, 9 3D Graphics, 9 3D Mesh Authentication, 384 3D Model Compression, 6 Encryption, 34 Feature Extraction, 36 Information Hiding, 34 Matching,
More informationMaximum Flow. Reading: CLRS Sections (skipping Lemma 26.8, Theorem 26.9 (2 nd edtion) or Lemma 26.7, Theorem 26.
Maximum Flow Reading: CLRS Sections 26.126.3 (skipping Lemma 26.8, Theorem 26.9 (2 nd edtion) or Lemma 26.7, Theorem 26.8 (3rd edition) CSE 2331 Steve Lai Flow Network A low network G = ( V, E) is a directed
More informationA Simple Feature Extraction Technique of a Pattern By Hopfield Network
A Simple Feature Extraction Technique of a Pattern By Hopfield Network A.Nag!, S. Biswas *, D. Sarkar *, P.P. Sarkar *, B. Gupta **! Academy of Technology, Hoogly  722 *USIC, University of Kalyani, Kalyani
More informationData Analysis and Manifold Learning Lecture 1: Introduction to spectral and graphbased methods
Data Analysis and Manifold Learning Lecture 1: Introduction to spectral and graphbased methods Radu Horaud INRIA Grenoble RhoneAlpes, France Radu.Horaud@inrialpes.fr http://perception.inrialpes.fr/ Introduction
More informationExploring Indoor Environment for a Mobile Robot Yinxiao Li
Exploring Indoor Environment for a Mobile Robot Yinxiao Li Robotics Lab Columbia University 04/16/2013 Project Objectives Floor Segmentation: Free space to go for a mobile robot ImageBased Segmentation
More informationSocial Media Mining. Graph Essentials
Graph Essentials Graph Basics Measures Graph and Essentials Metrics 2 2 Nodes and Edges A network is a graph nodes, actors, or vertices (plural of vertex) Connections, edges or ties Edge Node Measures
More informationSpatial Statistics Chapter 3 Basics of areal data and areal data modeling
Spatial Statistics Chapter 3 Basics of areal data and areal data modeling Recall areal data also known as lattice data are data Y (s), s D where D is a discrete index set. This usually corresponds to data
More informationProbabilistic Roadmap Path Planning. Reference: Principles of Robot Motion H. Choset et. al. MIT Press
Probabilistic Roadmap Path Planning Reference: Principles of Robot Motion H. Choset et. al. MIT Press Probabilistic Roadmap Path Planning Explicit Geometry based planners (grown obstacles, Voronoi etc)
More informationLearning 3D Models for Scene Understanding. Thomas Funkhouser Princeton University
Learning 3D Models for Scene Understanding Thomas Funkhouser Princeton University Scene Understanding New Scenes User Input Database of Example Scenes Analysis (Learning) Probabilistic Model of Scene Recognition
More informationDetection and Tracking of Moving Objects from a Moving Platform
Detection and Tracking of Moving Objects from a Moving Platform Gérard Medioni Institute of Robotics and Intelligent Systems Computer Science Department Viterbi School of Engineering University of Southern
More informationVoronoi Diagrams Robust and Efficient implementation
Robust and Efficient implementation Master s Thesis Defense Binghamton University Department of Computer Science Thomas J. Watson School of Engineering and Applied Science May 5th, 2005 Motivation The
More informationA study of the 2D SIFT algorithm
A study of the 2D SIFT algorithm Introduction SIFT : Scale invariant feature transform Method for extracting distinctive invariant features from images that can be used to perform reliable matching between
More informationOBJECTLEVEL SEGMENTATION OF RGBD DATA
OBJECTLEVEL SEGMENTATION OF RGBD DATA Hai Huang a, Hao Jiang b, Claus Brenner c, Helmut Mayer a a Institute of Applied Computer Science, Bundeswehr University Munich, Neubiberg, Germany {hai.huang, helmut.mayer}@unibw.de
More informationIn the following we will only consider undirected networks.
