Automatic segmentation of focused objects from images with low depth of field

Li, Zhi; Li, Weiwei; Shen, Liquan; Zhengkang, Han; Zhang, Zhaoyang

doi:10.1016/j.patrec.2009.11.016

Cited by 28 publications

(10 citation statements)

References 12 publications

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“…The proposed algorithm was implemented in MATLAB 2008b on a computer with 2.00 GHZ CPU and 2 GB RAM. The performance index for evaluation is chosen as the segmentation error rate (SER) [18,19], defined as…”

Section: Resultsmentioning

confidence: 99%

A novel active contour model for unsupervised low-key image segmentation

Mei

Si²,

Karimi

et al. 2013

Open Engineering

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Unsupervised image segmentation is greatly useful in many vision-based applications. In this paper, we aim at the unsupervised low-key image segmentation. In low-key images, dark tone dominates the background, and gray level distribution of the foreground is heterogeneous. They widely exist in the areas of space exploration, machine vision, medical imaging, etc. In our algorithm, a novel active contour model with the probability density function of gamma distribution is proposed. The flexible gamma distribution gives a better description for both of the foreground and background in low-key images. Besides, an unsupervised curve initialization method is designed, which helps to accelerate the convergence speed of curve evolution. The experimental results demonstrate the effectiveness of the proposed algorithm through comparison with the CV model. Also, one real-world application based on our approach is described in this paper.

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Section: Resultsmentioning

confidence: 99%

A novel active contour model for unsupervised low-key image segmentation

Mei

Si²,

Karimi

et al. 2013

Open Engineering

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show abstract

“…In order to quantitatively evaluate the target extraction performance, we utilize the pixel-based quality measure 19 to calculate the extraction error rate, as follows:…”

Section: Resultsmentioning

confidence: 99%

Extracting targets from regions-of-interest in infrared images using a 2-D histogram

et al. 2011

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We propose an effective method of extracting targets from a region-of-interest (ROI) in infrared images by using a 2-D histogram, considering intensity values and distance values from a center of the ROI. Existing approaches for extracting targets have utilized only intensity values of pixels or an analysis of a 1-D histogram of intensity values. Because the 1-D histogram has mixed bins containing false-negative bins from the target region as well as false-positive bins from the background region, it is difficult to extract target regions effectively due to the mixed bins. In order to solve the problem of the mixed bins, we propose a novel 2-D histogrambased approach for extracting targets. Experimental results have shown that the proposed method achieves better performance of extracting targets than existing methods under various environments, such as target regions with irregular intensities, dim targets, and cluttered backgrounds. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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“…8, a deep processing is taken from the cloth, the posture and the background. In order to measure the result of the experiment, we use the method of segmenting error rate [19] through (13) …”

Section: Experiments Testmentioning

confidence: 99%

A dynamic portrait segmentation by merging colors and depth information

Juang

Tsou

2015

Int. J. Control Autom. Syst.

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In this paper, we proposed a new method to improve the dynamic portrait segmentation in Kinect which always causes the problem of incomplete image segmentation of portrait due to the loss of the depth. This problem can be solved by using the color information to reinforce the areas where the depth is uncertain. We can segment the portrait's foreground more completely using the proposed method. First, the depth information can be divided into foreground, background, uncertain areas to produce a judging area for the foreground's uncertain areas. Secondly, the volunteer image will be segmented by Sobel edge detection, watershed and other steps in color information then be treated as the characteristic value of color area to calculate the mean value and standard deviation respectively. Finally, we chose the best image from these processing by comparing the color feature of the foreground edge and the judging area. The results show that we can completely segment out the portrait image as well as reduce its error rate significantly.

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Automatic segmentation of focused objects from images with low depth of field

Cited by 28 publications

References 12 publications

A novel active contour model for unsupervised low-key image segmentation

A novel active contour model for unsupervised low-key image segmentation

Extracting targets from regions-of-interest in infrared images using a 2-D histogram

A dynamic portrait segmentation by merging colors and depth information

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