Saliency‐based localising active contour for automatic natural object segmentation

Gao, Shangbing; Yang, Jian; Yan, Yunyang; Bo, Zhou

doi:10.1049/iet-ipr.2013.0070

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…ACM is one of the popular models for image segmentation. There are several advantages of ACM over classical image segmentation methods [3]. First, ACM can obtain subpixel accuracy of object boundaries [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…There are several advantages of ACM over classical image segmentation methods [3]. First, ACM can obtain subpixel accuracy of object boundaries [3,4]. Second, this model can be developed within the framework of the energy minimization principle.…”

Section: Introductionmentioning

confidence: 99%

“…Second, this model can be developed within the framework of the energy minimization principle. Third, they can get smooth and closed results and can be used for further applications, such as feature extraction and shape analysis [3].…”

Section: Introductionmentioning

confidence: 99%

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Lip segmentation using automatic selected initial contours based on localized active contour model

Liu

2018

J Image Video Proc.

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With the rapid development of artificial intelligence and the increasing popularity of smart devices, human-computer interaction technology has become a multimedia and multimode technology from being computer-focused to people-centered. Among all ways of human-computer interactions, using language to interact with machines is the most convenient and efficient one. However, the performance of audio speech recognition systems is not satisfied in a noisy environment. Thus, more and more researchers focus their works on visual lip reading technology. By extracting lip movement features of speakers rather than audio features, visual lip reading systems can get superior results when noises and interferences exist. Lip segmentation plays an important role in a visual lip reading system, since the segmentation result is crucial to the final recognition accuracy. In this paper, we propose a localized active contour model-based method using two initial contours in a combined color space. We apply illumination equalization to original RGB images to decrease the interference of uneven illumination. A combined color space consists of the U component in CIE-LUV color space and the sum of C2 and C3 components of the image after discrete Hartley transform. We select a rhombus as the initial contour of a closed mouth, because it has a similar shape to a closed lip. For an open mouth, we utilize a combined semi-ellipse as the initial contours of both outer and inner lip boundaries. After attaining the results of each color component separately, we merge them together to obtain the final segmentation result. From the experiment, we can conclude that this method can get better segmentation results compared with the method using a circle as the initial contour to segment gray images and images in combined color space, especially for open mouth. An extremely obvious advantage of this method is the results of open mouth excluding internal information of mouth such as teeth, black holes, and tongue, because of the introduction of the inner initial contour.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lip segmentation using automatic selected initial contours based on localized active contour model

Liu

2018

J Image Video Proc.

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“…Effective methods of the saliency detection can help people to reduce time and cost when handling images from large databases. As an important technique, extracting saliency map is widely used in image retrieval [1][2][3], image or video compression [4][5][6], object detection [7][8][9][10][11], image and video segmentation [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Moving visual focus in salient object segmentation

Chen

Liu

Xiao

et al. 2015

IET Image Processing

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Saliency detection plays an important role in image segmentation, object detection and retrieval, which attracts more attention in the field of computer vision recently. Most existing saliency detection algorithms have not considered the influence of visual focus shifting yet. In this paper, a novel algorithm named moving region contrast (MRC) is proposed to analyze image saliency. The algorithm MRC is built on a novel concept of moving visual focus (MVF). The initial visual focus is defined as the geometric center of the image. Then the visual focus is calculated iteratively by focus-moving technique where a saliency gravitation model is employed to determine the moving direction. The salient region is obtained according to the final visual focus. The experiments are conducted on the dataset with 1000 images released by Achanta. Experimental results show that the proposed algorithm achieves marked improvements in performance and outperforms other 11 popular algorithms.

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“…The performance of active contour model is affected by initial curve setting, if the initial curve starts in almost the right place, not only the final results can be obtained, but also the time needed for segmentation is reduced drastically [15]. For instance, prior knowledge about objects generated by different saliency detection methods was integrated with a localizing region-based active contour model for traffic sign detection [16] and natural object segmentation [17]. Li and Acton [18] proposed a novel automatic initialization approach by the poisson inverse gradient method in both 2D and 3D images.…”

Section: Introductionmentioning

confidence: 99%