Computer Vision REU Week 6 Adam Kavanaugh. This week in review  Worked toward narrowing down a research topic  Read many papers in both 2004 and 2005.

Slides:

Advertisements

Similar presentations

Distinctive Image Features from Scale-Invariant Keypoints

Advertisements

Ranking Multimedia Databases via Relevance Feedback with History and Foresight Support / 12 I9 CHAIR OF COMPUTER SCIENCE 9 DATA MANAGEMENT AND EXPLORATION.

3D Model Matching with Viewpoint-Invariant Patches(VIP) Reporter ：鄒嘉恆 Date ： 10/06/2009.

Presented by Xinyu Chang

Foreground Focus: Finding Meaningful Features in Unlabeled Images Yong Jae Lee and Kristen Grauman University of Texas at Austin.

VisualRank: Applying PageRank to Large-Scale Image Search Yushi Jing, Member, IEEE, and Shumeet Baluja, Member, IEEE.

MIT CSAIL Vision interfaces Towards efficient matching with random hashing methods… Kristen Grauman Gregory Shakhnarovich Trevor Darrell.

MIT CSAIL Vision interfaces Approximate Correspondences in High Dimensions Kristen Grauman* Trevor Darrell MIT CSAIL (*) UT Austin…

Activity Recognition Aneeq Zia. Agenda What is activity recognition Typical methods used for action recognition “Evaluation of local spatio-temporal features.

Kiyoshi Irie, Tomoaki Yoshida, and Masahiro Tomono 2011 IEEE International Conference on Robotics and Automation Shanghai International Conference Center.

A KLT-Based Approach for Occlusion Handling in Human Tracking Chenyuan Zhang, Jiu Xu, Axel Beaugendre and Satoshi Goto 2012 Picture Coding Symposium.

Computer Vision REU Week 2 Adam Kavanaugh. Video Canny Put canny into a loop in order to process multiple frames of a video sequence Put canny into a.

Learning similarity measure for natural image retrieval with relevance feedback Reporter: Francis 2005/3/3.

Object Recognition with Invariant Features n Definition: Identify objects or scenes and determine their pose and model parameters n Applications l Industrial.

Content Based Image Clustering and Image Retrieval Using Multiple Instance Learning Using Multiple Instance Learning Xin Chen Advisor: Chengcui Zhang Department.

Funding Networks Abdullah Sevincer University of Nevada, Reno Department of Computer Science & Engineering.

Effective Image Database Search via Dimensionality Reduction Anders Bjorholm Dahl and Henrik Aanæs IEEE Computer Society Conference on Computer Vision.

CMPT-884 Jan 18, 2010 Video Copy Detection using Hadoop Presented by: Cameron Harvey Naghmeh Khodabakhshi CMPT 820 December 2, 2010.

An efficient and effective region-based image retrieval framework Reporter: Francis 2005/5/12.

Object Recognition with Invariant Features n Definition: Identify objects or scenes and determine their pose and model parameters n Applications l Industrial.

CS335 Principles of Multimedia Systems Content Based Media Retrieval Hao Jiang Computer Science Department Boston College Dec. 4, 2007.

Motivation Where is my W-2 Form?. Video-based Tracking Camera view of the desk Camera Overhead video camera.

Automatic Image Alignment (feature-based) : Computational Photography Alexei Efros, CMU, Fall 2005 with a lot of slides stolen from Steve Seitz and.

1 Diffusion Distance for Histogram Comparison, CVPR06. Haibin Ling, Kazunori Okada Group Meeting Presented by Wyman 3/14/2006.

Distinctive image features from scale-invariant keypoints. David G. Lowe, Int. Journal of Computer Vision, 60, 2 (2004), pp Presented by: Shalomi.

Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point D. Lowe, IJCV 2004 Presenting – Anat Kaspi.

Scale Invariant Feature Transform (SIFT)

Detecting Patterns So far Specific patterns (eyes) Generally useful patterns (edges) Also (new) “Interesting” distinctive patterns ( No specific pattern:

Automatic Image Alignment (feature-based) : Computational Photography Alexei Efros, CMU, Fall 2006 with a lot of slides stolen from Steve Seitz and.

Sebastian Thrun CS223B Computer Vision, Winter Stanford CS223B Computer Vision, Winter 2005 Lecture 3 Advanced Features Sebastian Thrun, Stanford.

CMPUT 617 (Topics in Computing Science): Advanced Image Analysis Nilanjan Ray Fall 2012 Computing Science University of Alberta.

Large Scale Recognition and Retrieval. What does the world look like? High level image statistics Object Recognition for large-scale search Focus on scaling.

Distinctive Image Features from Scale-Invariant Keypoints By David G. Lowe, University of British Columbia Presented by: Tim Havinga, Joël van Neerbos.

The 2nd International Conference of e-Learning and Distance Education, 21 to 23 February 2011, Riyadh, Saudi Arabia Prof. Dr. Torky Sultan Faculty of Computers.

Unsupervised Learning of Categories from Sets of Partially Matching Image Features Kristen Grauman and Trevor Darrel CVPR 2006 Presented By Sovan Biswas.

AUTOMATIC ANNOTATION OF GEO-INFORMATION IN PANORAMIC STREET VIEW BY IMAGE RETRIEVAL Ming Chen, Yueting Zhuang, Fei Wu College of Computer Science, Zhejiang.

Mutual Information-based Stereo Matching Combined with SIFT Descriptor in Log-chromaticity Color Space Yong Seok Heo, Kyoung Mu Lee, and Sang Uk Lee.

Computer Science Department Distribution Fields A Unifying Representation for Low-Level Vision Problems Erik Learned-Miller with Laura Sevilla Lara, Manju.

