Object and Action Detection from a Single Example
|
|
- Julianna Bryant
- 6 years ago
- Views:
Transcription
1 Object and Action Detection from a Single Example Peyman Milanfar* EE Department University of California, Santa Cruz *Joint work with Hae Jong Seo AFOSR Program Review, June 4-5, 29
2 Take a look at this:
3 See it here?
4 How about here?
5 Or here?
6 Single Example, No Training! (Most) people can find the Dragon Fruit from one look. Even if they ve never seen it before.
7 Outline I. Motivation II. III. Overview Object Detection IV. Action Detection V. Conclusion and Future work
8 Fundamental Problems in Machine Vision Develop a unified framework that can robustly detect objects/actions of interest within images/videos without training query target image query target video 1) Whether objects (actions) are present or not, 2) How many objects (actions)? 3) Where are they located?
9 Challenges in Detection Objects Actions Besides, Contexts: Degradation: 1) different clothes, 2) different illumination, Medical imaging Underwater Raindrop Noise 3) different background 4) action speed Blur
10 Outline I. Motivation II. III. System Overview Object Detection IV. Action Detection V. Conclusion and Future work
11 Object Detection using Local Regression Kernels Local Steering Kernels as Descriptors Using a single example Resemblance Map Detected Similar Objects Query
12 Query Target Object Detection System Overview -.1 stage 1 Compute local steering kernels Descriptors ) Significance.5 Tests.1 3) Non-maxima Suppression Final result PCA stage 2 stage 3 1Image Feature 2Image Feature.1 1Image Feature 3Image 4Template Feature Feature Image 1Image compute feature images Image Feature.5 3Image Feature.1.2 2Image Feature.1 1Image Feature 4Image Feature -.5 3Image Feature -.1 4Template 2Image Feature Feature 4Image Feature 3Image Feature.1 1) Resemblance Map (RM).2 using Matrix Cosine Similarity 4Image Feature Image Feature 2Image Feature Image Feature 2Image Feature 3Image Feature 4Image Feature 3Image Feature 4Image Feature 1Image Fea 2Image Fea 3Image Fea.2 4Image Fea H. Seo and P. Milanfar, Training-free, Generic Object Detection using Locally Adaptive Regression Kernels, Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence
13 Stage 1: Calculation of Local Descriptors SVD
14 Robustness of LSK Descriptors Original Brightness Contrast WGN image change change sigma =
15 System Overview.1 : Stage 2 1Image Feature Query Target stage 1 Compute local steering kernels ) Significance.5 Tests.1 3) Non-maxima Suppression Final result PCA stage 2 compute feature images stage 3.1 1Image Fea.2 2Image Feature Image Feature Image Fea.2 1Image Feature Image 4Template Feature Feature Image 3Image Fea Image 2Image Feature Image Feature Image Feature.5 3Image Feature Image Feature Image Feature Image Feature Image Feature Image Feature Image Feature Image Feature.1 1) Resemblance Map (RM).2 using Matrix Cosine Similarity Image Feature Image Fea -.2 3Image Feature 2Image Feature Image Feature 3Image Feature Image Feature
16 Energy Stage 2: Feature Extraction from Descriptors Densely collected Vectorization Descriptors Apply PCA to for dimensionality reduction Retain the d largest principal components Project and onto d Eigenvalue rank
17 Stage 2: Salient features after PCA LSK Object: Helicopter Query 1Image Target Feature 1 st eigenvector 2 nd eigenvector 3 rd eigenvector 1Image Feature 1Image Feature 1Image Feature 2Image Feature 2Image Feature 2Image Feature 2Image Feature 3Image Feature 3Image Feature 3Image Feature 3Image Feature 1Image Feature 1Image Feature 1Image Feature 1Image Feature 2Image Feature 2Image Feature 2Image Feature 2Image Feature 3Image Feature 3Image Feature 3Image Feature 3Image Feature 4 th eigenvector 4Image Feature 4Image Feature 4Image Feature 4Image Feature 4Image Feature 4Image Feature 4Image Feature Eigenvectors 4Image Feature Query features Target features 11
18 Stage 2: Salient features after PCA LSK Object: Car Query Target st eigenvector nd.2.4 eigenvector rd.2.4 eigenvector th.2.4 eigenvector Eigenvectors Query features Target features 11
19 Image Feature.2 System Overview.1 : Stage Query Target.1 1Image Fea.2 2Image Feature Image Feature Image Fea.2 1Image Feature stage 1 stage 2 3Image 4Template Feature Feature Image 3Image Fea Image.2.1 PCA -.5 2Image Feature Image Feature Image Fea Image Feature Image Feature.5 3Image Feature.1.1 2Image Feature Image Feature Image Feature Compute local steering kernels ) Significance Tests.5.1 3) Non-maxima Suppression -.5 Final result compute feature images stage Image Feature 3Image Feature.1.5 4Image Feature Image Feature Image Feature 4Image Feature Image Feature 3Image Feature.1 1) Resemblance Map (RM).2 using Matrix Cosine Similarity Image Feature
20 Stage 3: Finding similarity between features Target image is divided into a set of overlapping patches Query Target Query Target
21 Stage 3: Correlation based Metric The vector cosine similarity Query Target patch Inner product between two normalized vectors Measures angle while discarding the magnitude
22 Stage 3: Correlation based Metric The vector cosine similarity Query Target patch Inner product between two normalized vectors Measures angle while discarding the magnitude
23 Stage 3: Matrix Cosine Similarity What about a set of vectors? Matrix Cosine Similarity Frobenius Inner product between normalized matrices Query Target patch
24 Stage 3: Matrix Cosine Similarity What about a set of vectors? Matrix Cosine Similarity Frobenius Inner product between normalized matrices Query Target patch A weighted sum of the column-wise vector cosine similarities We can prove optimality of this approach in a naïve Bayes sense.
