PPT-Saliency detection with background model

Donghun Yeo CV Lab Contents Definition of Saliency Detection Saliency Detection via Dense and Sparse Reconstruction Saliency Detection via Absorbing Markov Chain Definition of Saliency Detection. we have evolved the process and methodology of leak detection and location into a science and can quickly and accurately locate leaks in homes, office buildings, swimming pools and space, as well as under streets and sidewalks, driveways, asphalt parking lots and even golf courses. The resulting saliency map in evitably loses information in the original image and 64257nding salient objects in it is dif64257cult We propose to detect salient objects by directly measuring the saliency of an image win dow in the original image and com Shuang Liang Tongji University shuangliangtongjieducn Yichen Wei Jian Sun Microsoft Research yichenw jiansun microsoftcom Abstract Recent progresses in salient object detection have ex ploited the boundary prior or background information to assis 02nT Faster cycle rates Up to 10Hz Longer range detection Pros brPage 5br Magnetometers Magnetometers Large distant targets mask small local targets Difficult to pick out small target due to background noise No sense of direction of target on single Kallol Dey. Rahul. . Mitra. Shubham. . Gautam. What is Spam ?. According to . wikipedia. … . Email spam, also known as junk email or unsolicited bulk email (UBE),is a subset of electronic spam involving nearly identical messages sent to numerous recipients by email. Clicking on links in spam email may send users to phishing web sites or sites that are hosting malware. . Presenter: Wei Wang. Institute of Digital Media, . PKU . Outline. Introduction to visual attention. The . computational . models of visual attention. The state-of-the-art models . of visual attention. Scene Analysis and . 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. Felix Lee. HPC I. 28.10.2013. Real-time Live Panoramic Video. OMNICAM. real-time acquisition. warping & stitching. blending and rendering of 6 HD video streams. one panoramic video of . 7k x 2k . Workshop on Performance Evaluation of Tracking Systems 2007, . held at the International Conference on Computer Vision 2007. Gerald Dalley, . Xiaogang. Wang, and W. Eric L. Grimson. Processing Pipeline. Peng. Wang. 1. . Jingdong. Wang. 2. Gang Zeng. 1. . Jie. Feng. 1. Hongbin. Zha. 1. . Shipeng. Li. 2. 1. Key Laboratory on Machine Perception, Peking University . 2. Microsoft Research Asia. Outline. 22 . Outubro. 2007. . Universidade. Federal do Paraná.. Gerald Dalley, . Xiaogang. Wang, and W. Eric L. Grimson. Glasgow Airport. 4 cameras. 9 video clips. 1 for training. 8 for testing. Dataset Description. : Advanced . Computer. Graphics. Perception. in 3D . Computer Graphics. Motivation. Çok. rendering . yavaş. . olur. ! (told by?). Take perception into account. Don’t waste resources. Render less. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . Face Detection. What kind of features?. using Channel Dependent Posteriors. Presented By:. Vinit Shah. Neural Engineering Data Consortium,. Temple University. 1. Abstract. An important factor of seizure detection problem, known as segmentation: defined as the ability to detect start and stop times within a fraction of a second, is a challenging and under-researched problem..