• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.536-538
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• 2005

As multimedia data and huge-Quantity video data having been increasingly and commonly used, the key frame algorithm, as one of the methods for manipulating these kinds of data, became an important matter and has been studied for many years. But the formerly proposed key frame extraction methods take much processing time or need complex calculations due to decoding processes. In order to solve these problems which the former methods have and to enhance the key frame extraction efficiency, a novel key frame extraction method in compressed domain is proposed in this paper. In this method we get an edge histogram for each I-frame in DCT domain and then extract the key frames by means of histogram difference metric. Experimental results show that our algorithm achieves fast processing speed and high accuracy.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.319-326
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• 2009

In this paper, we present an algorithm which recognize hand shape in real time using only image without adhering separate sensor. Hand recognizes using edge orientation histogram, which comes under a constant quantity of 2D appearances because hand shape is intricate. This method suit hand pose recognition in real time because it extracts hand space accurately, has little computation quantity, and is less sensitive to lighting change using color information in complicated background. Method which reduces recognition error using principal component analysis(PCA) method to can recognize through hand shape presentation direction change is explained. A case that hand shape changes by turning 3D also by using this method is possible to recognize. Human interface system manufacture technique, which controls a home electric appliance or game using, suggested method at experience could be applied.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.195-208
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• 2004

Histogram is a very useful method on various practical aspect. With increasing importance of simple calculation method and convenience, it became the basic method in digital image processing nowadays. However, basic limit of using histogram is losing spatial position information of pixels on image. This paper reanalyzes image by presenting histogram with spatial position information(spatial histogram). Also using that result, level order recursive median filter is realized. Presented recursive median filter showed much improved results on edge maintenance aspect compared to existing recursive median filter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.151-154
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• 2008

In this paper, this program which is Retrieval System using Image Gray-scale histogram and Edge features is used to reduce the errors incurred by inputting methods which are used in a current Retrieval System. The Retrieval Algorithm is proceeding with several steps which are extracting features of images quality, extracting edge features and refining images, analysing extracted features, retaining important information from analyzed features, retrieving retained information from database, extracting and comparing among images from retrieved database. The proposed Retrieval System is used for a fast retrieval with accuracy and it is confirmed through simulations.

• ETRI Journal
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• v.29 no.2
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• pp.246-248
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• 2007

Query-by-example (QBE) is a well-known method for image retrieval. In reality, however, an example image to be used for the query is rarely available. Therefore, it is often necessary to find a good example image to be used for the query before applying the QBE method. Query-by-layout (QBL) is our proposal for that purpose. In particular, we make use of the visual descriptors such as the edge histogram descriptor (EHD) and the color layout descriptor (CLD) in MPEG-7. Since image features of the CLD and the EHD can be localized in terms of a$4{\times}4$ sub-image, we can specify image features such as color and edge distribution on each sub-image separately for image retrieval without a query image. Experimental results show that the proposed query method can be used to retrieve a good image as a starting point for further QBE-based image retrieval.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.352-355
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• 2000

In this paper, we propose an adaptive thresholding for edge detection. first, we got histograms for background image and image with moving object, respectively. Then we make difference histogram between histograms of background and object image. A thresholding value is decided using gradient of peak to peak in the difference histogram. The experimentation is processed using a moving car in the road. The result is that edge is detected well regardless of the brightness.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.189-195
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• 2012

In this paper, face detection method in NIR(Near-InfraRed) images for human recognition is proposed. Edge histogram based on edge intensity and its direction, has been used to detect effectively faces on NIR image. The edge histogram descripts and discriminates face effectively because it is strong in environment of lighting change. SVM(Support Vector Machine) has been used as a classifier to detect face and the proposed method showed better performance with smaller features than in ULBP(Uniform Local Binary Pattern) based method.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.185-193
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• 2022

There have been various studies in Korea to develop a %IMF(Intramuscular Fat Percentage) estimation method suitable for Hanwoo. Recently, a %IMF estimation method using a convolutional neural network (CNN), a kind of deep learning method among artificial intelligence methods, has been studied. In this study, we performed a performance comparison when various preprocessing methods were applied to the %IMF estimation of ultrasound images using CNN as mentioned above. The preprocessing methods used in this study are normalization, histogram equalization, edge enhancement, and a method combining normalization and edge enhancement. When estimating the %IMF of Hanwoo by the conventional method that did not apply preprocessing in the experiment, the accuracy was 98.2%. The other hand, we found that the accuracy improved to 99.5% when using preprocessing with histogram equalization alone or combined regularization and edge enhancement.

• The Journal of Korean Association of Computer Education
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• v.7 no.5
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• pp.111-120
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• 2004

The MPEG-7 standard defines a set of descriptors that extracts low-level features such as color, texture and object shape from an image and generates metadata in order to represent these extracted information. But the matching performance for image mining ma y not be satisfactory by u sing only on e of these features. Rather than by combining these features we can achieve a better query performance. In this paper we propose a new image retrieval technique for image mining that combines the features extracted from MPEG-7 visual color and texture descriptors. Specifically, we use only some specifications of Scalable Color Descriptor (SCD) and Non-Homogeneous Texture Descriptor also known as Edge Histogram Descriptor (EHD) for the implementation of the color and edge histograms respectively. MPEG-7 standard defines $l_{1}$-norm based matching in EHD and SCD. But in our approach, for distance measurement, we achieve a better result by using cosine similarity coefficient for color histograms and Euclidean distance for edge histograms. Our approach toward this system is more experimental based than hypothetical.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.113-117
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• 2007

In this paper, we propose a new method for determining automatically the size of the matching window using histogram of the gradient in order to improve the performance of stereo matching using one-meter resolution satellite imagery. For each pixel, we generate Flatness Index Image by calculating the mean value of the vertical or horizontal intensity gradients of the 4-neighbors of every pixel in the entire image. The edge pixel has high flatness index value, while the non-edge pixel has low flatness index value. By using the histogram of the Flatness Index Image, we find a flatness threshold value to determine whether a pixel is edge pixel or non-edge pixel. If a pixel has higher flatness index value than the flatness threshold value, we classify the pixel into edge pixel, otherwise we classify the pixel into non-edge pixel. If the ratio of the number of non-edge pixels in initial matching window is low, then we consider the pixel to be in homogeneous region and enlarge the size of the matching window We repeat this process until the size of matching window reaches to a maximum size. In the experiment, we used IKONOS satellite stereo imagery and obtained more improved matching results than the matching method using fixed matching window size.