• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.323-330
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• 2019

Radiometric normalization with multi-temporal satellite images is essential for time series analysis and change detection. Generally, relative radiometric normalization, which is an image-based method, is performed, and histogram matching is a representative method for normalizing the non-linear properties. However, since it utilizes global statistical information only, local information is not considered at all. Thus, this paper proposes a histogram matching method considering local information. The proposed method divides histograms based on density, mean, and standard deviation of image intensities, and performs histogram matching locally on the sub-histogram. The matched histogram is then further partitioned and this process is performed again, iteratively, controlled with the wasserstein distance. Finally, the proposed method is compared to global histogram matching. The experimental results show that the proposed method is visually and quantitatively superior to the conventional method, which indicates the applicability of the proposed method to the radiometric normalization of multi-temporal images with non-linear properties.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.155-164
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• 2014

Local histogram equalization is one of the most popular ways of enhancing the local brightness features of an input image. However, local histogram equalization reveals some problems. First, undesired artifacts are produced by over-enhancing the local features. Second, the enhancement of local features does not always result in global contrast enhancement. To cope with these problems, we propose an illumination driven local histogram equalization method. First, to estimate the illumination information, the proposed method combines the input image and the blurred image produced through the process of the down-sampling and the up-sampling. Next, the proposed method adaptively adjusts the mapping function estimated by the local histogram equalization using the information of the illumination. As a result, the proposed illumination information driven local histogram equalization method simultaneously enhances the global and the local contrast levels while preventing any local artifacts. Experimental results show that the proposed algorithm outperforms the conventional methods on objective and subjective criteria.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.867-871
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• 2004

In this paper, a new analytic approach for shape preserving contrast enhancement is presented. Contrast enhancement is achieved by means of segmental histogram stretching modification which preserves the given image shape, not distorting the original shape. After global stretching, the image is partitioned into several level-sets according to threshold condition. The image information of each level-set is represented as typical value based on grouped differential values. The basic property is modified into common local schemes, thereby introducing the enhanced effect through extreme discrimination between subsets. The scheme is based on stretching the histogram of subsets in which the intensity gray levels between connected pixels are approximately same In spite of histogram widening, stretched by local image information, it neither creates nor destroys the original image, thereby preserving image shape and enhancing the contrast. By designing local histogram stretching operations, we can preserve the original shape of level-sets of the image, and also enhance the global intensity. Thus it can hold the main properties of both global and local image schemes, which leads to versatile applications in the field of digital epigraphy.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.856-862
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• 1987

An local adaptive image segmentatin algorithm for local feature detection and effective clustering of unimodal histogram shape are proposed. Local adaptive difference image and its histogram are obtained from the input image. The parameters are derived from the histogram and used for the segmentation based on relaxatin process. The results showed effective region segmentation and good noise cleaning for the ocular fundus fluorescein angiogram which has low contrast and unimodal histogram.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.189-194
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• 2017

In this study, we propose a method which is based on "Image segmentation by histogram thresholding using hierarchical cluster analysis"/HCA/ and "A nonparametric approach for histogram segmentation"/NHS/. HCA method uses that all histogram bins are one cluster then it reduces cluster numbers by using distance metric. Because this method has too many clusters, it is more computation. In order to eliminate disadvantages of "HCA" method, we used "NHS" method. NHS method finds all local minima of histogram. To reduce cluster number, we use NHS method which is fast. In our approach, we combine those two methods to eliminate disadvantages of Arifin method. The proposed method is not only less computational than "HCA" method because combined method has few clusters but also it uses local minima of histogram which is computed by "NHS".

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.1-8
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• 2006

Network measurement is a vital part of network traffic engineering. In a network, the metric 'capacity' characterizes the maximum throughput the path can provide when there is no traffic load, or the minimum transmission rate among all links in a path. Pathrate is one of the most widely used network capacity measurement tools nowadays. It's famous for its accurate estimation result and non restriction of the temporal network traffic condition. After several years of development, its performance becomes more stable and reliable. Extant local modes detection algorithm in pathrate is based on statistic methodology histogram. This paper suggests a new algorithm for local modes detection based on ASH (Averaged Shifted Histogram). We have implemented this algorithm and will prove it can accomplish the same task as the original one with a better result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5197-5218
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• 2019

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

• 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.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.52-58
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• 2014

Exact histogram specification (EHS) transforms the histogram of an input image into the specified histogram. In the conventional EHS techniques, the pixels are first sorted according to their graylevels, and the pixels that have the same graylevel are further differentiated according to the local average of the pixel values and the edge strength. The strictly ordered pixels are then mapped to the desired histogram. However, since the conventional sorting method is inherently dependent on the initial graylevel-based sorting, the contrast enhancement capability of the conventional EHS algorithms is restricted. We propose a modified EHS algorithm considering the just noticeable difference. In the proposed algorithm, the edge pixels are pre-processed such that the output edge pixels obtained by the modified EHS can result in the local contrast enhancement. Moreover, we introduce a new sorting method for the pixels that have the same graylevel. Experimental results show that the proposed algorithm provides better image enhancement performance compared to the conventional EHS algorithms.

• Spatial Information Research
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• v.21 no.3
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• pp.11-19
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• 2013

Compared with traditional video monitoring systems that provide a video-recording function as a main service, an intelligent video monitoring system is capable of extracting/tracking objects and detecting events such as car accidents, traffic congestion, pedestrian detection, and so on. Thus, the object tracking is an essential function for various intelligent video monitoring and surveillance systems. In this paper, we propose a background and local histogram-based object tracking approach for intelligent video monitoring systems. For robust object tracking in a live situation, the result of optical flow and local histogram verification are combined with the result of background subtraction. In the proposed approach, local histogram verification allows the system to track target objects more reliably when the local histogram of LK position is not similar to the previous histogram. Experimental results are provided to show the proposed tracking algorithm is robust in object occlusion and scale change situation.