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Review and Summary

Review and Summary. We have covered a LOT of material, spending more time and more detail on 2D image segmentation and analysis, but hopefully giving you a feel for 3D, too. Binary Images and Classification (Chs. 3-4) Color, Texture, and Segmentation (Chs. 6, 7, 10)

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Review and Summary

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  1. Review and Summary We have covered a LOT of material, spending more time and more detail on 2D image segmentation and analysis, but hopefully giving you a feel for 3D, too. • Binary Images and Classification (Chs. 3-4) • Color, Texture, and Segmentation (Chs. 6, 7, 10) • Content-based Image Retrieval (Ch 8) • Motion (Ch 9) • 3D Perception and Sensing (Chs. 12 and parts of 13) • Object Recognition (Chs. 11 and 14)

  2. Binary Images and Classification • Finding good thresholds • Connected components operator • Mathematical morphology • Region properties • Region adjacency graphs • Feature extraction • Feature vectors • Classifiers

  3. Color and Texture • Color spaces: RGB, HIS • Color histograms • Color image segmentation via clustering • Mathematical models of shading • Structural vs statistical approaches to texture • Common statistical measures • - Edge density and direction histograms • - Local binary pattern histogram • - Co-occurrence matrix features • - Laws texture energy measures • - Autocorrelation • Texture-based segmentation

  4. Segmentation • REGIONS • region growing using a statistical approach • clustering • - K-means and isodata • - Recursive histogram-based clustering • - Shi’s graph cut partitioning • LINES AND ARCS • Tracking • Hough transform • - line segments • - circular arcs • Burns line finder

  5. Content-Based Image Retrieval • Query by example • Image distance measures • - color • ° color histograms • ° gridded color • - texture • ° texture summary features • ° texture histograms • ° gridded texture • - shape • ° tangent angle histograms • ° boundary matching • - regions and spatial relationships • Object recognition • ° face detection • ° flesh detection

  6. Motion • Change detection • Optic Flow • - interest points • - regions • - Ming’s algorithm: local analysis plus global • optimization • Tracking • Application to MPEG video compression • Video segmentation and structuring (Daniel G. P.)

  7. 3D Perception and Sensing • Intrinsic image approach • Perspective imaging model • Depth from stereo with parallel camera axes • Finding correspondences • - correlation • - symbolic matching • - epipolar constraint • - ordering constraint • Camera model • Tsai’s camera calibration algorithm • General stereo • Pose estimation (not covered, but used in AR) • 3D object reconstruction from range data

  8. Object Recognition • Affine transformations • Alignment • - local-feature-focus • - pose clustering • - geometric hashing • - surface signatures • Relational matching / relational distance • - via tree search • - via discrete relaxation • - via probabilistic relaxation • Relational indexing • Deformable models • Functional models • Appearance-based recognition

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