• Journal of Digital Contents Society
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• v.12 no.1
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• pp.1-9
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• 2011

JPEG2000 offers the technique which compresses the interested regions with higher quality than the background. It is called by an ROI(Region-of-Interest) coding method. In this paper, we use images including the human faces, which are processed uppermost and compressed with high quality. The proposed method consists of 2 steps. The first step extracts some faces and the second one is ROI coding. To extract the faces, the method cuts or scale-downs some regions with $20{\times}20$ window pixels for all the pixels of the image, and after preprocessing, recognizes the faces using neural networks. Each extracted region is identified by ROI mask and then ROI-coded using Maxshift method. After then, the image is compressed and saved using EBCOT. The existing methods searched the ROI by edge distributions. On the contrary, the proposed method uses human intellect. And the experiment shows that the method is sufficiently useful with images having several human faces.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1713-1720
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• 1999

This paper presents a new key frame extraction technique, for scene change detection, using the proposed AHIM (Accumulative Histogram Intersection Measure) from the DC image constructed by DCT DC coefficients in the compressed video sequence that is video compression standard such as MPEG. For fast content-based browsing and video retrieval in a video database, we also provide a novel coarse-to-fine video indexing scheme. In the extracted key frame, we perform the region segmentation as a preprocessing. First, the segmented image is projected with the horizontal direction, then we transform the result into a histogram, which is saved as a database index. In the second step, we calculate the moments and change them into a distance value. From the simulation results, the proposed method clearly shows the validity and superiority in respect of computation time and memory space, and that in conjunction with other techniques for indexing, such as color, can provide a powerful framework for image indexing and retrieval.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.374-380
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• 2019

Containers are fitted with various placards on the surface to indicate the risk of cargo. If the containers are loaded with dangerous goods, care should be taken in handling the containers. Therefore, as part of the port automation system, there is a demand for automatic placard recognition. In this paper, proposed is a method to extract placard areas from a container image, which is the first part of the placard recognition system. The fact that placards are of various types but all have a diamond shape can be an advantage in recognition. However, it is a disadvantage in recognition that the placards can be distorted in various ways because the container surface is not flat. When the proposed method was applied to actual images, type I error did not occur. In addition, since the shape feature of the object and basic image operations are used to extract regions of interest, it can be applied to various shape-based region extraction problems.

• Journal of Platform Technology
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• v.9 no.4
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• pp.71-78
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• 2021

Self-driving vehicles are increasing as new alternatives to solving problems such as human safety, environment and aging. And such technology development has a great ripple effect on other industries. However, various problems are occurring. The number of casualties caused by self-driving is increasing. Although the collision of fixed obstacles is somewhat decreasing, on the contrary, the technology by active obstacles is still insignificant. Therefore, in this study, in order to solve the core problem of self-driving vehicles, we propose a method of extracting active obstacles on the road. First, a center scene is extracted from a continuous image. In addition, it was proposed to extract activity obstacles using activity size and activity repeatability information from objects included in the center scene. The center scene is calculated using region segmentation and merging. Based on these results, the size of the frequency for each pixel in the region was calculated and the size of the activity of the obstacle was calculated using information that frequently appears in activity. Compared to the results extracted directly by humans, the extraction accuracy was somewhat lower, but satisfactory results were obtained. Therefore, it is believed that the proposed method will contribute to solving the problems of self-driving and reducing human accidents.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.13-22
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• 2024

Currently, with the proliferation of digital devices, the significance of handwritten texts in daily lives is gradually diminishing. As the use of keyboards and touch screens increase, a decline in Korean handwriting quality is being observed across a broad spectrum of Korean documents, from young students to adults. However, Korean handwriting still remains necessary for many documentations, as it retains individual unique features while ensuring readability. To this end, this paper aims to implement an application designed to improve and correct the quality of handwritten Korean script The implemented application utilizes the CRAFT (Character-Region Awareness For Text Detection) model for handwriting area detection and employs the VGG-Feature-Extraction as a deep learning model for learning features of the handwritten script. Simultaneously, the application presents the user's handwritten Korean script's reliability on a syllable-by-syllable basis as a recognition rate and also suggests the most similar fonts among candidate fonts. Furthermore, through various experiments, it can be confirmed that the proposed application provides an excellent recognition rate comparable to conventional commercial character recognition OCR systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.48-60
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• 2007

