PPT-Segmentation-based Deformable Part Models

2015 2 12 Jeany Son References Bottomup Segmentation for Topdown Detection CVPR 2013 Segmentationaware Deformable Part Models CVPR 2014 2 Prior Works on Segmentation amp Recognition. The ARMApq series is generated by 12 pt pt 12 qt 949 949 949 Thus is essentially the sum of an autoregression on past values of and a moving average o tt t white noise process Given together with starting values of the whole series Divvala Alexei A Efros and Martial Hebert Robotics Institute Carnegie Mellon University Abstract The Deformable Parts Model DPM has recently emerged as a very useful and popular tool for tackling the intracategory diversity problem in object detecti Varun. . Gulshan. †. , . Carsten. Rother. ‡. , Antonio . Criminisi. ‡. , Andrew Blake. ‡. and Andrew . Zisserman. †. . 1. Star-convexity. †. Visual . Geometry Group, University of . Oxford, UK . Deformable . Mirrors:. a new Adaptive Optics scheme . for . Advanced . Gravitational . Wave Interferometers. Marie Kasprzack. Laboratoire de l’Accélérateur Linéaire. European Gravitational Observatory. 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. 6. Objectives. Explain STP Process. Segmentation Bases. Target Segment Criteria. Elements of Effective Positioning. Creating Brand Value/Equity. STP Marketing & the Evolution of Marketing Strategy. Sungsu. Lim. AALAB, KAIST. Image Segmentation. Computer vision. : make machine to see or to understand/ . interpret . the scenes (images & videos) like human do.. Image segmentation. is one of the most challenging issues in computer vision.. Marco Pedersoli Andrea Vedaldi Jordi Gonzàlez. [Fischler Elschlager 1973]. Object detection. 2. 2. Addressing the computational bottleneck. branch-and-bound . [Blaschko Lampert 08, Lehmann et al. 09]. for Object Detection. Forrest Iandola, . Ning. Zhang, Ross . Girshick. , Trevor Darrell, and Kurt . Keutzer. Deformable Parts Model (DPM): state of the art algorithm for object detection [1]. Several attempts to accelerate multi-category DPM detection, such as [2] [3]. Chapter Objectives. After . reading this chapter you should be able . to:. Appreciate . the importance of market segmentation for specific consumer groups and realize that the targeting decision is the initial and most fundamental of all . Dr. Ananda Hussein. Ford. ’. s Model T Followed a Mass Market Approach. Four levels of Micromarketing. Segments. Local areas. Individuals. Niches. What is a Market Segment?. A . market segment. consists of a group of customers who share a similar set of needs ad wants. . Monday, Feb . 21. Prof. Kristen . Grauman. UT-Austin. Recap so far:. Grouping and Fitting. Goal: move from array of pixel . values (or filter outputs) . to a collection of regions, objects, and shapes.. Presentation by Jonathan Kaan DeBoy. Paper by Hyunggi Cho, Paul E. Rybski and Wende Zhang. 1. Motivation. B. uild understanding . of surrounding. D. etect . vulnerable road users (VRU). B. icyclist. M. Marco Pedersoli Andrea Vedaldi Jordi Gonzàlez. [Fischler Elschlager 1973]. Object detection. 2. 2. Addressing the computational bottleneck. branch-and-bound . [Blaschko Lampert 08, Lehmann et al. 09].