• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.38-45
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• 2001

In this paper, we propose an algorithm for moving object tracking from moving camera using histogram back program intersection(HI) and XY-projection The proposed method segments objects using histogram back projection, matches tracing objects using histogram intersection and extracts them using XY- projection. Through the simulation this paper shows that the proposed method segments. matches and tracks objects without significant error image sequences obtained by moving camera.

• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.123-127
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• 2004

The most popular technique for image retrieval in a heterogeneous collection of color images is the comparison of images based on their color histogram. The color histogram describes the distribution of colors in the color space of a color image. In the most image retrieval systems, the color histogram is used to compute similarities between the query image and all the images in a database. But, small changes in the resolution, scaling, and illumination may cause important modifications of the color histogram, and so two color images may be considered to be very different from each other even though they have completely related semantics. A new method of color feature representation based on the 3-dimensional RGB color map is proposed to improve the defects of the color histogram. The proposed method is based on the three 2-dimensional projection map evaluated by projecting the RGB color space on the RG, GB, and BR surfaces. The experimental results reveal that the proposed is less sensitive to small changes in the scene and that achieve higher retrieval performances than the traditional color histogram.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.23-30
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• 2016

In this paper, we propose an adaptive histogram projection technique for dynamic range compression and an efficient detail enhancement method which is enhancing strong edge while reducing noise. First, The high dynamic range image is divided into low-pass component and high-pass component by applying 'guided image filtering'. After applying 'guided filter' to high dynamic range image, second, the low-pass component of the image is compressed into 8-bit with the adaptive histogram projection technique which is using global standard deviation value of whole image. Third, the high-pass component of the image adaptively reduces noise and intensifies the strong edges using standard deviation value in local path of the guided filter. Lastly, the monitor display image is summed up with the compressed low-pass component and the edge-intensified high-pass component. At the end of this paper, the experimental result show that the suggested technique can be applied properly to the IR images of various scenes.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.456-456
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• 2000

The back projection histogram method has been proposed as the method to construct an image from waves reflected from a buried object for subsurface radar. This method is compose of two phases, i.e., a back projection image construction process and a back projection image superposition process. A simulation analysis of this method has been studied. In this paper, an experimental study is demonstrated in air as the homogeneous Held using three cylinders as buried objects.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.2
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• pp.73-79
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• 2010

The most popular technique for image retrieval in a heterogeneous collection of color images is the comparison of images based on their color histogram. The color histogram describes the distribution of colors in the color space of a color image. In the most image retrieval systems, the color histogram is used to compute similarities between the query image and all the images in a database. But, small changes in the resolution, scaling, and illumination may cause important modifications of the color histogram, and so two color images may be considered to be very different from each other even though they have completely related semantics. A new method of color feature representation based on the 3-dimensional RGB color map is proposed to improve the defects of the color histogram. The proposed method is based on the three 2-dimensional projection map evaluated by projecting the RGB color space on the RG, GB, and BR surfaces. The experimental results reveal that the proposed is less sensitive to small changes in the scene and that achieve higher retrieval performances than the traditional color histogram.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.651-656
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• 2001

Histogram intersection method, that counts the occurrence of color pixels, is one of the easy and simple color image retrieval methods. The method has an appropriate global property but does not contain the knowledge of shape for images. The absence of spatial information makes it difficult to discriminate images of the similar histogram. The application of one-dimensional projection to each image enables to obtain shape or spatial information of image. But in this case there is another problem having different length of the projection vector according to the size of each image. Thus this paper proposes a method that uses relative distances between peaks and their maximum value in the projection vector. In order to verify retrieval performance, the experimental results between the histogram intersection method, the projection only method, and the proposed one are compared and analyzed.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.849-852
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• 2000

본 논문은 배경이 고정되지 않은 복잡한 동영상에서의 물체 추적을 위하여 다중 모델 색상 히스토그램 역투영(Multi Model Color Histogram Back-projection)방법을 제안한다. 색상 히스토그램 역투영(Color Histogram Back-projection)을 이용하면 카메라의 움직임 때문에 발생하는 배경의 변화에 관계없이 물체를 추적할 수 있다. 기존의 방법은 추적하려는 물체에 대해 하나의 모델만을 적용했기 때문에, 배경영역 색분포의 영향을 많이 받는다. 이를 해결하기 위해 다중 모델 색상 히스토그램 역투영 방법을 이용하였다. 이 방법은 추적하려는 물체에 대해 여러 개의 모델을 구하여 각각에 대해 색상 히스토그램 역투영을 수행한다 또한 역투영 이진 영상에서 물체의 위치를 결정하기 위한 수평, 수직 프로젝션 방법의 문제점을 레이블링(Labeling)을 사용하여 보완하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.2
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• pp.94-102
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• 2011

In this paper, a novel drowsiness detection algorithm using eye-blink pattern is proposed. The proposed drowsiness detection model using finite automata makes it easy to detect eye-blink, drowsiness and sleep by checking the number of input symbols standing for closed eye state only. Also it increases the accuracy by taking vertical projection histogram after locating the eye region using the feature of horizontal projection histogram, and minimizes the external effects such as eyebrows or black-framed glasses. Experimental results in eye-blinks detection using the JZU eye-blink database show that our approach achieves more than 93% precision and high performance.

• International Journal of Contents
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• v.8 no.1
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• pp.30-38
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• 2012

Text extraction and binarization are the important pre-processing steps for text recognition. The performance of text binarization strongly related to the accuracy of recognition stage. In our proposed method, the first stage based on line detection and shape feature analysis applied to locate the position of a business card and detect the shape from the complex environment. In the second stage, several local regions contained the possible text components are separated based on the projection histogram. In each local region, the pixels grouped into several connected components based on the connected component labeling and projection histogram. Then, classify each connect component into text region and reject the non-text region based on the feature information analysis such as size of connected component and stroke width estimation.

• Journal of information and communication convergence engineering
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• v.11 no.1
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• pp.62-68
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• 2013

This paper proposes a method for identification of harmful video images based on the degree of harmfulness in the video content. To extract harmful candidate frames from the video effectively, we used a video color extraction method applying a projection map. The procedure for identifying the harmful video has five steps, first, extract the I-frames from the video and map them onto projection map. Next, calculate the similarity and select the potentially harmful, then identify the harmful images by comparing the similarity measurement value. The method estimates similarity between the extracted frames and normative images using the critical value of the projection map. Based on our experimental test, we propose how the harmful candidate frames are extracted and compared with normative images. The various experimental data proved that the image identification method based on the 2-dimensional projection map is superior to using the color histogram technique in harmful image detection performance.