PPT-Image gradients and edges

April 11 th 2017 Yong Jae Lee UC Davis Announcements PS0 due this Friday Questions 2 Last time Image formation Linear filters and convolution useful for Image smoothing removing noise. edusg Chaoqiang Liu Center for Wavelets Approx and Info Proc National University of Singapore Singapore 117542 tslliucqnusedusg Abstract Restoration of a degraded image from motion blurring is highly dependent on the estimation of the blurring kernel Algorithms for Image Analysis. Correspondence. (stereo and etc.). CS . 4487/6587 . Algorithms for Image Analysis. . Correspondence. Matching. and . Correspondence. . problems. Stereo. . local methods (windows) . Properties . on Convective Initiation in the Sahel. Amanda . Gounou. (1). , Christopher Taylor. (2) . , Francoise . Guichard. (1). , Phil Harris. (2) . , Richard Ellis. (2). , Fleur . Couvreux. (1). and Martin De . Cheng. 1. Ziming Zhang. 2. Wen-Yan Lin. 3. . Philip H. S. . Torr. 1. 1. Oxford University, . 2. Boston University . 3. Brookes Vision Group. Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients. Based on this observation, we propose to use a binarized normed gradients (BING) for efficient objectness estimation. Experiments on the . Jitendra. Malik. Different kinds of images. Radiance images, where a pixel value corresponds to the radiance from some point in the scene in the direction of the camera.. Other modalities. X-rays, MRI…. Raanan. . Fattal. . ACM . Siggraph. 2007. Presenter: . 이성호. Previous. . work. Classical approach. Nearest-Neighbor, Bilinear, . Bicubic. , . Hann. , Hamming, and . Lanczos. interpolation kernels.. Edges = jumps in brightness/color. Brightness jumps marked in white. Edges. Edges = jumps in brightness/color. Important!. Give object outlines and . shapes. Brightness jumps marked in white. Edges. Edges = jumps in brightness/color. geobodies. Adam Halpert. ExxonMobil CEES Visit. 12 November 2010. S. tanford. . E. xploration. . P. roject. Why automate?. Save time. Manual salt-picking is tedious, time-consuming. Major bottleneck for iterative imaging/model-building. Ming-Ming Cheng . . Ziming. Zhang . . Wen-Yan Lin . . Philip . Torr. The University of Oxford . . Boston University . . Brookes Vision Group. 2014 IEEE Conference on Computer Vision and Pattern Recognition. Mahalanobis. distance. MASTERS THESIS. By: . Rahul. Suresh. COMMITTEE MEMBERS. Dr.Stan. . Birchfield. Dr.Adam. Hoover. Dr.Brian. Dean. Introduction. Related work. Background theory: . Image as a graph. Gradients. GUIs. 2. Mac 1984. Windows 3 1990. GUIs. 3. Gradients. Gradient: vary color from one to another interpolating in between. Current trend (~2010) is to use gradients everywhere in interfaces and graphic design. TonyChanandLuminitaVese Tony Basic idea in classical active contours Area(inside(C)) Boundary detectionstopping edge-function (external forces) 0 ) ( lim 0 t g g g Example: , , 0 t g g g t p G ug | | Presented at:. International . Conference on Biomedical . Engineering (ICBME) 2013. by . R. Srivastava. 1. , X. Gao. 1. , F. Yin. 1. , D. Wong. 1. , J. Liu. 1. ,. C.Y. Cheung. 2. , T.Y. Wong. 2. Institute for Infocomm Research, Singapore. Applications. 报告人：程明明. 南开大学、计算机与控制工程学. 院. http://mmcheng.net/. Contents. Global . contrast based salient region . detection. ,. PAMI 2014. BING: Binarized Normed Gradients for Objectness Estimation at .