http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Hierarchical Cluster Analysis Histogram Thresholding with Local Minima
Sengee, Nyamlkhagva,Radnaabazar, Chinzorig,Batsuuri, Suvdaa,Tsedendamba, Khurel-Ochir,Telue, Berekjan Korea Multimedia Society 2017 The journal of multimedia information system Vol.4 No.4
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".
A Novel Filter ed Bi-Histogram Equalization Method
Sengee, Nyamlkhagva,Choi, Heung-Kook Korea Multimedia Society 2015 멀티미디어학회논문지 Vol.18 No.6
Here, we present a new framework for histogram equalization in which both local and global contrasts are enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Filters are chosen depending on image properties, such as noise removal and smoothing. Our experimental results confirmed that this does not increase the computational cost because the filtering process is done by our proposed arrangement of making the histogram while checking neighborhood metrics simultaneously. If the two methods, i.e., histogram equalization and filtering, are performed sequentially, the first method uses the original image data and next method uses the data altered by the first. With combined histogram equalization and filtering, the original data can be used for both methods. The proposed method is fully automated and any spatial neighborhood filter type and size can be used. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.
Contrast Enhancement using Histogram Equalization with a New Neighborhood Metrics
Sengee, Nyamlkhagva,Choi, Heung-Kook Korea Multimedia Society 2008 멀티미디어학회논문지 Vol.11 No.6
In this paper, a novel neighborhood metric of histogram equalization (HE) algorithm for contrast enhancement is presented. We present a refinement of HE using neighborhood metrics with a general framework which orders pixels based on a sequence of sorting functions which uses both global and local information to remap the image greylevels. We tested a novel sorting key with the suggestion of using the original image greylevel as the primary key and a novel neighborhood distinction metric as the secondary key, and compared HE using proposed distinction metric and other HE methods such as global histogram equalization (GHE), HE using voting metric and HE using contrast difference metric. We found that our method can preserve advantages of other metrics, while reducing drawbacks of them and avoiding undesirable over-enhancement that can occur with local histogram equalization (LHE) and other methods.
Contrast Enhancement for Segmentation of Hippocampus on Brain MR Images
Sengee, Nyamlkhagva,Sengee, Altansukh,Adiya, Enkhbolor,Choi, Heung-Kook Korea Multimedia Society 2012 멀티미디어학회논문지 Vol.15 No.12
An image segmentation result depends on pre-processing steps such as contrast enhancement, edge detection, and smooth filtering etc. Especially medical images are low contrast and contain some noises. Therefore, the contrast enhancement and noise removal techniques are required in the pre-processing. In this study, we present an extension by a novel histogram equalization in which both local and global contrast is enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Most important is that original image information can be used for both global brightness preserving and local contrast enhancement, and image quality improvement filtering. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.
Altansukh Sengee,Nyamlkhagva Sengee,Heung-Kook Choi,Myung-Ja Tak,Sae-Hong Cho 한국멀티미디어학회 2011 한국멀티미디어학회 국제학술대회 Vol.2011 No.-
This study objective was to compare popular image reconstruction methods which are the filtered backprojection (FBP) and maximum likelihood expectation maximization (MLEM) on some medical and phantom images with noise. Peak signal noise ratio (PSNR) is used to evaluate the methods. Experimental result shows that FBP and MLEM are closely similar result but MLEM is better than FBP in noisy images.
AN AUTOMATIC METHOD FOR IMAGE CONTRAST ENHANCEMENT BY HISTOGRAM WEIGHT CLUSTERING
Nyamlkhagva Sengee,Byambaragchaa Bazarragchaa,Choi Heung Kook 한국멀티미디어학회 2008 한국멀티미디어학회 학술발표논문집 Vol.2008 No.2
Histogram equalization (GHE) is a simple and well known method in contrast enhancement techniques. There are extensions of GHE to preserve image brightness. Although they can preserve a brightness of original image more than GHE, they couldn't enhance a visualization of original image on some images. Therefore, we propose a new method which not only can preserve the brightness of original image but also can enhance the visualization of original image. The proposed method is called 'Brightness Preserving Weight Clustering Histogram Equalization' (BPWCHE). BPWCHE assigns each non zero bins of original image's histogram to one cluster, and computes every cluster's weight. Then, in order to reduce cluster number, we use three criterions (cluster weight, weight ratio and width of two neighbor clusters) to merge two neighbor clusters. The clusters acquire same partitions of result image histogram. Finally, transformation functions of each cluster's sub-histogram are calculated based on traditional GHE method in their new acquired partitions of result image histogram, and the sub-histogram's gray levels are mapped to the result image by the transformation functions of sub-histograms, correspondingly. As experimental results, BPWCHE can preserve image brightness and enhance visualization of image more effective than GHE and other brightness preserving method.