• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.1964-1981
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• 2014

An improved Mean-Shift (MS) tracker called joint CB-LBWH, which uses a combined weighted-histogram scheme of CBWH (Corrected Background-Weighted Histogram) and LBWH (likelihood-based Background-Weighted Histogram), is presented. Joint CB-LBWH is based on the notion that target representation employs both feature saliency and confidence to form a compound weighted histogram criterion. As the more prominent and confident features mean more significant for tracking the target, the tuned histogram by joint CB-LBWH can reduce the interference of background in target localization effectively. Comparative experimental results show that the proposed joint CB-LBWH scheme can significantly improve the efficiency and robustness of MS tracker when heavy occlusions and complex scenes exist.

• Transactions on Electrical and Electronic Materials
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• v.13 no.1
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• pp.10-15
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• 2012

In conventional, skin detection methods using for skin color definitions is based on prior knowledge. By experimentation, the threshold value for dividing the background from the skin region is determined subjectively. A drawback of such techniques is that their performance is dependent on a threshold value which is estimated from repeated experiments. To overcome this, the present paper introduces a skin region detection method. This method uses a histogram approximation based on the mean shift algorithm. This proposed method applies the mean shift procedure to a histogram of a skin map of the input image. It is generated by comparing with the standard skin colors in the $C_bC_r$ color space. It divides the background from the skin region by selecting the maximum value according to the brightness level. As the histogram has the form of a discontinuous function. It is accumulated according to the brightness values of the pixels. It is then, approximated by a Gaussian mixture model (GMM) using the Bezier curve technique. Thus, the proposed method detects the skin region using the mean shift procedure to determine a maximum value. Rather than using a manually selected threshold value, as in existing techniques this becomes the dividing point. Experiments confirm that the new procedure effectively detects the skin region.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.195-198
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• 2013

Recognition and Tracking of interesting object is the significant field in Computer Vision. Mean-Shift algorithm have chronic problems that some errors are occurred when histogram of tracking area is similar to another area. in this paper, we propose to solve the problem. Each algorithm blocks skin color filtering, face detect and Mean-Shift started consecutive order assists better operation of the next algorithm. Avoid to operations of the overhead of tracking area similar to a histogram distribution areas overlap only consider the number of white pixels by running the Viola-Jones algorithm, simple arithmetic increases the convergence of the Mean-Shift. The experimental results, it comes to 78% or more of white pixels in the Mean-Shift search area, only if the recognition of the face area when it is configured to perform a Viola-Jones algorithm is tracking the object, was 100 percent successful.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.329-341
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• 2014

Kernel-based mean-shift object tracking has gained more interests nowadays, with the aid of its feasibility of reliable real-time implementation of object tracking. This algorithm calculates the best mean-shift vector based on the color histogram similarity between target model and target candidate models, where the color histograms are usually produced after uniform color-space quantization for the implementation of real-time tracker. However, when the image of target model has a reduced contrast, such uniform quantization produces the histogram model having large values only for a few histogram bins, resulting in a reduced accuracy of similarity comparison. To solve this problem, a non-uniform quantization algorithm has been proposed, but it is hard to apply to real-time tracking applications due to its high complexity. Therefore, this paper proposes a fast non-uniform color-space quantization method using the histogram equalization, providing an adjusted histogram distribution such that the bins of target model histogram have as many meaningful values as possible. Using the proposed method, the number of bins involved in similarity comparison has been increased, resulting in an enhanced accuracy of the proposed mean-shift tracker. Simulations with various test videos demonstrate the proposed algorithm provides similar or better tracking results to the previous non-uniform quantization scheme with significantly reduced computation complexity.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.77-83
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• 2019