25 Stage 3: Generate Resemblance Map Resemblance Map (RM) Describes the proportion of variance in common between two features Lawley-Hotelling Trace statistic
26 Stage 3: Non-parametric Significance Tests 1. Is any sufficiently similar object present? i.e., 2. How many objects of interest are present? so that ~ 5 % of variance in common probability.4 Empirical PDF % confidence level Significance level
27 Experimental Results Query Targets Dataset from Weizmann Inst.
28 Experimental Results query query target:1 target target:3 target
29 Experimental Results query target target:2 target:1
30 Experimental Results query target:3 target target:1 target target:2 target 25
31 Experimental Results query Higher resemblance target target:1 target target:2 Lower resemblance
32 Detection rate Experimental Results Weizmann Inst. Object Test Set CIE L*a*b* channel Luminance channel only SIFT descriptor [1999] Shape Context [21] GLOH [25] x 1-4 Detection rate = TP/(TP+FN) False positive rate = FP/(FP+TN) False positive rate
33 Experimental Results The MIT-CMU Face Test Set Query
34 Detection rate Experimental Results 1 The MIT-CMU Face Test Set ROC curve x 1-4 False positive rate 36
35 Gallery Set:1 subjects x 25 different conditions Query Q
36 Gallery Set:1 subjects x 25 different conditions Query Q
37 query target output query target output
38 query target output query target output
39 Outline I. Motivation II. III. System Overview Object Detection IV. Action Detection V. Conclusion and Future work
40 Action Detection System Overview Query stage 1 stage 2 PCA Target Compute space-time local steering kernels compute feature volumes No Motion Estimation No Segmentation No Learning No Prior Information 2) Significance Tests 3) Non-maxima Suppression Final result Frame:256 stage 3 1) Resemblance Map (RM) using Matrix Cosine Similarity H. Seo and P. Milanfar, Generic Action Recognition from a Single Example, Submitted to International Journal of Computer Vision (IJCV), March 29
41 Stage 1: Space Time Descriptors : 3x3 local covariance matrix : space-time coordinates First frame Key frame Last frame 38
42 Experimental Results Shechtman s action test set (Beach walk) Query Typical run time for target (5 frames of 144 x 192) and query (13 frames of 9 x 11) : a little over 1 minute
43 Experimental Results (Multiple Actions) Multiple queries Automatic cropping Very Confusing Moving to the Left Very Confusing Very Confusing Very Confusing Moving to the Left Very Confusing Very Confusing Very Confusing Moving to the Left Very Confusing Very Confusing Very Confusing Moving to the Left Very Confusing Very Confusing Very Confusing Moving to the Left Very Confusing Very Confusing
44 Action Recognition Query Automatic cropping of a short action clip (25 frames) Action Category 1 5 Action Detection 2 6 Scoring Rank least similar most similar
45 Action Classification Performance Average confusion matrices Classification rate: 96 % Classification rate: % Bend Jack Jump box hclp Pjump Run Side Skip Walk hwav jog run Wave1 Wave walk (Weizmann dataset) (KTH dataset) 9 video sequences 6 video sequences Classification rate = 1 (# of miss classification) / (total # of sequences) Evaluation setting: Leave-one-out Classify each testing video as one of the predefined classes by 3-NN (nearest neighbor)
46 Action Classification Performance Comparison with state-of-the art methods (KTH dataset) Our Approach (1-NN) Our Approach (2-NN) Our Approach (3-NN) 89% 93% 95.66% Our Approach (3-NN) 95.66% Kim et al. (28) 95.33% Ali et al.(28) 87.7% Dollar et al. (25) 81.17% Ning et al. (28) 92.31% Niebles et al. (28) 81.5% Wong et al. (27) 71.16% Classification rate = 1 (# of miss classification) / (total # of sequences) We outperform all the state-of-the art methods on KTH dataset.
