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. Felzenszwalb University of Chicago pffcsuchicagoedu Ross B Girshick University of Chicago rbgcsuchicagoedu David McAllester TTI at Chicago mcallestertticedu Abstract We describe a general method for building cascade clas si64257ers from partbased de Felzenszwalb University of Chicago pffcsuchicagoedu Ross B Girshick University of Chicago rbgcsuchicagoedu David McAllester TTI at Chicago mcallestertticedu Abstract We describe a general method for building cascade clas si64257ers from partbased de In this work we report on progress in integrating deep convo lutional features with Deformable Part Models DPMs We substitute the HistogramofGradient features of DPMs with Convolutional Neu ral Network CNN features obtained from the topmost 64257fth Based on "Segmentation . of carpal bones from CT images using . skeletally coupled . deformable . models” by . Thomas B. . Sebastian, . Hüseyin. . Tek. , . Joseph J. . Crisco, . Benjamin B. . Kimia. Anthony Yezzi. Georgia Institute of Technology. Snakes: Active Contour Models. Snakes or Active Contours pose the segmentation as an energy minimization problem.. Kass, Witkins & Terzopoulos.. Initialization. - continuous and discrete approaches . 2 : . Exact . and approximate techniques. . - non-submodular and high-order problems. 3: Multi-region segmentation (Milan). - high-dimensional applications . Figure2.CNNequivalenttoasingle-componentDPM.ADPMcomponentcanbewrittenasanequivalentCNNbyunrollingtheDPMdetectionalgorithmintoanetwork.Wepresenttheconstructionforasingle-componentDPM-CNNhereandthenshow PhysicallybaseddeformablemodelshavebeenwidelyembracedbytheComputerGraphicscommunity.ManyproblemsoutlinedinaprevioussurveybyGibsonandMirtich[ 1.IntroductionPhysicallybaseddeformablemodelshavetwodecades 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]. - continuous and discrete approaches . 2 : . Exact . and approximate techniques. . - non-submodular and high-order problems. 3: Multi-region segmentation (Milan). - high-dimensional applications . ). Felzenswalb. , . Girshick. , . McAllester. & . Ramanan. (2010). Slides drawn from a tutorial By R. . Girshick. AP 12% 27% 36% 45% 49%. 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].