On the Object Proposal Presented by Yao Lu 10-03-2014.

Slides:

Advertisements

Similar presentations

Latent SVMs for Human Detection with a Locally Affine Deformation Field Ľubor Ladický 1 Phil Torr 2 Andrew Zisserman 1 1 University of Oxford 2 Oxford.

Advertisements

Regionlets for Generic Object Detection

Automatic Image Annotation Using Group Sparsity

Unsupervised Detection of Regions of Interest Using Iterative Link Analysis Gunhee Kim 1 Antonio Torralba 2 1: SCS, CMU 2: CSAIL, MIT Neural Information.

Ľubor Ladický1 Phil Torr2 Andrew Zisserman1

Ming-Ming Cheng 1 Ziming Zhang 2 Wen-Yan Lin 3 Philip H. S. Torr 1 1 Oxford University, 2 Boston University 3 Brookes Vision Group Training a generic objectness.

Classification using intersection kernel SVMs is efficient Joint work with Subhransu Maji and Alex Berg Jitendra Malik UC Berkeley.

Lecture 31: Modern object recognition

Many slides based on P. FelzenszwalbP. Felzenszwalb General object detection with deformable part-based models.

Robust Object Tracking via Sparsity-based Collaborative Model

Global spatial layout: spatial pyramid matching Spatial weighting the features Beyond bags of features: Adding spatial information.

Object-centric spatial pooling for image classification Olga Russakovsky, Yuanqing Lin, Kai Yu, Li Fei-Fei ECCV 2012.

Enhancing Exemplar SVMs using Part Level Transfer Regularization 1.

Large-Scale Object Recognition with Weak Supervision

High-level Component Filtering for Robust Scene Text Detection

Groups of Adjacent Contour Segments for Object Detection Vittorio Ferrari Loic Fevrier Frederic Jurie Cordelia Schmid.

A Robust Pedestrian Detection Approach Based on Shapelet Feature and Haar Detector Ensembles Wentao Yao, Zhidong Deng TSINGHUA SCIENCE AND TECHNOLOGY ISSNl.

More sliding window detection: Discriminative part-based models Many slides based on P. FelzenszwalbP. Felzenszwalb.

Student: Yao-Sheng Wang Advisor: Prof. Sheng-Jyh Wang ARTICULATED HUMAN DETECTION 1 Department of Electronics Engineering National Chiao Tung University.

Generic Object Detection using Feature Maps Oscar Danielsson Stefan Carlsson

Learning Spatial Context: Using stuff to find things Geremy Heitz Daphne Koller Stanford University October 13, 2008 ECCV 2008.

What, Where & How Many? Combining Object Detectors and CRFs

From R-CNN to Fast R-CNN

Generic object detection with deformable part-based models

From Edges to Objects Piotr Dollár and Larry Zitnick.

Learning Based Hierarchical Vessel Segmentation

“Secret” of Object Detection Zheng Wu (Summer intern in MSRNE) Sep. 3, 2010 Joint work with Ce Liu (MSRNE) William T. Freeman (MIT) Adam Kalai (MSRNE)

Detection, Segmentation and Fine-grained Localization

Efficient Region Search for Object Detection Sudheendra Vijayanarasimhan and Kristen Grauman Department of Computer Science, University of Texas at Austin.

Object Detection with Discriminatively Trained Part Based Models

Lecture 31: Modern recognition CS4670 / 5670: Computer Vision Noah Snavely.

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

Pedestrian Detection and Localization

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

Efficient Subwindow Search: A Branch and Bound Framework for Object Localization ‘PAMI09 Beyond Sliding Windows: Object Localization by Efficient Subwindow.

CS654: Digital Image Analysis Lecture 30: Clustering based Segmentation Slides are adapted from:

Geodesic Saliency Using Background Priors

Hierarchical Matching with Side Information for Image Classification

Fully Convolutional Networks for Semantic Segmentation

Category Independent Region Proposals Ian Endres and Derek Hoiem University of Illinois at Urbana-Champaign.

An Effective & Interactive Approach to Particle Tracking for DNA Melting Curve Analysis 李穎忠 DEPARTMENT OF COMPUTER SCIENCE & INFORMATION ENGINEERING NATIONAL.

Recognition Using Visual Phrases

Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.

Object Recognition by Integrating Multiple Image Segmentations Caroline Pantofaru, Cordelia Schmid, Martial Hebert ECCV 2008 E.

Describing People: A Poselet-Based Approach to Attribute Classification.

Regionlets for Generic Object Detection IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 37, NO. 10, OCTOBER 2015 Xiaoyu Wang, Ming.

Rich feature hierarchies for accurate object detection and semantic segmentation 2014 IEEE Conference on Computer Vision and Pattern Recognition Ross Girshick,

Minimum Barrier Salient Object Detection at 80 FPS JIANMING ZHANG, STAN SCLAROFF, ZHE LIN, XIAOHUI SHEN, BRIAN PRICE, RADOMIR MECH IEEE INTERNATIONAL CONFERENCE.

Spatial Localization and Detection

Week 4: 6/6 – 6/10 Jeffrey Loppert. This week.. Coded a Histogram of Oriented Gradients (HOG) Feature Extractor Extracted features from positive and negative.

1 Bilinear Classifiers for Visual Recognition Computational Vision Lab. University of California Irvine To be presented in NIPS 2009 Hamed Pirsiavash Deva.

Strong Supervision from Weak Annotation: Interactive Training of Deformable Part Models S. Branson, P. Perona, S. Belongie.

Recent developments in object detection

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

Faster R-CNN – Concepts

Object detection with deformable part-based models

Data Driven Attributes for Action Detection

Object Localization Goal: detect the location of an object within an image Fully supervised: Training data labeled with object category and ground truth.

Object detection as supervised classification

R-CNN region By Ilia Iofedov 11/11/2018 BGU, DNN course 2016.

Enhanced-alignment Measure for Binary Foreground Map Evaluation

Cheng-Ming Huang, Wen-Hung Liao Department of Computer Science

Object detection.

Learning to Combine Bottom-Up and Top-Down Segmentation

Object Detection + Deep Learning

Edge boxes: Locating object proposals from edges

Progress report 2019/1/14 PHHung.

Outline Background Motivation Proposed Model Experimental Results

RCNN, Fast-RCNN, Faster-RCNN

Introduction Face detection and alignment are essential to many applications such as face recognition, facial expression recognition, age identification,

Presentation transcript:

On the Object Proposal Presented by Yao Lu

Intro to Object Proposal Motivation Sliding window based object detection Iterate over window size, aspect ratio, and location

Intro to Object Proposal Goal Fast execution High recall with low # of candidate boxes Unsupervised/weakly supervised Difference with image saliency

Selective Search Selective search K. Van de Sande et al. Segmentation as selective search for object recognition. ICCV Merge of multiple segmentation to propose candidate box 1536 boxes = 96.7 recall

BING M. Cheng et al. “BING: Binarized normed gradients for objectnetss estimation at 300fps”, CVPR Resize images to different size & aspect ratio Train an 8x8 template using a linear SVM Use linear combination to integrate predictions. Binarize the template to speed-up

BING Pascal VOC => 0.95 recall Speed

Geodesic Object Proposal P. Krahenbuhl and V. Koltun. Geodesic Object Proposals. ECCV 2014.

Edge Boxes

Conclusion Object proposal greatly enhances object detection efficiency. Current methods have very simple intuitions Selective search BING Geodesic object proposal Edge boxes Future goal of object proposal: Less # of boxes Higher recall Faster speed