PPT-Classification, Detection and Segmentation

of Deformable Animals in Images Advisers Prof CV Jawahar Prof A PZisserman 3 rd August 2011 Omkar M Parkhi 200807012 Object Category Recognition Popular in the community since long time. Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Jitandra. Malik (UC Berkeley). Presenter: . Hossein. . Azizpour. Abstract. Can CNN improve . s.o.a. . object detection results?. Yes, it helps by learning rich representations which can then be combined with computer vision techniques.. Contents. Overview of IDS/IPS. Components of an IDS/IPS. IDS/IPS classification. By scope of protection. By detection model. 2. /37. Intrusion. A set of actions aimed at compromising the security goals (confidentiality, integrity, availability of a computing/networking resource). Contents. Overview of IDS/IPS. Components of an IDS/IPS. IDS/IPS classification. By scope of protection. By detection model. 2. /37. Intrusion. A set of actions aimed at compromising the security goals (confidentiality, integrity, availability of a computing/networking resource). Anurag Arnab. Collaborators: . sadeep. . Jayasumana. , . shuai. . zheng. , Philip . torr. Introduction. Semantic Segmentation. Labelling every pixel in an image. A key part of Scene Understanding. Initial Findings. Introduction. Traffic State Detection Using Acoustics. Future Work. • . Early Detection. : . Minimum amount of recording needed by the classifier to. . correctly identify a scene.. 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. Yassine Benajiba. 1. and . Imed. Zitouni. 2. 1 CCLS, Columbia University. 2 IBM T.J. Watson Research Center. ybenajiba@ccls.columbia.edu. , . izitouni@us.ibm.com. . Outline. The Arabic Language. ATB vs. Morph segmentation. Bharath. . Hariharan. , Pablo . Arbeláez. , . Ross . Girshick. and Jitendra Malik. UC . Berkeley. What is image understanding?. person 1. person 2. horse 1. horse 2. Object Detection. Detect every instance of the category and localize it with a bounding box.. 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. Friedrich . Müller. , Reiner . Creutzburg. Abstract:. OCT (Optical coherence tomography) has become a popular method for macular degeneration diagnosis. The advantages over other methods are: OCT is . Azmi Haider. Loay Mualem. Hyperspectral Imaging seminar - Prof. Hagit Hel-Or. Content. Introduction. What is segmentation?. What’s the goal?. Two segmentation methods:. Watershed segmentation. Minimum spanning forest segmentation. dermoscopic. images is crucial and results in an increase in the survival rate. The clinical ABCD (asymmetry, border irregularity, color variation and diameter greater than 6mm) rule is one of the most widely used methods for early melanoma recognition. However, accurate classification of melanoma is still extremely difficult due to following reasons(not limited to): great visual resemblance between melanoma and non-melanoma skin lesions, less contrast difference between skin and the lesions etc. There is an ever-growing need of correct and reliable detection of skin cancers. Advances in the field of deep learning deems it perfect for the task of automatic detection and is very useful to pathologists as they aid them in terms of efficiency and accuracy. . 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.. Source:. TG-Endoscopy Topic Driver. Title:. Att.3 – Presentation (TG-Endoscopy). Purpose:. Discussion. Contact:. Jianrong Wu . Tencent Healthcare, China. E-mail: . edwinjrwu@tencent.com. Abstract:.