PPT-Deformable Part Models (DPM

Felzenswalb Girshick McAllester amp Ramanan 2010 Slides drawn from a tutorial By R Girshick AP 12 27 36 45 49. 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 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 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 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 Figure2.CNNequivalenttoasingle-componentDPM.ADPMcomponentcanbewrittenasanequivalentCNNbyunrollingtheDPMdetectionalgorithmintoanetwork.Wepresenttheconstructionforasingle-componentDPM-CNNhereandthenshow PhysicallybaseddeformablemodelshavebeenwidelyembracedbytheComputerGraphicscommunity.ManyproblemsoutlinedinaprevioussurveybyGibsonandMirtich[ 1.IntroductionPhysicallybaseddeformablemodelshavetwodecades DeformableModelsinMedicalImageAnalysis:ASurveyTimMcInerneyandDemetriTerzopoulosDepartmentofComputerScience,UniversityofToronto,Toronto,ON,CanadaM5S3H5Thisarticlesurveysdeformablemodels,apromisingandvi 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]. 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.. 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. . Shape. . Retrieval. . with . Missing. . Parts. Organizers: . Emanuele . Rodolà. , Or Litany, Michael Bronstein, Alex Bronstein. Motivation. Existing retrieval techniques do not deal well with . 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].