47 Publications H. Seo and P. Milanfar, Training-free, Generic Object Detection using Locally Adaptive Regression Kernels, Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 H. Seo and P. Milanfar, Generic Action Recognition from a Single Example, Submitted to International Journal of Computer Vision (IJCV), March 29 H. Seo and P. Milanfar, Static and Space-time Visual Saliency Detection by Self- Resemblance, Submitted to Journal of Vision (JoV), May 29 H. Seo and P. Milanfar, Detection of Human Actions from a Single Example, Accepted for publication in International Conference on Computer Vision (ICCV), March 29 H. Seo and P. Milanfar, Nonparametric Bottom-Up Saliency Detection by Self- Resemblance, Accepted for IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1st International Workshop on Visual Scene Understanding (ViSU 9), Miami, June, 29 H. Seo and P. Milanfar, Using Local Regression Kernels for Statistical Object Detection, Proceedings of IEEE International Conference on Image Processing (ICIP), San Diego, 28
48 Conclusions & Future Work Local Steering Kernels are Very Effective Descriptors Simple Approach: PCA + Matrix Cosine Similarity Excellent Detection and Recognition is Achieved without Training Make algorithm scalable for image and (video) retrieval Increase accuracy by incorporating context Detect /recognize objects of interest in general degraded data without explicit restoration
Locally Adaptive Regression Kernels with (many) Applications
Locally Adaptive Regression Kernels with (many) Applications Peyman Milanfar EE Department University of California, Santa Cruz Joint work with Hiro Takeda, Hae Jong Seo, Xiang Zhu Outline Introduction/Motivation
More informationTraining-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIENCE, VOL.32, NO.9, SEPTEMBER 2010 Hae Jong Seo, Student Member,
More informationAction Recognition from One Example
Action Recognition from One Example 1 Hae Jong Seo, Student Member, IEEE, and Peyman Milanfar, Senior Member, IEEE Abstract We present a novel action recognition method based on space-time locally adaptive
More informationGeneric Human Action Recognition from a Single Example
Noname manuscript No. (will be inserted by the editor) Generic Human Action Recognition from a Single Example Hae Jong Seo Peyman Milanfar Received: date / Accepted: date Abstract We present a novel human
More informationLocally Adaptive Regression Kernels with (many) Applications
Locally Adaptive Regression Kernels with (many) Applications Peyman Milanfar EE Department University of California, Santa Cruz Joint work with Hiro Takeda, Hae Jong Seo, Xiang Zhu Outline Introduction/Motivation
More informationAction Recognition in Video by Sparse Representation on Covariance Manifolds of Silhouette Tunnels
Action Recognition in Video by Sparse Representation on Covariance Manifolds of Silhouette Tunnels Kai Guo, Prakash Ishwar, and Janusz Konrad Department of Electrical & Computer Engineering Motivation
More informationAction Recognition & Categories via Spatial-Temporal Features
Action Recognition & Categories via Spatial-Temporal Features 华俊豪, 11331007 huajh7@gmail.com 2014/4/9 Talk at Image & Video Analysis taught by Huimin Yu. Outline Introduction Frameworks Feature extraction
More informationBEING able to automatically detect people in videos is
1 Using Local Steering Kernels to Detect People in Videos Arthur Louis Alaniz II, Christina Marianne G. Mantaring Department of Electrical Engineering, Stanford University {aalaniz, cmgmant} @stanford.edu
More informationMotion illusion, rotating snakes
Motion illusion, rotating snakes Local features: main components 1) Detection: Find a set of distinctive key points. 2) Description: Extract feature descriptor around each interest point as vector. x 1
More informationEVENT DETECTION AND HUMAN BEHAVIOR RECOGNITION. Ing. Lorenzo Seidenari
EVENT DETECTION AND HUMAN BEHAVIOR RECOGNITION Ing. Lorenzo Seidenari e-mail: seidenari@dsi.unifi.it What is an Event? Dictionary.com definition: something that occurs in a certain place during a particular
More informationCOSC160: Detection and Classification. Jeremy Bolton, PhD Assistant Teaching Professor
COSC160: Detection and Classification Jeremy Bolton, PhD Assistant Teaching Professor Outline I. Problem I. Strategies II. Features for training III. Using spatial information? IV. Reducing dimensionality
More informationEvaluation of Local Space-time Descriptors based on Cuboid Detector in Human Action Recognition
International Journal of Innovation and Applied Studies ISSN 2028-9324 Vol. 9 No. 4 Dec. 2014, pp. 1708-1717 2014 Innovative Space of Scientific Research Journals http://www.ijias.issr-journals.org/ Evaluation
More informationLecture 18: Human Motion Recognition
Lecture 18: Human Motion Recognition Professor Fei Fei Li Stanford Vision Lab 1 What we will learn today? Introduction Motion classification using template matching Motion classification i using spatio
More informationFeatures Points. Andrea Torsello DAIS Università Ca Foscari via Torino 155, Mestre (VE)
Features Points Andrea Torsello DAIS Università Ca Foscari via Torino 155, 30172 Mestre (VE) Finding Corners Edge detectors perform poorly at corners. Corners provide repeatable points for matching, so
More informationLinear Discriminant Analysis for 3D Face Recognition System
Linear Discriminant Analysis for 3D Face Recognition System 3.1 Introduction Face recognition and verification have been at the top of the research agenda of the computer vision community in recent times.
