• Title/Summary/Keyword: Video Segmentation

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A Face Segmentation Algorithm Using Window (윈도우를 사용한 얼굴영역의 추출 기법)

  • 임성현;이철희
    • Proceedings of the IEEK Conference
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    • 2000.11d
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    • pp.45-48
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    • 2000
  • In this paper, we propose a region-based segmentation algorithm to extract human face area using a window function and neural networks. Furthermore, we apply the erosion and dilation to remove small error areas. By applying the window function, it is possible to reduce error. In particular, false segmentation of the eye and the lip can be considerably reduced. Experiments show promising results and it is expected that the Proposed method can be applied to video conference and still image compression.

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Local Watershed and Region Merging Algorithm for Object Segmentation (객체분할을 위한 국부적 워터쉐드와 영역병합 알고리즘)

  • Yu, Hong-Yeon;Hong, Sung-Hoon
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.299-300
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    • 2006
  • In this paper, we propose a segmentation algorithm which combines the ideas from local watershed transforms and the region merging algorithm based hierarchical queue. Only the process of watershed and region merging algorithm can be restricted area. A fast region merging approach is proposed to extract the video object from the regions of watershed segmentation. Results show the effectiveness and convenience of the approach.

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Pig Segmentation using Concave-Points and Edge Information (오목점과 에지 정보를 이용한 돼지의 경계 구분)

  • Baek, Hansol;Chung, Yeonwoo;Ju, Miso;Chung, Yongwha;Park, Daihee;Kim, Hakjae
    • Journal of Korea Multimedia Society
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    • v.19 no.8
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    • pp.1361-1370
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    • 2016
  • To reduce huge losses in pig farms, weaning pigs with weak immune systems are required to be carefully supervised. Even if various researches have been performed for pig monitoring environment, segmenting each pig from touching-pigs is still entrenched as a difficult problem. In this paper, we propose a segmentation method for touching-pigs by using concave-points and edge information in a video surveillance system. Especially, we interpret the segmentation problem as a time-series analysis problem in order to identify the concave-points generated by touching-pigs. Based on the experimental results with the videos obtained from a domestic pig farm, we believe that the proposed method can accurately segment the touching-pigs.

Desktop program production

  • Enami, Kazumasa;Fukui, Kazuo;Yagi, Nobuyuki
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 1996.06b
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    • pp.77-81
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    • 1996
  • In order to conform to the needs of effective program production in multimedia era, we are studying Desk Top Program Production system. With the DTPP, users can easily produce multimedia program including video, sound, and ancillary data, and freely handle video images synthesizing video components retrieved from video database. This paper describes the new program production system, DTPP and its key technologies such as cooperative program production via multimedia network, indexing and utilization of attribute information of images, and image segmentation and spatio-temporal editing.

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Detecting Gradual Transitions in Video Sequences (비디오 영상에서 점진적 장면전환 검출)

  • 이광국;김형준;김회율
    • Proceedings of the IEEK Conference
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    • 2002.06d
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    • pp.149-152
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    • 2002
  • Automated video segmentation is important as the first step of video indexing, video retrieval and other uses. Unlike abrupt changes that are relatively easy to detect, gradual transitions like dissolve, fade-in and fade-out are rather difficult to detect. In this paper, we propose a method for detecting gradual transitions based on local statistics and less dependent to a given threshold level. Experimental results show that the proposed method detected about 85% of gradual transitions.

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Automatic Extraction of Focused Video Object from Low Depth-of-Field Image Sequences (낮은 피사계 심도의 동영상에서 포커스 된 비디오 객체의 자동 검출)

  • Park, Jung-Woo;Kim, Chang-Ick
    • Journal of KIISE:Software and Applications
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    • v.33 no.10
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    • pp.851-861
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    • 2006
  • The paper proposes a novel unsupervised video object segmentation algorithm for image sequences with low depth-of-field (DOF), which is a popular photographic technique enabling to represent the intention of photographer by giving a clear focus only on an object-of-interest (OOI). The proposed algorithm largely consists of two modules. The first module automatically extracts OOIs from the first frame by separating sharply focused OOIs from other out-of-focused foreground or background objects. The second module tracks OOIs for the rest of the video sequence, aimed at running the system in real-time, or at least, semi-real-time. The experimental results indicate that the proposed algorithm provides an effective tool, which can be a basis of applications, such as video analysis for virtual reality, immersive video system, photo-realistic video scene generation and video indexing systems.

