• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.958-964
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• 2010

Histogram Equalization(HE) is one of the well known methods for contrast enhancement. but, it did not applied directly due to side effects such as significant change in brightness or washed out appearance. Many conventional method try to overcome this problem but they did not guarantee various image or depend on user define parameter. In this paper, an Adaptive histogram Compensated Histogram Equalization(ACHE) is proposed for contrast enhancement. ACHE has a parameter that based on median of input image. Histogram of input image is compensated according to parameter. And then finally compensated histogram is equalized. Experimental results show that proposed method suppresses side effects such as detail loss or washed out appearance. Moreover, parameter calculated automatically with low computation complexity. As a result, it could applies FPD directly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2330-2334
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• 2009

This paper presented a new scene change detection method of compounding color histogram and $x^2$ histogram. This method overcomes the disadvantages of difference value detection methods and will be taking advantage. Also, this method can detect all from the abrupt scene change detection to gradual scene change detection. The proposed method has been compared with previous method, and our experimental results show the better results than the previous method.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.721-725
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• 2004

Multiple-count problem is occurred when rectangle objects span across several buckets. The Cumulative Density (CD) histogram is a technique which solves multiple-count problem by keeping four sub-histograms corresponding to the four points of rectangle. Although it provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors may be occurred when it is applied to real applications. In this paper, we proposed selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models: (1) probabilistic model which considers the query window area ratio, (2) probabilistic model which considers intersection area between a given grid and objects. In order to evaluate the proposed methods, we experimented with real dataset and experimental results showed that the proposed technique was superior to the existing selectivity estimation techniques. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.231-233
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• 2009

The object recognition is one of the major computer vision fields. The object recognition using features(SIFT) is finding common features in input images and query images. But the object recognition using feature methods has suffered of difficulties due to heavy calculations when resizing input images and query images. In this paper, we focused on speed up finding features in the images. we proposed method using block segmentation and histogram. Block segmentation used diving input image and than histogram decided correlation between each 1]lock and query image. This paper has confirmed that tile matching time reduced for object recognition since reducing block.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.917-918
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• 2008

Histogram Equalization (HE) method is widely used for contrast enhancement. However, HE often introduce washed out appearance or color distortion due to the over enhancement in contrast. In this paper, Dynamic Range Separate Histogram Equalization (DRSHE) is proposed for contrast enhancement. DRSHE reconfigures the dynamic range of histogram using probability distribution ratio. The experimental results show that DRSHE suppresses the washed out appearance or color distortion and preserves naturalness of the original image compared with conventional methods.

• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.1-6
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• 2002

In this study, we propose the new color-based image retrieval technique using the representative colors of images and their ratios to a total image size obtained through color segmentation in HSV color space. Color information of an image is described by reconstructing the color histogram of an image through Gaussian modelling to its representative colors and ratios. And the similarity between two images is measured by histogram intersection. The proposed method is compared with the existing methods by performing retrieval experiments for various 1280 trademark image database.

• MALSORI
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• no.60
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• pp.145-164
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• 2006

This paper proposes class histogram equalization (CHEQ) to compensate noisy acoustic features for robust speech recognition. CHEQ aims to compensate for the acoustic mismatch between training and test speech recognition environments as well as to reduce the limitations of the conventional histogram equalization (HEQ). In contrast to HEQ, CHEQ adopts multiple class-specific distribution functions for training and test environments and equalizes the features by using their class-specific training and test distributions. According to the class-information extraction methods, CHEQ is further classified into two forms such as hard-CHEQ based on vector quantization and soft-CHEQ using the Gaussian mixture model. Experiments on the Aurora 2 database confirmed the effectiveness of CHEQ by producing a relative word error reduction of 61.17% over the baseline met-cepstral features and that of 19.62% over the conventional HEQ.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3751-3770
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• 2015

Many sorts of image processing software facilitate image editing and also generate a great number of doctored images. Forensic technology emerges to detect the unintentional or malicious image operations. Most of forensic methods focus on the detection of single operations. However, a series of operations may be used to sequentially manipulate an image, which makes the operation detection problem complex. Forensic investigators always want to know as much exhaustive information about a suspicious image's entire processing history as possible. The detection of the operation chain, consisting of a series of operations, is a significant and challenging problem in the research field of forensics. In this paper, based on the histogram distribution uniformity of a manipulated image, we propose an operation chain detection scheme to identify histogram equalization (HE) followed by the dither-like operation (DLO). Two histogram features and a local spatial feature are utilized to further determine which DLO may have been applied. Both theoretical analysis and experimental results verify the effectiveness of our proposed scheme for both global and local scenarios.

• 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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• 2013.05a
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• pp.820-823
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• 2013

Recently, the need for improving image quality of the image is increasing in various fields smartphones, cameras, and portable devices. How a significant impact on improving image quality of the image is a contrast enhancement, as a representative method to improve the contrast, the process of histogram equalization, various studies have been made. However, the method of histogram equalization general, by readjusting the only brightness, when the image histogram is biased to one side, due to changes in the excess brightness, distortions such as blocking phenomenon occurs. In this paper, we provide a contrast enhancement techniques through the compression and re-distribution of a well-balanced average brightness of the histogram distribution. By be differential compression histogram based on the histogram frequency in order to suppress the supersaturation phenomenon due to the increase in contrast ratio excessive repositioning well-balanced histogram lopsided, the proposed method, the balance of the brightness of the image I want to to take. The experimental results, the image brightness is balanced manner compared to conventional methods, the proposed method showed a good effect to improve the contrast without supersaturation phenomenon as compared with the conventional methods.