More informationLast week. Multi-Frame Structure from Motion: Multi-View Stereo. Unknown camera viewpoints
Last week Multi-Frame Structure from Motion: Multi-View Stereo Unknown camera viewpoints Last week PCA Today Recognition Today Recognition Recognition problems What is it? Object detection Who is it? Recognizing
More informationPreviously. Part-based and local feature models for generic object recognition. Bag-of-words model 4/20/2011
Previously Part-based and local feature models for generic object recognition Wed, April 20 UT-Austin Discriminative classifiers Boosting Nearest neighbors Support vector machines Useful for object recognition
More informationCS 4495 Computer Vision A. Bobick. CS 4495 Computer Vision. Features 2 SIFT descriptor. Aaron Bobick School of Interactive Computing
CS 4495 Computer Vision Features 2 SIFT descriptor Aaron Bobick School of Interactive Computing Administrivia PS 3: Out due Oct 6 th. Features recap: Goal is to find corresponding locations in two images.
More informationLocal Image Features
Local Image Features Computer Vision Read Szeliski 4.1 James Hays Acknowledgment: Many slides from Derek Hoiem and Grauman&Leibe 2008 AAAI Tutorial Flashed Face Distortion 2nd Place in the 8th Annual Best
More informationSUPERVISED NEIGHBOURHOOD TOPOLOGY LEARNING (SNTL) FOR HUMAN ACTION RECOGNITION
SUPERVISED NEIGHBOURHOOD TOPOLOGY LEARNING (SNTL) FOR HUMAN ACTION RECOGNITION 1 J.H. Ma, 1 P.C. Yuen, 1 W.W. Zou, 2 J.H. Lai 1 Hong Kong Baptist University 2 Sun Yat-sen University ICCV workshop on Machine
More informationLocal Image Features
Local Image Features Computer Vision CS 143, Brown Read Szeliski 4.1 James Hays Acknowledgment: Many slides from Derek Hoiem and Grauman&Leibe 2008 AAAI Tutorial This section: correspondence and alignment
More informationIMPROVING SPATIO-TEMPORAL FEATURE EXTRACTION TECHNIQUES AND THEIR APPLICATIONS IN ACTION CLASSIFICATION. Maral Mesmakhosroshahi, Joohee Kim
IMPROVING SPATIO-TEMPORAL FEATURE EXTRACTION TECHNIQUES AND THEIR APPLICATIONS IN ACTION CLASSIFICATION Maral Mesmakhosroshahi, Joohee Kim Department of Electrical and Computer Engineering Illinois Institute
More informationThe SIFT (Scale Invariant Feature
The SIFT (Scale Invariant Feature Transform) Detector and Descriptor developed by David Lowe University of British Columbia Initial paper ICCV 1999 Newer journal paper IJCV 2004 Review: Matt Brown s Canonical
More informationMULTIVARIATE TEXTURE DISCRIMINATION USING A PRINCIPAL GEODESIC CLASSIFIER
MULTIVARIATE TEXTURE DISCRIMINATION USING A PRINCIPAL GEODESIC CLASSIFIER A.Shabbir 1, 2 and G.Verdoolaege 1, 3 1 Department of Applied Physics, Ghent University, B-9000 Ghent, Belgium 2 Max Planck Institute
More informationRobotics Programming Laboratory
Chair of Software Engineering Robotics Programming Laboratory Bertrand Meyer Jiwon Shin Lecture 8: Robot Perception Perception http://pascallin.ecs.soton.ac.uk/challenges/voc/databases.html#caltech car
More informationScale Invariant Feature Transform
Scale Invariant Feature Transform Why do we care about matching features? Camera calibration Stereo Tracking/SFM Image moiaicing Object/activity Recognition Objection representation and recognition Image
More informationObject Recognition. Lecture 11, April 21 st, Lexing Xie. EE4830 Digital Image Processing
Object Recognition Lecture 11, April 21 st, 2008 Lexing Xie EE4830 Digital Image Processing http://www.ee.columbia.edu/~xlx/ee4830/ 1 Announcements 2 HW#5 due today HW#6 last HW of the semester Due May
More informationAdaptive Kernel Regression for Image Processing and Reconstruction
Adaptive Kernel Regression for Image Processing and Reconstruction Peyman Milanfar* EE Department University of California, Santa Cruz *Joint work with Sina Farsiu, Hiro Takeda AFOSR Sensing Program Review,
More informationMETHODS FOR TARGET DETECTION IN SAR IMAGES
METHODS FOR TARGET DETECTION IN SAR IMAGES Kaan Duman Supervisor: Prof. Dr. A. Enis Çetin December 18, 2009 Bilkent University Dept. of Electrical and Electronics Engineering Outline Introduction Target
More informationClassifying Images with Visual/Textual Cues. By Steven Kappes and Yan Cao
Classifying Images with Visual/Textual Cues By Steven Kappes and Yan Cao Motivation Image search Building large sets of classified images Robotics Background Object recognition is unsolved Deformable shaped
More informationEvaluation and comparison of interest points/regions
Introduction Evaluation and comparison of interest points/regions Quantitative evaluation of interest point/region detectors points / regions at the same relative location and area Repeatability rate :
More informationScale Invariant Feature Transform
Why do we care about matching features? Scale Invariant Feature Transform Camera calibration Stereo Tracking/SFM Image moiaicing Object/activity Recognition Objection representation and recognition Automatic
More informationOutline 7/2/201011/6/
Outline Pattern recognition in computer vision Background on the development of SIFT SIFT algorithm and some of its variations Computational considerations (SURF) Potential improvement Summary 01 2 Pattern
More informationVerification: is that a lamp? What do we mean by recognition? Recognition. Recognition
Recognition Recognition The Margaret Thatcher Illusion, by Peter Thompson The Margaret Thatcher Illusion, by Peter Thompson Readings C. Bishop, Neural Networks for Pattern Recognition, Oxford University
More informationWhat do we mean by recognition?
