Video Segmentation Based on Motion Coherence of Particles in a Video Sequence

Silva, L.S.; Scharcanski, Jacob

doi:10.1109/tip.2009.2038778

“…Luciano et al [94] performed an experiment on real and synthetic sequences in April 2010. In this paper they experimented with real and synthetic sequences suggest that our method also could be used in other image processing and computer vision tasks, besides video coding, such as video information retrieval and video understanding.…”

Section: Video Segmentationmentioning

confidence: 99%

Review of Graph, Medical and Color Image base Segmentation Techniques

Baldevbhai¹

2012

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This literature review attempts to provide a brief overview of some of the most common segmentation techniques, and a comparison between them.It discusses Graph based methods, Medical image segmentation research papers and Color Image based Segmentation Techniques. With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques starting from graph based approach to color image segmentation and medical image segmentation, which covers the application of both techniques, are reviewed.Information about open source software packages for image segmentation and standard databases are provided. Finally, summaries and review of research work for image segmentation techniques along with quantitative comparisons for assessing the segmentation results with different parameters are represented in tabular format, which are the extracts of many research papers.

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“…Video Segmentation: Luciano et al [94] performed an experiment on real and synthetic sequences in April 2010. In this paper they experimented with real and synthetic sequences suggest that our method also could be used in other image processing and computer vision tasks, besides video coding, such as video information retrieval and video understanding.…”

Section: 3mentioning

confidence: 99%

Untitled

2012

IOSRJEEE

0

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This literature review attempts to provide a brief overview of some of the most common segmentation techniques, and a comparison between them.It discusses Graph based methods, Medical image segmentation research papers and Color Image based Segmentation Techniques. With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques starting from graph based approach to color image segmentation and medical image segmentation, which covers the application of both techniques, are reviewed.Information about open source software packages for image segmentation and standard databases are provided. Finally, summaries and review of research work for image segmentation techniques along with quantitative comparisons for assessing the segmentation results with different parameters are represented in tabular format, which are the extracts of many research papers.

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“…appearance or motions from various videos are much different, while possibly low contrast with the background. These challenges cause great difficulties on existing video segmentation techniques [3], [11], [12], [16], [19], [33], which usually benefit from visual cues such as motion or appearance. Additionally, these methods rely on the assumption that the motion or appearance of object is dramatically distinct from background, which is against the situation as we mentioned before.…”

mentioning

confidence: 99%

Robust Video Object Cosegmentation

Yang

¹

,

Shen

²

,

Li

³

et al. 2015

IEEE Trans. on Image Process.

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With ever-increasing volumes of video data, automatic extraction of salient object regions became even more significant for visual analytic solutions. This surge has also opened up opportunities for taking advantage of collective cues encapsulated in multiple videos in a cooperative manner. However, it also brings up major challenges, such as handling of drastic appearance, motion pattern, and pose variations, of foreground objects as well as indiscriminate backgrounds. Here, we present a cosegmentation framework to discover and segment out common object regions across multiple frames and multiple videos in a joint fashion. We incorporate three types of cues, i.e., intraframe saliency, interframe consistency, and across-video similarity into an energy optimization framework that does not make restrictive assumptions on foreground appearance and motion model, and does not require objects to be visible in all frames. We also introduce a spatio-temporal scale-invariant feature transform (SIFT) flow descriptor to integrate across-video correspondence from the conventional SIFT-flow into interframe motion flow from optical flow. This novel spatio-temporal SIFT flow generates reliable estimations of common foregrounds over the entire video data set. Experimental results show that our method outperforms the state-of-the-art on a new extensive data set (ViCoSeg).

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Video Segmentation Based on Motion Coherence of Particles in a Video Sequence

Cited by 20 publications

References 39 publications

Review of Graph, Medical and Color Image base Segmentation Techniques

Review of Graph, Medical and Color Image base Segmentation Techniques

Untitled

Robust Video Object Cosegmentation

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