In this paper, we propose an object tracking algorithm based on color histogram and convolutional neural network model. In order to increase the tracking accuracy, we synthesize generic object tracking using regression network algorithm which is one of the convolutional neural network model-based tracking algorithms and a mean-shift tracking algorithm which is a color histogram-based algorithm. Both algorithms are classified through support vector machine and designed to select an algorithm with higher tracking accuracy. The mean-shift tracking algorithm tends to move the bounding box to a large range when the object tracking fails, thus we improve the accuracy by limiting the movement distance of the bounding box. Also, we improve the performance by initializing the tracking start positions of the two algorithms based on the average brightness and the histogram similarity. As a result, the overall accuracy of the proposed algorithm is 1.6% better than the existing generic object tracking using regression network algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.1-10
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• 2012

In this paper, we propose a new class of color histogram model suitable for object tracking. In addition to the pixel count, each bin of the proposed model also contains the spatial mean and the average value of the pixels located at a certain distance from the mean location of the bin. Using the proposed color histogram model, we derive a mean shift procedure using the modified Bhattacharyya distance. Unlike most mean shift based methods, our algorithm performs well even when the object being tracked shares similar colors with the background. Experimental results demonstrate improved tracking performance over existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1377-1389
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• 2016

This paper presents a target model update scheme for the mean-shift tracking with background weighted histogram. In the scheme, the target candidate histogram is corrected by considering the back-projection weight of each pixel in the kernel after the best target candidate in the current frame image is chosen. In each frame, the target model is updated by the weighted average of the current target model and the corrected target candidate. We compared our target model update scheme with the previous ones by applying several test sequences. The experimental results showed that the object tracking accuracy was greatly improved by using the proposed scheme.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2007.02a
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• pp.127-133
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• 2007

We propose new reversible watermarking method for images. Being reversibility, original image and watermarked message should be recovered exactly. We propose different technique for hiding data to pairs. We use new type of histogram (pair histogram), which shows frequencies of each pair in image. We use histogram shift method for data embedding to pairs. We also propose improved version of method which allow hiding data with good performance for high capacities. This algorithm has better result compare to Tian's difference expansion method based on the Haar wavelet decomposition. For proposed algorithm capacity is higher under same PSNR.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.667-673
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• 2009

This paper presents an efficient facial expression recognition method by measuring the histogram distance based on preprocessing. The preprocessing that uses both centroid shift and histogram equalization is applied to improve the recognition performance, The distance measurement is also applied to estimate the similarity between the facial expressions. The centroid shift based on the first moment balance technique is applied not only to obtain the robust recognition with respect to position or size variations but also to reduce the distance measurement load by excluding the background in the recognition. Histogram equalization is used for robustly recognizing the poor contrast of the images due to light intensity. The proposed method has been applied for recognizing 72 facial expression images(4 persons * 18 scenes) of 320*243 pixels. Three distances such as city-block, Euclidean, and ordinal are used as a similarity measure between histograms. The experimental results show that the proposed method has superior recognition performances compared with the method without preprocessing. The ordinal distance shows superior recognition performances over city-block and Euclidean distances, respectively.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.8-16
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• 2012

An image obtained from a low light environment results in a low-exposure problem caused by non-ideal camera settings, i.e. aperture size and shutter speed. Of particular note, the multiple color-filter aperture (MCA) system inherently suffers from low-exposure problems and performance degradation in its image classification and registration processes due to its finite size of the apertures. In this context, this paper presents a novel method for the color enhancement of low-exposure images and its application to color shift model-based MCA system for image refocusing. Although various histogram equalization (HE) approaches have been proposed, they tend to distort the color information of the processed image due to the range limits of the histogram. The proposed color enhancement algorithm enhances the global brightness by analyzing the basic cause of the low-exposure phenomenon, and then compensates for the contrast degradation artifacts by using an adaptive histogram specification. We also apply the proposed algorithm to the preprocessing step of the refocusing technique in the MCA system to enhance the color image. The experimental results confirm that the proposed method can enhance the contrast of any low-exposure color image acquired by a conventional camera, and is suitable for commercial low-cost, high-quality imaging devices, such as consumer-grade camcorders, real-time 3D reconstruction systems, digital, and computational cameras.