Announcements Recognition Project 3 due today Project 4 out today (help session + photos end-of-class) The Margaret Thatcher Illusion, by Peter Thompson Readings Szeliski, Chapter 14 1 Recognition What
More informationImage Processing. Image Features
Image Processing Image Features Preliminaries 2 What are Image Features? Anything. What they are used for? Some statements about image fragments (patches) recognition Search for similar patches matching
More informationHarder case. Image matching. Even harder case. Harder still? by Diva Sian. by swashford
Image matching Harder case by Diva Sian by Diva Sian by scgbt by swashford Even harder case Harder still? How the Afghan Girl was Identified by Her Iris Patterns Read the story NASA Mars Rover images Answer
More informationK-Means Based Matching Algorithm for Multi-Resolution Feature Descriptors
K-Means Based Matching Algorithm for Multi-Resolution Feature Descriptors Shao-Tzu Huang, Chen-Chien Hsu, Wei-Yen Wang International Science Index, Electrical and Computer Engineering waset.org/publication/0007607
More information3D Object Recognition using Multiclass SVM-KNN
3D Object Recognition using Multiclass SVM-KNN R. Muralidharan, C. Chandradekar April 29, 2014 Presented by: Tasadduk Chowdhury Problem We address the problem of recognizing 3D objects based on various
More informationFeature Detection. Raul Queiroz Feitosa. 3/30/2017 Feature Detection 1
Feature Detection Raul Queiroz Feitosa 3/30/2017 Feature Detection 1 Objetive This chapter discusses the correspondence problem and presents approaches to solve it. 3/30/2017 Feature Detection 2 Outline
More informationSalient Region Detection and Segmentation in Images using Dynamic Mode Decomposition
Salient Region Detection and Segmentation in Images using Dynamic Mode Decomposition Sikha O K 1, Sachin Kumar S 2, K P Soman 2 1 Department of Computer Science 2 Centre for Computational Engineering and
More informationFinal Project Face Detection and Recognition
Final Project Face Detection and Recognition Submission Guidelines: 1. Follow the guidelines detailed in the course website and information page.. Submission in pairs is allowed for all students registered
More informationLearning Human Actions with an Adaptive Codebook
Learning Human Actions with an Adaptive Codebook Yu Kong, Xiaoqin Zhang, Weiming Hu and Yunde Jia Beijing Laboratory of Intelligent Information Technology School of Computer Science, Beijing Institute
More informationShort Survey on Static Hand Gesture Recognition
Short Survey on Static Hand Gesture Recognition Huu-Hung Huynh University of Science and Technology The University of Danang, Vietnam Duc-Hoang Vo University of Science and Technology The University of
More informationA performance evaluation of local descriptors
MIKOLAJCZYK AND SCHMID: A PERFORMANCE EVALUATION OF LOCAL DESCRIPTORS A performance evaluation of local descriptors Krystian Mikolajczyk and Cordelia Schmid Dept. of Engineering Science INRIA Rhône-Alpes
More informationLAPLACIAN OBJECT: ONE-SHOT OBJECT DETECTION BY LOCALITY PRESERVING PROJECTION Sujoy Kumar Biswas and Peyman Milanfar
LAPLACIAN OBJECT: ONE-SHOT OBJECT DETECTION BY LOCALITY PRESERVING PROJECTION Sujoy Kumar Biswas and Peyman Milanfar Electrical Engineering Department University of California, Santa Cruz 1156 High Street,
More informationVision and Image Processing Lab., CRV Tutorial day- May 30, 2010 Ottawa, Canada
Spatio-Temporal Salient Features Amir H. Shabani Vision and Image Processing Lab., University of Waterloo, ON CRV Tutorial day- May 30, 2010 Ottawa, Canada 1 Applications Automated surveillance for scene
More informationRobust Face Recognition via Sparse Representation Authors: John Wright, Allen Y. Yang, Arvind Ganesh, S. Shankar Sastry, and Yi Ma
Robust Face Recognition via Sparse Representation Authors: John Wright, Allen Y. Yang, Arvind Ganesh, S. Shankar Sastry, and Yi Ma Presented by Hu Han Jan. 30 2014 For CSE 902 by Prof. Anil K. Jain: Selected
More informationEigenJoints-based Action Recognition Using Naïve-Bayes-Nearest-Neighbor
EigenJoints-based Action Recognition Using Naïve-Bayes-Nearest-Neighbor Xiaodong Yang and YingLi Tian Department of Electrical Engineering The City College of New York, CUNY {xyang02, ytian}@ccny.cuny.edu
More informationAction recognition in videos
Action recognition in videos Cordelia Schmid INRIA Grenoble Joint work with V. Ferrari, A. Gaidon, Z. Harchaoui, A. Klaeser, A. Prest, H. Wang Action recognition - goal Short actions, i.e. drinking, sit
More informationSCALE INVARIANT FEATURE TRANSFORM (SIFT)
1 SCALE INVARIANT FEATURE TRANSFORM (SIFT) OUTLINE SIFT Background SIFT Extraction Application in Content Based Image Search Conclusion 2 SIFT BACKGROUND Scale-invariant feature transform SIFT: to detect
More informationAutomatic Image Alignment (feature-based)
