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

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. 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. SST Climate Data Record. Kenneth S. Casey. NOAA National Oceanographic Data Center. Seattle, November 2010. 2. The National Oceanographic Data Center (NODC) opened its doors on . November 1, 1960. with only 29 employees who were tasked with compiling the large, disparate collections of oceanographic data into a single system. A few months later in January of 1961, NODC was formally dedicated by the Asst. Secretary of the Navy for Research and Development, James H. Wakelin, Jr. . 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]. Mouna Hammoudi. 1. , Gregg Rothermel. 1. , . Andrea Stocco. 2. . This work has been partially supported by the National Science Foundation through award IIS-1314365.. 1. University of Nebraska-Lincoln, USA. 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. 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). 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. 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]. Christopher J. . Rossbach. , . Owen S. Hofmann, . Emmett . Witchel. University of Texas at Austin, USA. Transactional Memory: . Motivation Mantra. We need better parallel programming tools. (Concurrent programming == programming w/locks). 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 . *Some modification after . seminar. Tackgeun. YOU. Contents. Baseline Algorithm. Fast R-CNN. Observations & Proposals. Fast R-CNN in Microsoft COCO. Object Detection. Definition. Predict the location/label of objects in the scene.