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Dan DeBlasio

This document outlines the recent expansion of the video class, enhancing its functionality for image processing techniques. Key features include canny edge detection, image inversion, and combining frames for complex video analysis. Additionally, it presents ideas on unsupervised learning through object feature extraction from video sequences using the SIFT algorithm. This method addresses challenges such as clutter, pose changes, occlusions, and multiple objects, fostering advancements in video processing and computer vision.

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Dan DeBlasio

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  1. Dan DeBlasio Week 3 18 May 2006

  2. Expansion of Video Class • End of last week, weekend • Extends most useful functions of image class • Few small errors in little things

  3. public static void main(String[] args){ video vid = new video(args[0],Integer.parseInt(args[1]),Integer.parseInt(args[2])); video c = vid.canny(Double.parseDouble(args[3]),Double.parseDouble(args[4])); c.save("sample/tempc"); c = vid.invert().skel(Integer.parseInt(args[5])); c.save("sample/temps"); c = vid.invert().lineOrient(Integer.parseInt(args[6])); c.save("sample/templ"); c.combineToRight("sample/tempsl",new video("sample/temps",0,45)); vid.combineToRight("sample/tempvc",new video("sample/tempc",0,45)); new video("sample/tempvc",0,45).combineToBottom(args[7],new video("sample/tempsl",0,45)); }

  4. Old Topic Ideas • Mosaicing • Super-Resolution • SIFT operated on either • combination

  5. Old Topic Ideas (cont.)

  6. Major Topic Idea Concentration • “Unsupervised Learning of Object Features from Video Sequences” • Using SIFT algorithm • Applies to multiple frames in a video • Identifies objects that are common thru-out • Deals with problems of • Clutter • Change in Pose • Multiple Objects • Occlusion

  7. Major Topic Concentration

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