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

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. 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 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 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 Li Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China jjyanzleilywenszli nlpriaaccn Abstract This paper solves the speed bottleneck of deformable part mod 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. 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]. Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. 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. Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. 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. 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. 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. *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.