PPT-A coarse-to-fine approach for fast deformable object detection

Author : olivia-moreira | Published Date : 2018-11-06

Marco Pedersoli Andrea Vedaldi Jordi Gonzàlez Fischler Elschlager 1973 Object detection 2 2 Addressing the computational bottleneck branchandbound Blaschko Lampert

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A coarse-to-fine approach for fast deformable object detection: Transcript

Marco Pedersoli Andrea Vedaldi Jordi Gonzàlez Fischler Elschlager 1973 Object detection 2 2 Addressing the computational bottleneck branchandbound Blaschko Lampert 08 Lehmann et al 09. uabes Department of Engineering Science University of Oxford UK vedaldirobotsoxacuk Abstract We present a method that can dramatically accelerate object detection with part based models The method is based on the observation that the cost of detectio Sharpening. Sharpening. Boost detail in an image without introducing noise or artifacts. Undo blur. due to lens aberrations. slight misfocus. Recall Denoising. Input. =. Signal. +. Noise. Deformable . Mirrors:. a new Adaptive Optics scheme . for . Advanced . Gravitational . Wave Interferometers. Marie Kasprzack. Laboratoire de l’Accélérateur Linéaire. European Gravitational Observatory. 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]. - Fitting:. Deformable contours. Computer Vision, FCUP, . 20134. Miguel . Coimbra. Slides by Prof. Kristen . Grauman. Deformable contours. a.k.a. active contours, snakes. Given. : initial contour (model) near desired object . with applications to DNA. Ard. Louis . & Jonathan . Doye. . . Alex Wilber, Iain Johnston. , . Tom . Ouldridge. , . Anna Lewis, Alex Williamson, Gabriel . Villar. , . Pavinder. . Thiara. , Adam Levy. Recognition(. 细粒度分类. ) . 沈志强. Datasets. . -- Caltech-UCSD Bird-200-2011. Number of categories: 200. Number of images: 11,788. Annotations per image: 15 Part Locations, 1 Bounding Box. 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.. Tone Mapping. So far. So far. Tone Mapping. Some Images have too much dynamic range to display on a slide:. (belgium.hdr). Recall Sharpening. Input. =. Coarse . Fine. Tone Mapping. Input. Christopher J. . Rossbach. , . Owen S. Hofmann, . Emmett . Witchel. UT Austin. TM Research Mantra. We need better parallel programming tools. CMP ubiquity. (Concurrent programming == programming w/locks). . Shape. . Retrieval. . with . Missing. . Parts. Organizers: . Emanuele . Rodolà. , Or Litany, Michael Bronstein, Alex Bronstein. Motivation. Existing retrieval techniques do not deal well with . Yundi Jiang, . Jari. . Kolehmainen. , . Yile. . Gu. . Yannis. . Kevrekidis. , Ali . Ozel. & Sankaran Sundaresan. Princeton University, NJ. 2018 NETL Workshop on Multiphase Flow Science. 1. Joel Kamdem Teto. z. Introduction. Fine-grained Multithreading . The ability of a single core to handle multiple thread by:. Providing a register for each thread. Dividing the pipeline bandwidth into N part . Xindian. Long. 2018.09. Outline. Introduction. Object Detection Concept and the YOLO Algorithm. Object Detection Example (CAS Action). Facial Keypoint Detection Example (. DLPy. ). Why SAS Deep Learning .

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