PPT-Closing the Loop for Edge Detection and Object Proposals

Yao Lu Linda Shapiro University of Washington AAAI17 Background human visual perception Object perception Edge perception Assigning edges to regions Grouping regions to objects Bottomup and topdown pathways. Lawrence Zitnick and Piotr Dollar Microsoft Research Abstract The use of object proposals is an e64256ective recent approach for increasing the computational e64259ciency of object detection We pro pose a novel method for generating object bounding Author: Michael Sedivy. Introduction. Edge Detection in Image Processing. MCMC and the Use of Gibbs Sampler. Input. Results. Conclusion/Future Work. References. Edge Detection. Detecting Edges in images is a complex task, but it useful in other image processing problems. Alex Wade. CAP6938 Final Project. Introduction. GPU based implementation of . A Computational Approach to Edge Detection. by John Canny. Paper presents an accurate, localized edge detection method. Purpose. CSE . 576. Ali Farhadi. Many slides from Steve Seitz and Larry . Zitnick. Edge. Attneave's. Cat (1954) . Edges are caused by a variety of factors. depth discontinuity. surface color discontinuity. illumination discontinuity. Winter in . Kraków. photographed by . Marcin. . Ryczek. Edge detection. Goal: . Identify sudden changes (discontinuities) in an image. Intuitively, most semantic and shape information from the image can be encoded in the edges. Binarized Normed Gradients for Objectness Estimation at 300fps. Ming-Ming Cheng. 1. Ziming Zhang. 2. Wen-Yan Li. 1. Philip H. S. Torr. 1. 1. Torr . Vision Group, Oxford . University . 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. Facebook AI Research. Wenchi. Ma. Data: 11/04/2016. More information from object detection. More information from object detection. More information from object detection. Object Detection for now with Deep Learning. Source: D. Lowe, L. Fei-Fei. Canny edge detector. Filter image with x, y derivatives of Gaussian . Find magnitude and orientation of gradient. Non-maximum suppression:. Thin multi-pixel wide “ridges” down to single pixel width. 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. Alex Wade. CAP6938 Final Project. Introduction. GPU based implementation of . A Computational Approach to Edge Detection. by John Canny. Paper presents an accurate, localized edge detection method. Purpose. hindcast . results and its preliminary evaluation in the South China Sea. Shihe Ren. a. , Xueming Zhu. a. , and Drevillon Marie. b. a. . National Marine Environmental Forcasting Center, Beijing, China. What is Edge Detection?. Identifying points/Edges . in a digital image at which the image brightness changes sharply . or . has . discontinuities. . - Edges are significant local changes of intensity in an image.. Edge. Attneave's. Cat (1954) . 2. Edges are caused by a variety of . factors.. depth discontinuity. surface color discontinuity. illumination discontinuity. surface normal discontinuity. Origin of edges.