Automatic Image Alignment (feature-based) Mike Nese with a lot of slides stolen from Steve Seitz and Rick Szeliski 15-463: Computational Photography Alexei Efros, CMU, Fall 2006 Today s lecture Feature
More informationFace detection and recognition. Detection Recognition Sally
Face detection and recognition Detection Recognition Sally Face detection & recognition Viola & Jones detector Available in open CV Face recognition Eigenfaces for face recognition Metric learning identification
More informationApplications Video Surveillance (On-line or off-line)
Face Face Recognition: Dimensionality Reduction Biometrics CSE 190-a Lecture 12 CSE190a Fall 06 CSE190a Fall 06 Face Recognition Face is the most common biometric used by humans Applications range from
More informationHuman Action Recognition Using Independent Component Analysis
Human Action Recognition Using Independent Component Analysis Masaki Yamazaki, Yen-Wei Chen and Gang Xu Department of Media echnology Ritsumeikan University 1-1-1 Nojihigashi, Kusatsu, Shiga, 525-8577,
More informationLarge-Scale Face Manifold Learning
Large-Scale Face Manifold Learning Sanjiv Kumar Google Research New York, NY * Joint work with A. Talwalkar, H. Rowley and M. Mohri 1 Face Manifold Learning 50 x 50 pixel faces R 2500 50 x 50 pixel random
More informationWikipedia - Mysid
Wikipedia - Mysid Erik Brynjolfsson, MIT Filtering Edges Corners Feature points Also called interest points, key points, etc. Often described as local features. Szeliski 4.1 Slides from Rick Szeliski,
More informationFace Recognition Using SIFT- PCA Feature Extraction and SVM Classifier
IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 5, Issue 2, Ver. II (Mar. - Apr. 2015), PP 31-35 e-issn: 2319 4200, p-issn No. : 2319 4197 www.iosrjournals.org Face Recognition Using SIFT-
More informationRecognition: Face Recognition. Linda Shapiro EE/CSE 576
Recognition: Face Recognition Linda Shapiro EE/CSE 576 1 Face recognition: once you ve detected and cropped a face, try to recognize it Detection Recognition Sally 2 Face recognition: overview Typical
More informationLinear Discriminant Analysis in Ottoman Alphabet Character Recognition
Linear Discriminant Analysis in Ottoman Alphabet Character Recognition ZEYNEB KURT, H. IREM TURKMEN, M. ELIF KARSLIGIL Department of Computer Engineering, Yildiz Technical University, 34349 Besiktas /
More informationNIST. Support Vector Machines. Applied to Face Recognition U56 QC 100 NO A OS S. P. Jonathon Phillips. Gaithersburg, MD 20899
^ A 1 1 1 OS 5 1. 4 0 S Support Vector Machines Applied to Face Recognition P. Jonathon Phillips U.S. DEPARTMENT OF COMMERCE Technology Administration National Institute of Standards and Technology Information
More informationLearning video saliency from human gaze using candidate selection
Learning video saliency from human gaze using candidate selection Rudoy, Goldman, Shechtman, Zelnik-Manor CVPR 2013 Paper presentation by Ashish Bora Outline What is saliency? Image vs video Candidates
More informationFace Recognition for Mobile Devices
Face Recognition for Mobile Devices Aditya Pabbaraju (adisrinu@umich.edu), Srujankumar Puchakayala (psrujan@umich.edu) INTRODUCTION Face recognition is an application used for identifying a person from
More informationFeature descriptors. Alain Pagani Prof. Didier Stricker. Computer Vision: Object and People Tracking
Feature descriptors Alain Pagani Prof. Didier Stricker Computer Vision: Object and People Tracking 1 Overview Previous lectures: Feature extraction Today: Gradiant/edge Points (Kanade-Tomasi + Harris)
More informationDimension Reduction CS534
Dimension Reduction CS534 Why dimension reduction? High dimensionality large number of features E.g., documents represented by thousands of words, millions of bigrams Images represented by thousands of
More informationShape Matching. Brandon Smith and Shengnan Wang Computer Vision CS766 Fall 2007
Shape Matching Brandon Smith and Shengnan Wang Computer Vision CS766 Fall 2007 Outline Introduction and Background Uses of shape matching Kinds of shape matching Support Vector Machine (SVM) Matching with
More informationFace Recognition using Eigenfaces SMAI Course Project
Face Recognition using Eigenfaces SMAI Course Project Satarupa Guha IIIT Hyderabad 201307566 satarupa.guha@research.iiit.ac.in Ayushi Dalmia IIIT Hyderabad 201307565 ayushi.dalmia@research.iiit.ac.in Abstract
More informationFace detection and recognition. Many slides adapted from K. Grauman and D. Lowe
Face detection and recognition Many slides adapted from K. Grauman and D. Lowe Face detection and recognition Detection Recognition Sally History Early face recognition systems: based on features and distances
More informationCSE 252B: Computer Vision II
CSE 252B: Computer Vision II Lecturer: Serge Belongie Scribes: Jeremy Pollock and Neil Alldrin LECTURE 14 Robust Feature Matching 14.1. Introduction Last lecture we learned how to find interest points
