PPT-Segmentation as Selective Search for Object Recognition

Kaushik Nandan 1 Contents Introduction Related Work Segmentation as Selective Search Object Recognition System Evaluation Conclusions References 2 1 Introduction Object recognition determining . RRUijlings 12 KEAvande Sande 2 TGevers andAWMSmeulders University of TrentoItaly University of Amsterdam the Netherlands TechnicalReport 2012 submitted to IJCV Abstract This paper addresses the problem of generating possib Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother*, Philip H. S. Torr. Torr Vision Group, University of Oxford. *The . Technische Universität . Dresden. Traditional Goal. By George Kour. S. upervised . By. :. Prof. Dana Ron. Dr. Raid . Saabne. . Masters Thesis Defense. 16 . November, 2014. Tel Aviv University - Faculty of Engineering - Department of Electrical Engineering . Approach for Topology-Change-Aware Video Matting. Jinlong Ju. 1. , Jue Wang. 3. , . Yebin. Liu. 1. , . Haoqian. Wang. 2. , . Qionghai. Dai. 1. Department of Automation, Tsinghua University, China. Zhiyong Yang. Brain and Behavior Discovery Institute. James and Jean Culver Vision . Discovery Institute. Department of Ophthalmology. Georgia Regents University. April. . 4, 2013. Outline. A model of pattern recognition . . selective attention. attending to part of the environment while ignoring the rest. Examples . Listening to instructor while ignoring everything else . Looking around a room for the face of your friend. Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. Behrooz Chitsaz. Director, IP Strategy. Microsoft Research. behroozc@microsoft.com. Frank Seide. Lead Researcher. Microsoft Research. fseide@microsoft.com. Kit Thambiratnam. Researcher. Microsoft Research. 2015. 2. 12.. Jeany Son. References. Bottom-up Segmentation for Top-down . Detection, CVPR 2013. Segmentation-aware Deformable Part Models, CVPR 2014. 2. Prior Works on Segmentation & Recognition. person 1. person 2. horse 1. horse 2. R-CNN: Regions with CNN features. Input. image. Extract region. proposals (~2k / image). Compute CNN. features. Classify regions. (linear SVM). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). Kaushik . Nandan. 1. Contents:. Introduction. Related . Work. Segmentation as Selective . Search. Object Recognition . System. Evaluation. Conclusions. References. 2. 1. Introduction. Object recognition: determining .