In previous researches for iris feature extraction, they transform a original iris image into rectangular one by stretching and interpolation, which causes the distortion of iris patterns. Consequently, it reduce iris recognition accuracy. So we are propose the method that extracts iris feature by using polar coordinates without distortion of iris patterns. Our proposed method has three strengths compared with previous researches. First, we extract iris feature directly from polar coordinate circular iris image. Though it requires a little more processing time, there is no degradation of accuracy for iris recognition and we compares the recognition performance of polar coordinate to rectangular type using by Hamming Distance, Cosine Distance and Euclidean Distance. Second, in general, the center position of pupil is different from that of iris due to camera angle, head position and gaze direction of user. So, we propose the method of iris feature detection based on polar coordinate circular iris region, which uses pupil and iris position and radius at the same time. Third, we overcome override point from iris patterns by using polar coordinates circular method. each overlapped point would be extracted from the same position of iris region. To overcome such problem, we modify Gabor filter's size and frequency on first track in order to consider low frequency iris patterns caused by overlapped points. Experimental results showed that EER is 0.29%, d' is 5,9 and EER is 0.16%, d' is 6,4 in case of using conventional rectangular image and proposed method, respectively.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.35-44
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• 2010

In this paper, we propose a method about producing skin color model adaptively in input image and face detection. The principle process which we proposed is finding eyes candidates by applying the eye features to neural network, and then using the around color to find the distribution of color value. There will be a verification process that producing face region by using color value distribution which is detected as skin region and find mouth candidate in corresponding face region; if eye candidate and mouth candidate's connection structure is similar with face structure, then it can be judged as a face. Because this method can detect skin region adaptively by finding eyes, we solve the rate of false positive about the distorted skin color which is used by existing face detection methods. The experiment was performed about detecting the eye, the skin, the mouth and the face individually. The results revealed that the proposed technique is better than the traditional techniques.

• The Journal of the Korea Contents Association
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• v.6 no.2
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• pp.44-50
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• 2006

This study has proposed a method of content-based image retrieval using object region in order to overcome disadvantages of existing color histogram methods. The existing color histogram methods have a weak point of reducing accuracy, because these have both a quantization error and an absence of spatial information. In order to overcome this problem, we convert a color information to a HSV space, quantize hue factor being pure color information, and calculate histogram. And then we use hue for retrieval feature that is robust in brightness, movement, and rotation. To solve the problem of the absence of spatial information, we select object region in terms of color feature and region correlation. And we use both the edge and the DC in the selected region for retrieving. As a result of experiment with 1,000 natural color images, the proposed method shows better precision and recall than the existing methods.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.691-702
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• 2004

In this paper, we propose a sequence of method which extrats ROIs(Region of Interests) rapidly from the parcel image of large size. In the proposed method, original image is spilt into the small masks, and the meaningful masks, the ROIs, are extracted by two criterions sequentially The first criterion is difference of pixel value between Inner points, and the second is deviation of it. After processing, some informational ROIs-the areas of bar code, characters, label and the outline of object-are acquired. Using diagonal axis of each ROI and the feature of various 2D bar code, the area of 2D bar code can be extracted from the ROIs. From an experiment using above methods, various ROIs are extracted less than 200msec from large-size parcel image, and 2D bar code region is selected by the accuracy of 100％.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.268-275
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• 2002

This paper proposes a region-based segmentation algorithm fur extracting a video object by using the cost of potential of frame-difference energies. In the first step of a region-based segmentation using spatial intensity, each frame is segmented into a partition of homogeneous regions finely so that each region does not contain the contour of a video object. The fine partition is used as an initial partition for the second step of spatio-temporal segmentation. In spatio-temporal segmentation, the homogeneity cost for each pair of adjacent regions is computed which reflects the potential between the frame-difference energy on the common contour and the frame-difference energy of the lower potential region of the two. The pair of adjacent regions whose cost is minimal then is searched. The two regions of minimum cost ale merged, which result in updating the partition. The merging is recursively performed until only the contours remain which have Same difference energies of high potential. In the fecal step of post-processing, the video object is extracted removing the contours inside the object.