Roles in Networks Roles in Networks Motivation for work: Let topology define network roles. Work by Kleinberg on directed graphs, used topology to define two types of roles: authorities and hubs. (Each
More informationExtracting Convex Groups Efficiently
Chapter 7 Extracting Convex Groups Efficiently In the previous chapter, we mentioned that current contour extraction techniques are still unable to deal with unconstrained environments. We argued that
More informationLearning 3D Point Cloud Histograms CS229 Machine Learning Project
Learning 3D Point Cloud Histograms CS229 Machine Learning Project Brian JONES Michel AOUN December 11, 2009 Abstract In this paper we show how using histograms based on the angular relationships between
More informationRecitation. Mladen Kolar
10601 Recitation Mladen Kolar Topics covered after the midterm Learning Theory Hidden Markov Models Neural Networks Dimensionality reduction Nonparametric methods Support vector machines Boosting Learning
More informationData Association Based on Optimization in Graphical Models with Application to Sensor Networks
1 Data Association Based on Optimization in Graphical Models with Application to Sensor Networks Lei Chen, Martin J. Wainwright, Müjdat Çetin, and Alan S. Willsky Abstract We propose techniques based on
More informationFinding crossspecies orthologs with local topology. Michael Robinson
Finding crossspecies orthologs with local topology Acknowledgements SRC: Chris Capraro PNNL: Cliff Joslyn Katy Nowak Brenda Praggastis Emilie Purvine Baylor College of Medicine: Olivier Lichtarge Angela
More informationImage Segmentation I =
1. Introduction Image segmentation is the problem of partitioning an image into homogeneous regions that are semantically meaningful with respect to some characteristic lie intensity or texture [9]. Segmentation
More informationPreprocessing Techniques for OnLine Handwriting
Chapter 3 Preprocessing Techniques for OnLine Handwriting 3.1 Introduction Most of the classification techniques assume that the data is given in a predetermined form, which satisfy certain requirements
More informationCS664 Computer Vision. 6. Features. Dan Huttenlocher
CS664 Computer Vision 6. Features Dan Huttenlocher Local Invariant Features Similarity and affineinvariant keypoint detection Sparse using nonmaximum suppression Stable under lighting and viewpoint
More informationMarkov chains. 3. This stochastic diffusion also provides a useful model of the spread of information throughout an image.
Markov chains 1 Why Markov Models We discuss Markov models now. These have relevance in a few ways: 1. Markov models are a good way to model local, overlapping sets of information, which reflect cues to
More informationDEPARTMENT OF COMPUTER APPLICATIONS QUESTION BANK MC7403 DATA WAREHOUSING AND DATA MINING
UNITI: DATA WAREHOUSE PART A 1. Define: Data Warehouse 2. Write down the applications of data warehousing. 3. Define: data mart 4. What are the advantages of data warehousing. 5. What is the difference
More informationGraylevel morphology. Shape description
Graylevel morphology. Shape description 1 Today Gray scale morphology: an overview of basic principles We will start shape description and representation (Sonka Chapter 8) Note: some slides of this lecture
More informationSpatial Autoregressive Models
Spatial Autoregressive Models Sudipto Banerjee Division of Biostatistics, University of Minnesota September 22, 2009 1 Areal Modelling Areal unit data Maps of raw standard mortality ratios (SMR) of lung
More informationModelling, 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 informationObject 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 informationCountermeasure for the Protection of Face Recognition Systems Against Mask Attacks
Countermeasure for the Protection of Face Recognition Systems Against Mask Attacks Neslihan Kose, JeanLuc Dugelay Multimedia Department EURECOM SophiaAntipolis, France {neslihan.kose, jeanluc.dugelay}@eurecom.fr
More informationθ is the angle opposite to side c.
Mesh Simplification Spring 2010 1 The Law of Cosine Here are some commonly used formulas. First, we learn that c 2 = a 2 + b 2 2abcos(θ), where θ is the angle opposite to side c. Vector form: X Y 2 = X
More informationOutline. Principal Component Analysis (PCA) Singular Value Decomposition (SVD) MultiDimensional Scaling (MDS)
Outline Principal Component Analysis (PCA) Singular Value Decomposition (SVD) MultiDimensional Scaling (MDS) Nonlinear extensions: Kernel PCA Isomap PCA PCA: Principle Component Analysis (closely related
More informationLocalization of 3D Anatomical Structures Using Random Forests and Discrete Optimization
Localization of 3D Anatomical Structures Using Random Forests and Discrete Optimization René Donner, Erich Birngruber, Helmut Steiner, Horst Bischof, Georg Langs Computational Image Analysis and Radiology
More informationSome Basic Morphological Algorithms (4)
2/27/2014 37 Some Basic Morphological Algorithms (4) Convex Hull A set A is said to be convex if the straight line segment joining any two points in A lies entirely within A. The convex hull H or of an
More informationNotes on the Dual Simplex Method
Notes on the Dual Simplex Method 0. The standard dual simplex method 1. A more general and practical dual simplex method 2. Phase I for the dual simplex method 3. Degeneracy in the dual simplex method
More information