Practical Uses of A Semi-automatic Video Object Extraction System

Object-based technology is important for computer vision applications including gesture understanding, image recognition, augmented reality, etc. However, extracting the shape information of semantic objects from video sequences is a very difficult task, since this information is not explicitly provided within the video data. Therefore, an application for exttracting the semantic video object is indispensable and important for many advanced applications. An algorithm for semi-automatic video object extraction system has been developed. The performance measures of video object extraction system; including evaluation using ground truth and error metric is shown, followed by some practical uses of our video object extraction system. The principle at the basis of semi-automatic object extraction technique is the interaction of the user during some stages of the segmentation process, whereby the semantic information is provided directly by the user. After the user provides the initial segmentation of the semantic video objects, a tracking mechanism follows its temporal transformation in the subsequent frames, thus propagating the semantic information. Since the tracking tends to introduce boundary errors, the semantic information can be refreshed by the user at certain key frame locations in the video sequence. The tracking mechanism can also operate in forward or backward direction of the video sequence. The performance analysis of the results is described using single and multiple key frames; Mean Error and “Last_Error”, and also forward and backward extraction. To achieve best performance, results from forward and backward extraction can be merged

Inter-level Spatial Cloud Compression Algorithm

Static images of colour clouds play an important role to predict weather conditions to schedule proof and trial activities, and deploying resources at firing locations and observation posts. In this paper, a new inter-level cloud compression architecture and algorithm has been presented. Distributed architecture suitable for cloud computing has been suggested to implement inter-level compression algorithm (ILCA). Different possibilities between two successive cloud images have been combined to save the images of interest for further processing, ignoring the redundant ones. Vector quantisation technique is briefly discussed to achieve high level of compression. The algorithm presented in this paper can be easily modified to store flying, floating, and moving objects in air, water, and surface, respectively with high level of compression in various defence applications.Science Journal, Vol. 64, No. 6, November 2014, pp.536-541, DOI:http://dx.doi.org/10.14429/dsj.64.811

A GENERIC PROCESS CHAIN TO EXTRACT KEY-OBJECTS FROM VIDEO SHOTS

ABSTRACT This paper discusses object-based representation of video shots acquired by a moving camera. Our approach uses an extraction of foreground regions capable of representing semantic objects of interest. However, foreground regions extracted by motion compensation are not always representative of the entity they depict. A filtering and a clustering of these regions allow us to retain only the most representative of each real object in the shot, i.e. the key-object

Video object segmentation.

Wei Wei.Thesis submitted in: December 2005.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 112-122).Abstracts in English and Chinese.Abstract --- p.IIList of Abbreviations --- p.IVChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview of Content-based Video Standard --- p.1Chapter 1.2 --- Video Object Segmentation --- p.4Chapter 1.2.1 --- Video Object Plane (VOP) --- p.4Chapter 1.2.2 --- Object Segmentation --- p.5Chapter 1.3 --- Problems of Video Object Segmentation --- p.6Chapter 1.4 --- Objective of the research work --- p.7Chapter 1.5 --- Organization of This Thesis --- p.8Chapter 1.6 --- Notes on Publication --- p.8Chapter Chapter 2 --- Literature Review --- p.10Chapter 2.1 --- What is segmentation? --- p.10Chapter 2.1.1 --- Manual Segmentation --- p.10Chapter 2.1.2 --- Automatic Segmentation --- p.11Chapter 2.1.3 --- Semi-automatic segmentation --- p.12Chapter 2.2 --- Segmentation Strategy --- p.14Chapter 2.3 --- Segmentation of Moving Objects --- p.17Chapter 2.3.1 --- Motion --- p.18Chapter 2.3.2 --- Motion Field Representation --- p.19Chapter 2.3.3 --- Video Object Segmentation --- p.25Chapter 2.4 --- Summary --- p.35Chapter Chapter 3 --- Automatic Video Object Segmentation Algorithm --- p.37Chapter 3.1 --- Spatial Segmentation --- p.38Chapter 3.1.1 --- k:-Medians Clustering Algorithm --- p.39Chapter 3.1.2 --- Cluster Number Estimation --- p.41Chapter 3.1.2 --- Region Merging --- p.46Chapter 3.2 --- Foreground Detection --- p.48Chapter 3.2.1 --- Global Motion Estimation --- p.49Chapter 3.2.2 --- Detection of Moving Objects --- p.50Chapter 3.3 --- Object Tracking and Extracting --- p.50Chapter 3.3.1 --- Binary Model Tracking --- p.51Chapter 3.3.1.2 --- Initial Model Extraction --- p.53Chapter 3.3.2 --- Region Descriptor Tracking --- p.59Chapter 3.4 --- Results and Discussions --- p.65Chapter 3.4.1 --- Objective Evaluation --- p.65Chapter 3.4.2 --- Subjective Evaluation --- p.66Chapter 3.5 --- Conclusion --- p.74Chapter Chapter 4 --- Disparity Estimation and its Application in Video Object Segmentation --- p.76Chapter 4.1 --- Disparity Estimation --- p.79Chapter 4.1.1. --- Seed Selection --- p.80Chapter 4.1.2. --- Edge-based Matching by Propagation --- p.82Chapter 4.2 --- Remedy Matching Sparseness by Interpolation --- p.84Chapter 4.2 --- Disparity Applications in Video Conference Segmentation --- p.92Chapter 4.3 --- Conclusion --- p.106Chapter Chapter 5 --- Conclusion and Future Work --- p.108Chapter 5.1 --- Conclusion and Contribution --- p.108Chapter 5.2 --- Future work --- p.109Reference --- p.11

Motion and emotion : Semantic knowledge for hollywood film indexing

Ph.DDOCTOR OF PHILOSOPH