• 제목/요약/키워드: Foreground modeling unit

검색결과 2건 처리시간 0.12초

Maritime Object Segmentation and Tracking by using Radar and Visual Camera Integration

  • Hwang, Jae-Jeong;Cho, Sang-Gyu;Lee, Jung-Sik;Park, Sang-Hyon
    • Journal of information and communication convergence engineering
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    • 제8권4호
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    • pp.466-471
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    • 2010
  • We have proposed a method to detect and track moving ships using position from Radar and image processor. Real-time segmentation of moving regions in image sequences is a fundamental step in the radar-camera integrated system. Algorithms for segmentation of objects are implemented by composing of background subtraction, morphologic operation, connected components labeling, region growing, and minimum enclosing rectangle. Once the moving objects are detected, tracking is only performed upon pixels labeled as foreground with reduced additional computational burdens.

Salient Object Detection via Adaptive Region Merging

  • Zhou, Jingbo;Zhai, Jiyou;Ren, Yongfeng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권9호
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    • pp.4386-4404
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    • 2016
  • Most existing salient object detection algorithms commonly employed segmentation techniques to eliminate background noise and reduce computation by treating each segment as a processing unit. However, individual small segments provide little information about global contents. Such schemes have limited capability on modeling global perceptual phenomena. In this paper, a novel salient object detection algorithm is proposed based on region merging. An adaptive-based merging scheme is developed to reassemble regions based on their color dissimilarities. The merging strategy can be described as that a region R is merged with its adjacent region Q if Q has the lowest dissimilarity with Q among all Q's adjacent regions. To guide the merging process, superpixels that located at the boundary of the image are treated as the seeds. However, it is possible for a boundary in the input image to be occupied by the foreground object. To avoid this case, we optimize the boundary influences by locating and eliminating erroneous boundaries before the region merging. We show that even though three simple region saliency measurements are adopted for each region, encouraging performance can be obtained. Experiments on four benchmark datasets including MSRA-B, SOD, SED and iCoSeg show the proposed method results in uniform object enhancement and achieve state-of-the-art performance by comparing with nine existing methods.