• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.427-428
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• 2006

This paper presents an image fingerprinting scheme for the print-to-capture model performed by a photo printer and digital camera. When capturing an image by a digital camera, various kinds of distortions such as noise, geometrical distortions, and lens distortions are applied. slightly and simultaneously. In this paper, we consider several steps to extract fingerprints from the distorted image in print-and capture scenario. To embed ID into an image as a fingerprint, multi-bits embedding is applied. We embed 64 bits information as a fingerprint into spatial domain of color images. In order to restore a captured image from distortions a noise reduction filter is performed and a rectilinear tiling pattern is used as a template. To make the template, a multi-bits fingerprint is embedded repeatedly like a tiling pattern. We show that the extracting is successful from the image captured by a digital camera through the experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.133-138
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• 2008

In this paper, we propose a novel anchor shot detection system, named to MASD (Multi-phase Anchor Shot Detection), which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) one class SVM module for determining the anchor shots using a support vector data description. Besides the qualitative analysis, our experiments validate that the proposed system shows not only the comparable accuracy to the recently developed methods, but also more faster detection rate than those of others.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1228-1231
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• 2009

This paper presented an automatic system for display quality measurement by image processing. The goal is to replace human eyes for display quality evaluation by computer vision and get the objective quality review for consumer to make purchase of monitor or TV. Color, contrast, brightness, sharpness and motion blur are the main five factors to affect display quality that could be measured by supplying patterns and analyzing the corresponding images captured from webcam. The scores are calculated by image processing techniques. Linear regression model is then adopted to find the relation between human score and the measured display performance.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.117-122
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• 2009

We developed a multi-lingual namecard recognizer for building up a global client management systems. At first, we created the Unicode-based character image database for character recognition and learning of multi languages, and applied many color image processing techniques to get more correct data for namecard images which were acquired by various input devices. And by applying multi-layer perceptron neural network, individual character recognition applied for language types, and post-processing utilizing keyword databases made for individual languages, we increased a recognition rate for multi-lingual namecards.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.660-677
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• 2013

The blending of defective texture with the ambience texture results in camouflage. The gray value or color distribution pattern of the camouflaged images fails to reflect considerable deviations between the camouflaged object and the sublimating background demands improved strategies for texture analysis. In this research, we propose the implementation of an initial enhancement of the image that employs line masks, which could result in a better discrimination of the camouflaged portion. Finally, the gray value distribution patterns are analyzed in the enhanced image, to fix the camouflaged portions.

• Journal of the Korean Society of Costume
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• v.60 no.1
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• pp.28-42
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• 2010

A purpose of this study is to 6nd out how the casual and formal style clothes of stripe pattern giving variety by pattern direction, pattern width, and contrast coloration have an effect on image of wearers. For this, 192 stimuli were made and 1200 testee evaluated them using semantic differential scale. As a result, five image dimensions were drawn as a factor of attractiveness, gracefulness, activeness, visibility, and tenderness. Unlike the value contrast previously researched, it showed that chroma contrast coloration which was interacted with a color tone contrast coloration had an effect on all the 5 image dimensions. This result was recognized as significant clothes dues in evaluating the image of stripe wearers. Besides, clothing style, stripe pattern, and contrast coloration were made clear as an efficient parameter in image presentation of clothing wearers.

• ETRI Journal
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• v.34 no.3
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• pp.429-438
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• 2012

This study presents two reversible data hiding schemes based on the coefficient shifting (CS) algorithm. The first scheme uses the CS algorithm with a mean predictor in the spatial domain to provide a large payload while minimizing distortion. To guard against manipulations, the second scheme uses a robust version of the CS algorithm with feature embedding implemented in the integer wavelet transform domain. Simulations demonstrate that both the payload and peak signal-to-noise ratio generated by the CS algorithm with a mean predictor are better than those generated by existing techniques. In addition, the marked images generated by the variant of the CS algorithm are robust to various manipulations created by JPEG2000 compression, JPEG compression, noise additions, (edge) sharpening, low-pass filtering, bit truncation, brightness, contrast, (color) quantization, winding, zigzag and poster edge distortion, and inversion.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.877-880
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• 2000

In this paper, we develop an algorithm of soccer image sequences mosaicing using reverse affine transform. The continuous mosaic images of soccer ground field allows the user/viewer to view a “wide picture” of the player’s actions The first step of our algorithm is to automatic detection and tracking player, ball and some lines such as center circle, sideline, penalty line and so on. For this purpose, we use the ground field extraction algorithm using color information and player and line detection algorithm using four P-rules and two L-rules. The second step is Affine transform to map the points from image to model coordinate using predefined and pre-detected four points. General Affine transformation has many holes in target image. In order to delete these holes, we use reverse Affine transform. We tested our method in real image sequence and the experimental results are given.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.906-909
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• 2000

Conventional caption extraction methods use the difference between frames or color segmentation methods from the whole image. Because these methods depend heavily on heuristics, we should have a priori knowledge of the captions to be extracted. Also they are difficult to implement. In this paper, we propose a method that uses little heuristics and simplified algorithm. We use topographical features of characters to extract the character points and use KMST(Kruskal minimum spanning tree) to extract the candidate regions for captions. Character regions are determined by testing several conditions and verifying those candidate regions. Experimental results show that the candidate region extraction rate is 100%, and the character region extraction rate is 98.2%. And then we can see the results that caption area in complex images is well extracted.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.265-271
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• 2015

This study proposes an algorithm for recognizing apple trees in images and detecting apples to measure the number of apples on the trees. The proposed algorithm explores whether there are apple trees or not based on the number of image block-unit edges, and then it detects apple areas. In order to extract colors appropriate for apple areas, the CIE $L^*a^*b^*$ color space is used. In order to extract apple characteristics strong against illumination changes, modified census transform (MCT) is used. Then, using the AdaBoost learning algorithm, characteristics data on the apples are learned and generated. With the generated data, the detection of apple areas is made. The proposed algorithm has a higher detection rate than existing pixel-based image processing algorithms and minimizes false detection.