Color Recognition and Phoneme Pattern Segmentation of Hangeul Using Augmented Reality (증강현실을 이용한 한글의 색상 인식과 자소 패턴 분리)

  • Shin, Seong-Yoon;Choi, Byung-Seok;Rhee, Yang-Won
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.6
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    • pp.29-35
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    • 2010
  • While diversification of the use of video in the prevalence of cheap video equipment, augmented reality can print additional real-world images and video image. Although many recent advent augmented reality techniques, currently attempting to correct the character recognition is performed. In this paper characters marked with a visual marker recognition, and the color to match the marker color of the characters finds. And, it was shown on the screen by the character recognition. In this paper, by applying the phoneme pattern segmentation algorithm by the horizontal projection, we propose to segment the phoneme to match the six types of Hangul representation. Throughout the experiment sample of phoneme segmentation using augmented reality showed proceeding result at each step, and the experimental results was found to be that detection rate was above 90%.

Efficient Memory Update Module for Video Object Segmentation (동영상 물체 분할을 위한 효율적인 메모리 업데이트 모듈)

  • Jo, Junho;Cho, Nam Ik
    • Journal of Broadcast Engineering
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    • v.27 no.4
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    • pp.561-568
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    • 2022
  • Most deep learning-based video object segmentation methods perform the segmentation with past prediction information stored in external memory. In general, the more past information is stored in the memory, the better results can be obtained by accumulating evidence for various changes in the objects of interest. However, all information cannot be stored in the memory due to hardware limitations, resulting in performance degradation. In this paper, we propose a method of storing new information in the external memory without additional memory allocation. Specifically, after calculating the attention score between the existing memory and the information to be newly stored, new information is added to the corresponding memory according to each score. In this way, the method works robustly because the attention mechanism reflects the object changes well without using additional memory. In addition, the update rate is adaptively determined according to the accumulated number of matches in the memory so that the frequently updated samples store more information to maintain reliable information.

Region-Based Moving Object Segmentation for Video Monitoring System (비디오 감시시스템을 위한 영역 기반의 움직이는 물체 분할)

  • 이경미;김종배;이창우;김항준
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.40 no.1
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    • pp.30-38
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    • 2003
  • This paper presents an efficient region-based motion segmentation method for segmenting of moving objects in a traffic scene with a focus on a Video Monitoring System (VMS). The presented method consists of two phases: motion detection and motion segmentation. Using the adaptive thresholding technique, the differences between two consecutive frames are analyzed to detect the movements of objects in a scene. To segment the detected regions into meaningful objects which have the similar intensity and motion information, the regions are initially segmented using a k-means clustering algorithm and then, the neighboring regions with the similar motion information are merged. Since we deal with not the whole image, but the detected regions in the segmentation phase, the computational cost is reduced dramatically. Experimental results demonstrate robustness in the occlusions among multiple moving objects and the change in environmental conditions as well.

Automatic Indexing for the Content-based Retrieval of News Video (뉴스 비디오의 내용기반 검색을 위한 자동 인덱싱)

  • Yang, Myung-Sup;Yoo, Cheol-Jung;Chang, Ok-Bae
    • The Transactions of the Korea Information Processing Society
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    • v.5 no.5
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    • pp.1130-1139
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    • 1998
  • This paper presents an integrated solution for the content-based news video indexing and the retrieval. Currently, it is impossible to automatically index a general video, but we can index a specific structural video such as news videos. Our proposed model extracts automatically the key frames by using the structured knowledge of news and consists of the news item segmentation, caption recognition and search browser modules. We present above three modules in the following: the news event segmentation module recognizes an anchor-person shot based on face recognition, and then its news event are divided by the anchor-person's frame information. The caption recognition module detects the caption-frames with the caption characteristics, extracts their character region by the using split-merge method, and then recognizes characters with OCR software. Finally, the search browser module could make a various of searching mechanism possible.

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