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 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. 2015. Your . Defining . Moment . 2015. Defining Moment. Luke19. 41 . But . as he came closer to Jerusalem and saw the city ahead, he began to . weep. . . 42. . “How . I wish today that you of all people would understand the way to peace. But now it is too late, and peace is hidden from your . 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 . I am 12.. 12 is a . decimal . number. Use this table to convert a decimal number into a binary number.. To make 12 I need to add. 8. and 4.. Put a 1 under these numbers.. Put a 0 under the numbers that are not needed. . DUI and Other Treatment Dockets:. “Best Practices, . Best Resu. lts. ”. Terrence D. Walton, MSW, CSAC. Chief . Operating Officer. . National Association of Drug Court Professionals. Key Moments in NADCP History. What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. 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. Serge . Bolongie. , . Jitendra. Malik, Jan . Puzicha. Presenter : . Neha. . Raste. . 1. Outline. Introduction. Background. Algorithm. Explanation. Results and Discussion. 2. Introduction . Shape Context . 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. 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 .