PPT-Binary Shape Clustering via Zernike Moments

By Stephen Yoo Michael Vorobyov Moments In general moments describe numeric quantities at some distance from a reference point or axis Regular Cartesian Moments A regular moment has the form of projection . unredu Abstract Zernike Moments are useful tools in pattern recognition and image analysis due to their orthogonality and rotation invariance prop erty However direct computation of these moments is very expensive limiting their use especially at hig Tianqiang. 04/01/2014. Image/video understanding. Content creation. Why do we need 3D shapes?. Image/video understanding. [Chen et al. 2010]. Why do we need 3D shapes?. Image/video understanding. [Xiang et al. 2014 (ECCV)]. The Cartesian ellipsoid . produces a stigmatic image of only one object point. Normal eye and most of optical systems are not free from aberration. . . Reference sphere:. . a circular arc centered on the image point with a radius equal to the image distance. Interactive segmentation feedback model. Slicer visualization and . fiducials. enable effective expert input. Figure: Progressive interactive. segmentation of bone fracture.. Figure: Percentage of . Lena Gorelick. Joint Work with Yuri . Boykov. and Frank Schmidt. December 2012. 1. Medical Imaging . Retresat. , BIRC 2012, London . Standard Energy for . Binary Segmentation. 2. Medical Imaging Retresat, BIRC 2012, London . Alignment at 3000 FPS via . Regressing Local Binary Features. Shaoqing Ren, Xudong Cao,. Yichen Wei, and Jian Sun. Visual Computing Group. Microsoft Research Asia. What is Face Alignment?. Find face shape S, or semantic facial points. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. By:. Michael Vorobyov. Moments. In . general, moments are quantitative values that describe a distribution by raising the components to different powers. Regular (Cartesian) Moments. A regular moment . Serge . Bolongie. , . Jitendra. Malik, Jan . Puzicha. Presenter : . Neha. . Raste. . 1. Outline. Introduction. Background. Algorithm. Explanation. Results and Discussion. 2. Introduction . Shape Context . By:. Michael Vorobyov. Moments. In . general, moments are quantitative values that describe a distribution by raising the components to different powers. Regular (Cartesian) Moments. A regular moment . Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Using CGH for Testing Aspheric Surfaces Nasrin Ghanbari OPTI 521 Introduction Spherical wavefront from interferometer is incident on CGH Reflected light will have an aspheric phase function CGH cancels the aspheric phase Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because .