ALIGNMENT OF 3D ARTICULATE SHAPES. Articulated registration Input: Two or more 3d point clouds (possibly with connectivity information) of an articulated.

Svetlana Lazebnik, Cordelia Schmid, Jean Ponce

UCF REU: Weeks 1 & 2. Gradient Code Gradient Direction of the Gradient: Calculating theta.

Computer Vision 776 Jan-Michael Frahm 12/05/2011 Many slides from Derek Hoiem, James Hays.

Efficient EMD-based Similarity Search in Multimedia Databases via Flexible Dimensionality Reduction / 16 I9 CHAIR OF COMPUTER SCIENCE 9 DATA MANAGEMENT.

Visual Categorization With Bags of Keypoints Original Authors: G. Csurka, C.R. Dance, L. Fan, J. Willamowski, C. Bray ECCV Workshop on Statistical Learning.

CVPR 2006 New York City Spatial Random Partition for Common Visual Pattern Discovery Junsong Yuan and Ying Wu EECS Dept. Northwestern Univ.

Kylie Gorman WEEK 1-2 REVIEW. CONVERTING AN IMAGE FROM RGB TO HSV AND DISPLAY CHANNELS.

A feature-based kernel for object classification P. Moreels - J-Y Bouguet Intel.

Problem Query image by content in an image database.

CS654: Digital Image Analysis

Line Matching Jonghee Park GIST CV-Lab..  Lines –Fundamental feature in many computer vision fields 3D reconstruction, SLAM, motion estimation –Useful.

Quiz Week 8 Topical. Topical Quiz (Section 2) What is the difference between Computer Vision and Computer Graphics What is the difference between Computer.

3D Motion Data Mining Multimedia Project Multimedia and Network Lab, Department of Computer Science.

1 Review and Summary We have covered a LOT of material, spending more time and more detail on 2D image segmentation and analysis, but hopefully giving.

Finding Clusters within a Class to Improve Classification Accuracy Literature Survey Yong Jae Lee 3/6/08.

Distinctive Image Features from Scale-Invariant Keypoints Presenter :JIA-HONG,DONG Advisor : Yen- Ting, Chen 1 David G. Lowe International Journal of Computer.

SIFT Scale-Invariant Feature Transform David Lowe

Paper – Stephen Se, David Lowe, Jim Little

Nearest-neighbor matching to feature database

Nonparametric Semantic Segmentation

Paper Presentation: Shape and Matching

Finding Clusters within a Class to Improve Classification Accuracy

Features Readings All is Vanity, by C. Allan Gilbert,

Nearest-neighbor matching to feature database

Optical Flow For Vision-Aided Navigation

Aim of the project Take your image Submit it to the search engine

REU Week 1 Ivette Carreras UCF.

Large Scale Image Deduplication

Color Image Retrieval based on Primitives of Color Moments

Presented by Xu Miao April 20, 2005

Color Image Retrieval based on Primitives of Color Moments

Presentation transcript:

Computer Vision REU Week 6 Adam Kavanaugh

This week in review  Worked toward narrowing down a research topic  Read many papers in both 2004 and 2005 IEEE CVPR conference proceedings as well as others.  Mostly stuck with papers in the areas of: Image retrieval (general and medical)Image retrieval (general and medical) MotionMotion TrackingTracking

Content Based Image Retrieval  Basics of Image Retrieval Extract features or distinctive information from a database and a queryExtract features or distinctive information from a database and a query Use a type of distance function to find the most similar matchesUse a type of distance function to find the most similar matches Improve query results by getting user feedback, or use a learning approach to limit user involvementImprove query results by getting user feedback, or use a learning approach to limit user involvement

Image Retrieval Papers  Recurring Methods used: Fuzzy C-Mean clusteringFuzzy C-Mean clustering Earth Mover’s DistanceEarth Mover’s Distance Support Vector MachinesSupport Vector Machines  Some Papers:   Hoi, S.C.H.; Lyu, M.R., "A semi-supervised active learning framework for image retrieval"  Grauman, K.; Darrell, T., "Efficient image matching with distributions of local invariant features"  Hertz, T.; Bar-Hillel, A.; Weinshall, D., "Learning distance functions for image retrieval"

Image Retrieval as a Topic  Working with Dr. Sugaya to perform retrieval of gene mapping MRI’s  Gene mapping in this context is disease association

Motion and Tracking  Primary interest in motion and tracking falls into the category of Robotics Applications and/or previous work with optical flow.  Would like to see some kind of relevant application come out of the work on optical flow.

Some Topic Ideas in Motion  The presentations of the COCOA system and similar Aerial tracking applications are of interest.  Applying SIFT descriptors to give more accurate motion fields in Optical Flow.  Navigation and obstacle detection in motion

Some More Papers  Optical Flow Lei Yuan; Jinzong Li; Bing Zhu; Yulong Qiao, "A Discontinuity-preserving Optical Flow Algorithm"Lei Yuan; Jinzong Li; Bing Zhu; Yulong Qiao, "A Discontinuity-preserving Optical Flow Algorithm" Zhimin Fan; Ying Wu; Ming Yang, "Multiple collaborative kernel tracking"Zhimin Fan; Ying Wu; Ming Yang, "Multiple collaborative kernel tracking"  Tracking Greenspan, M.; Limin Shang; Jasiobedzki, P., "Efficient tracking with the Bounded Hough Transform"Greenspan, M.; Limin Shang; Jasiobedzki, P., "Efficient tracking with the Bounded Hough Transform" Freedman, D.; Turek, M.W., "Illumination- invariant tracking via graph cuts"Freedman, D.; Turek, M.W., "Illumination- invariant tracking via graph cuts"