More informationCHAPTER 3 PRINCIPAL COMPONENT ANALYSIS AND FISHER LINEAR DISCRIMINANT ANALYSIS
38 CHAPTER 3 PRINCIPAL COMPONENT ANALYSIS AND FISHER LINEAR DISCRIMINANT ANALYSIS 3.1 PRINCIPAL COMPONENT ANALYSIS (PCA) 3.1.1 Introduction In the previous chapter, a brief literature review on conventional
More informationCS229: Action Recognition in Tennis
CS229: Action Recognition in Tennis Aman Sikka Stanford University Stanford, CA 94305 Rajbir Kataria Stanford University Stanford, CA 94305 asikka@stanford.edu rkataria@stanford.edu 1. Motivation As active
More informationAggregating Descriptors with Local Gaussian Metrics
Aggregating Descriptors with Local Gaussian Metrics Hideki Nakayama Grad. School of Information Science and Technology The University of Tokyo Tokyo, JAPAN nakayama@ci.i.u-tokyo.ac.jp Abstract Recently,
More informationPatch-based Object Recognition. Basic Idea
Patch-based Object Recognition 1! Basic Idea Determine interest points in image Determine local image properties around interest points Use local image properties for object classification Example: Interest
More informationAn Implementation on Histogram of Oriented Gradients for Human Detection
An Implementation on Histogram of Oriented Gradients for Human Detection Cansın Yıldız Dept. of Computer Engineering Bilkent University Ankara,Turkey cansin@cs.bilkent.edu.tr Abstract I implemented a Histogram
More informationHuman pose estimation using Active Shape Models
Human pose estimation using Active Shape Models Changhyuk Jang and Keechul Jung Abstract Human pose estimation can be executed using Active Shape Models. The existing techniques for applying to human-body
More informationLocal invariant features
Local invariant features Tuesday, Oct 28 Kristen Grauman UT-Austin Today Some more Pset 2 results Pset 2 returned, pick up solutions Pset 3 is posted, due 11/11 Local invariant features Detection of interest
More informationReconstruction of Images Distorted by Water Waves
Reconstruction of Images Distorted by Water Waves Arturo Donate and Eraldo Ribeiro Computer Vision Group Outline of the talk Introduction Analysis Background Method Experiments Conclusions Future Work
More informationHuman Motion Detection and Tracking for Video Surveillance
Human Motion Detection and Tracking for Video Surveillance Prithviraj Banerjee and Somnath Sengupta Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Kharagpur,
More informationKey properties of local features
Key properties of local features Locality, robust against occlusions Must be highly distinctive, a good feature should allow for correct object identification with low probability of mismatch Easy to etract
More informationNOVEL PCA-BASED COLOR-TO-GRAY IMAGE CONVERSION. Ja-Won Seo and Seong Dae Kim
NOVEL PCA-BASED COLOR-TO-GRAY IMAGE CONVERSION Ja-Won Seo and Seong Dae Kim Korea Advanced Institute of Science and Technology (KAIST) Department of Electrical Engineering 21 Daehak-ro, Yuseong-gu, Daejeon
More informationHuman Activity Recognition Using a Dynamic Texture Based Method
Human Activity Recognition Using a Dynamic Texture Based Method Vili Kellokumpu, Guoying Zhao and Matti Pietikäinen Machine Vision Group University of Oulu, P.O. Box 4500, Finland {kello,gyzhao,mkp}@ee.oulu.fi
More informationLarge scale object/scene recognition
Large scale object/scene recognition Image dataset: > 1 million images query Image search system ranked image list Each image described by approximately 2000 descriptors 2 10 9 descriptors to index! Database
More informationMotion Estimation and Optical Flow Tracking
Image Matching Image Retrieval Object Recognition Motion Estimation and Optical Flow Tracking Example: Mosiacing (Panorama) M. Brown and D. G. Lowe. Recognising Panoramas. ICCV 2003 Example 3D Reconstruction
More informationFace Detection and Recognition in an Image Sequence using Eigenedginess
Face Detection and Recognition in an Image Sequence using Eigenedginess B S Venkatesh, S Palanivel and B Yegnanarayana Department of Computer Science and Engineering. Indian Institute of Technology, Madras
More informationSelection of Scale-Invariant Parts for Object Class Recognition
Selection of Scale-Invariant Parts for Object Class Recognition Gy. Dorkó and C. Schmid INRIA Rhône-Alpes, GRAVIR-CNRS 655, av. de l Europe, 3833 Montbonnot, France fdorko,schmidg@inrialpes.fr Abstract
More informationEdge and corner detection
Edge and corner detection Prof. Stricker Doz. G. Bleser Computer Vision: Object and People Tracking Goals Where is the information in an image? How is an object characterized? How can I find measurements
More informationImage matching. Announcements. Harder case. Even harder case. Project 1 Out today Help session at the end of class. by Diva Sian.
