• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.532-535
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• 2003

In this paper, we propose a new algorithm that can enhance the speed of template matching of hand vascular pattern person verification or recognition system. Various template matching algorithms have advantages in the matching accuracy, but most of the algorithms suffer from computational burden. To reduce the computational amount, with accuracy maintained, we propose following template matching scenario as follows. firstly, original hand vascular image is re-sampled in order to reduce spatial resolution. Secondly, reconstructed image is projected to vertical and horizontal direction, being converted to two one dimensional (1D) data. Thirdly, converted data is used to estimate spatial discrepancy between stored template image and target image. Finally, matching begins from where the estimated order is highest, and finishes when matching decision function is computed to be over certain threshold. We've applied the proposed algorithm to hand vascular pattern identification application for biometrics, and observed dramatic matching speed enhancement. This paper presents detailed explanation of the proposed algorithm and evaluation results.

• Journal of Multimedia Information System
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• v.5 no.4
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• pp.221-228
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• 2018

Skin-color information is not sufficient for palmprint segmentation in complex scenes, including mobile environments. Traditional active shape model (ASM) combines gray information and shape information, but its performance is not good in complex scenes. An improved ASM method is developed for palmprint segmentation, in which Perux method normalizes the shape of the palm. Then the shape model of the palm is calculated with principal component analysis. Finally, the color likelihood degree is used to replace the gray information for target fitting. The improved ASM method reduces the complexity, while improves the accuracy and robustness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.502-507
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• 2009

We present a real-time photomosaics method for small image set optimized by feature selection method. Photomosaics is an image that is divided into cells (usually rectangular grids), each of which is replaced with another image of appropriate color, shape and texture pattern. This method needs large set of tile images which have various types of image pattern. But large amount of photo images requires high cost for pattern searching and large space for saving the images. These requirements can cause problems in the application to a real-time domain or mobile devices with limited resources. Our approach is a genetic feature selection method for building an optimized image set to accelerate pattern searching speed and minimize the memory cost.

• Fashion & Textile Research Journal
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• v.16 no.4
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• pp.645-652
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• 2014

This study identifies consumer needs and a pattern sensory evaluation of jacquard fabrics for raincoats using quick-drying-absorbing polyester. We investigate the consumer's consciousness and raincoat improvements. Twelve kinds of jacquard fabrics were developed for use in this study. Developed jacquard fabrics were assessed subjectively by 152 university students using a 7-point scale of 26 consumer needs and 31 pattern image sensory descriptors. Data were analyzed by SPSS. The major results were: There was a need for consumers to improve the front fastener type, cuff fastener, mesh patch position, and raincoat pocket position. The most important parameter to choose raincoat fabric was waterproof and the other parameters were vapor-porous/water repellent, design, color, fashionability, air-permeability and easy-put on/off. The pattern image sensibility of jacquard fabrics was explained by seven factors: gorgeous, simple, cute, futuristic, ethnic, feminine, and cool. A higher pattern preference was found in the jacquard fabrics of unique, sporty, natural, luxurious, and trendy images. The pattern preference was predicted at 45.3% with gorgeous, simple, pure, cute, futuristic factors. The correlation coefficient between the pattern image sensibility factor 1 (gorgeous) and pattern preference was 0.674 and with factor 3 (cute) was 0.416, and with factor 6 (cool) was 0.209. The 4 factors (gorgeous, simple, cute, futuristic) were selected as a significant pattern image sensibility that influenced preference.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.371-378
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• 2000

In this study, we proposed pathological pattern detection system for X-ray chest image using artificial neural network. In a physical examination, radiologists have checked on the chest image projected the view box by a magnifying glass and found out what the disease is. Here, the detection of X-ray fluoroscopy is tedious and time-consuming for human doing. Lowering of efficiency for chest diagnosis is caused by lots mistakes of radiologist because of detecting the micro pathology from the film of small size. So, we proposed the method for disease detection using artificial neural network and digital image processing on a X-ray chest image. This method composes the function of image sampling, median filter, image equalizer used neural network and pattern recognition used neural network. We confirm this method has improved the problem of a conventional method.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.5
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• pp.825-839
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• 2021

This study aims to develop jacquard fashion masks using traditional patterns and investigates the preference and emotions of consumers for them. Nine patterns were designed with the motifs of plum flower, turtle, and geometric patterns using an Adobe Illustrator program. After that, 20 kinds of jacquard fabrics were developed using those patterns, and prototype masks were made. Furthermore, data were collected using a survey of 231 adult consumers to understand the emotional images evoked by jacquard fashion masks with traditional patterns. The results of the research show that the emotional dimension derived from jacquard fashion masks with traditional patterns consists of seven factors: luxurious image, frugal image, feminine image, oriental image, sporty image, geometrical image, generous image. We found that consumers preferred the M6 with the plum flower pattern as the motif as the most preferred design, while M13 with the geometric pattern as the motif was the lowest preferred.

• Journal of Korean Ophthalmic Optics Society
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• v.3 no.1
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• pp.201-207
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• 1998

It was theoretically calculated the image position and size using matrix to obtain the reflection pattern for eye's cornea and crystalline, and made system to measure the reflection pattern by three light sources and a reflex camera. Hyperopia and myopia were measured by reflect pattern using single light source at retina, and cornea and curvature of crystalline were measured by the reflection pattern using double light sources.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.529-534
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• 2009

This paper reports a non-photorealistic rendering method for creating stream pattern from an input image. Our method extracts potential stream pattern in the given image. The proposed approach uses a shock filter based on a partial difference equation(PDE) which is implemented by applying a selective dilation and erosion processes. However, unlike the traditional first order solution to the PDE, we employ a second order scheme and compensate for the undesired diffusive effects caused by a viscosity form. The selection of dilation or erosion for a pixel is based on an edge detector computed from a structure tensor. By adding noises on to the input image, our method also can generate stream pattern even if there is less texture in some area. The experimental results show that the stream pattern is extracted very well.

• Journal of the Optical Society of Korea
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• v.15 no.2
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• pp.140-145
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• 2011

Joint transform correlator (JTC) is a well known tool for color pattern recognition for a color image. Color images have red, green and blue components, thus in conventional JTC, three input channels of these color components are necessary for color pattern recognition. This paper proposes a new technique of color pattern recognition by decomposing the color image into three color components and recombining those components into a single gray image in the input plane. This new technique needs single input channel and single output CCD camera, thus a simple JTC can be used. We present various kinds of simulated results to show that our newly proposed technique can accurately recognize and discriminate color differences.