Scene segmentation

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  • The field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the core of a modernization effort that resulted in the current text. This new edition of "Advanced Image Segmentation" is but a reflection of the significant progress that has been made in the field of image segmentation in just the past few years.

    pdf126p kimngan_1 05-11-2012 34 6   Download

  • This paper proposes a new indicator of text structure, called the lexical cohesion profile (LCP), which locates segment boundaries in a text. A text segment is a coherent scene; the words in a segment a~e linked together via lexical cohesion relations. LCP records mutual similarity of words in a sequence of text. The similarity of words, which represents their cohesiveness, is computed using a semantic network. Comparison with the text segments marked by a number of subjects shows that LCP closely correlates with the human judgments.

    pdf3p bunmoc_1 20-04-2013 18 2   Download

  • Computer vision is one of the most studied subjects of recent times with paramount focus on stereo vision. Lot of activities in the context of stereo vision are getting reported spanning over vast research spectrum including novel mathematical ideas, new theoretical aspects, state of the art techniques and diverse range of applications.

    pdf236p greengrass304 15-09-2012 29 4   Download

  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài:An Integrated Dynamic Scene Algorithm for Segmentation and Motion Estimation

    pdf9p dauphong20 11-03-2012 22 3   Download

  • 4. SEGMENTATION AND EDGE DETECTION 4.1 Region Operations Discovering regions can be a very simple exercise, as illustrated in 4.1.1. However, more often than not, regions are required that cover a substantial area of the scene rather than a small

    doc14p 0914811815 23-04-2011 74 9   Download

  • We propose a content based video retrieval system in some main steps resulting in a good performance. From a main video, we process extracting keyframes and principal objects using Segmentation of Aggregating Superpixels (SAS) algorithm. After that, Speeded Up Robust Features (SURF) are selected from those principal objects. Then, the model “Bag-of-words” in accompanied by SVM classification are applied to obtain the retrieval result. Our system is evaluated on over 300 videos in diversity from music, history, movie, sports, and natural scene to TV program show.

    pdf10p tieuthi3006 16-03-2018 7 1   Download


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