Announcements Project 1 Out today Help session at the end of class Image matching by Diva Sian by swashford Harder case Even harder case How the Afghan Girl was Identified by Her Iris Patterns Read the
More informationAdvanced Video Content Analysis and Video Compression (5LSH0), Module 4
Advanced Video Content Analysis and Video Compression (5LSH0), Module 4 Visual feature extraction Part I: Color and texture analysis Sveta Zinger Video Coding and Architectures Research group, TU/e ( s.zinger@tue.nl
More informationAdaptive Action Detection
Adaptive Action Detection Illinois Vision Workshop Dec. 1, 2009 Liangliang Cao Dept. ECE, UIUC Zicheng Liu Microsoft Research Thomas Huang Dept. ECE, UIUC Motivation Action recognition is important in
More informationHarder case. Image matching. Even harder case. Harder still? by Diva Sian. by swashford
Image matching Harder case by Diva Sian by Diva Sian by scgbt by swashford Even harder case Harder still? How the Afghan Girl was Identified by Her Iris Patterns Read the story NASA Mars Rover images Answer
More informationDynamic Human Shape Description and Characterization
Dynamic Human Shape Description and Characterization Z. Cheng*, S. Mosher, Jeanne Smith H. Cheng, and K. Robinette Infoscitex Corporation, Dayton, Ohio, USA 711 th Human Performance Wing, Air Force Research
More informationA GENERIC FACE REPRESENTATION APPROACH FOR LOCAL APPEARANCE BASED FACE VERIFICATION
A GENERIC FACE REPRESENTATION APPROACH FOR LOCAL APPEARANCE BASED FACE VERIFICATION Hazim Kemal Ekenel, Rainer Stiefelhagen Interactive Systems Labs, Universität Karlsruhe (TH) 76131 Karlsruhe, Germany
More informationAutomatic Gait Recognition. - Karthik Sridharan
Automatic Gait Recognition - Karthik Sridharan Gait as a Biometric Gait A person s manner of walking Webster Definition It is a non-contact, unobtrusive, perceivable at a distance and hard to disguise
More informationEE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm
EE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm Group 1: Mina A. Makar Stanford University mamakar@stanford.edu Abstract In this report, we investigate the application of the Scale-Invariant
More informationSIFT: SCALE INVARIANT FEATURE TRANSFORM SURF: SPEEDED UP ROBUST FEATURES BASHAR ALSADIK EOS DEPT. TOPMAP M13 3D GEOINFORMATION FROM IMAGES 2014
SIFT: SCALE INVARIANT FEATURE TRANSFORM SURF: SPEEDED UP ROBUST FEATURES BASHAR ALSADIK EOS DEPT. TOPMAP M13 3D GEOINFORMATION FROM IMAGES 2014 SIFT SIFT: Scale Invariant Feature Transform; transform image
More informationPart based models for recognition. Kristen Grauman
Part based models for recognition Kristen Grauman UT Austin Limitations of window-based models Not all objects are box-shaped Assuming specific 2d view of object Local components themselves do not necessarily
More informationDimensionality Reduction and Classification through PCA and LDA
International Journal of Computer Applications (09 8887) Dimensionality Reduction and Classification through and Telgaonkar Archana H. PG Student Department of CS and IT Dr. BAMU, Aurangabad Deshmukh Sachin
More informationProf. Feng Liu. Spring /26/2017
Prof. Feng Liu Spring 2017 http://www.cs.pdx.edu/~fliu/courses/cs510/ 04/26/2017 Last Time Re-lighting HDR 2 Today Panorama Overview Feature detection Mid-term project presentation Not real mid-term 6
More informationVisual Learning and Recognition of 3D Objects from Appearance
Visual Learning and Recognition of 3D Objects from Appearance (H. Murase and S. Nayar, "Visual Learning and Recognition of 3D Objects from Appearance", International Journal of Computer Vision, vol. 14,
More informationBag of Optical Flow Volumes for Image Sequence Recognition 1
RIEMENSCHNEIDER, DONOSER, BISCHOF: BAG OF OPTICAL FLOW VOLUMES 1 Bag of Optical Flow Volumes for Image Sequence Recognition 1 Hayko Riemenschneider http://www.icg.tugraz.at/members/hayko Michael Donoser
More information