• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.763-765
• /
• 2005

In the area of remote sensing, an immense number of images are continuously generated by various remote sensing systems. These images must then be managed by a database system efficient storage and retrieval. There are many types of image database systems, among which the content-based image retrieval (CBIR) system is the most advanced. CBIR utilizes the metadata of images including the feature data for indexing and searching images. Therefore, the performance of image retrieval is significantly affected by the storage method of the image metadata. There are many features of images such as color, texture, and shape. We mainly consider the shape feature because shape can be identified in any remote sensing while color does not always necessarily appear in some remote sensing. In this paper, we propose a metadata representation and storage method for image search based on shape features. First, we extend MPEG-7 to describe the shape features which are not defined in the MPEG-7 standard. Second, we design a storage schema for storing images and their metadata in a relational database system. Then, we propose an efficient storage method for managing the shape feature data using a Wavelet technique. Finally, we provide the performance results of our proposed storage method.

• Journal of the Korean Home Economics Association
• /
• v.43 no.11 s.213
• /
• pp.97-107
• /
• 2005

Ed- the file appears to be corrupted, and in many sections (these are highlighted) I cannot read it. I presume it has been copied from another format, maybe Hangul 2004. The purpose of this study was to determine a method of make-up image extraction from specific paintings and also to create cyber make-up models according to the images. For this study, Georgia O'Keeffee's floral paintings were analyzed and their colors were compiled to make color palettes. This study attempted to approach floral image make-up which applies specific paintings through the digital mode in the manner of computer graphics. The results of this study were as follows: First, we found romantic images, including feminine, lovely and soft images by Y, GY and RP group colors, in 'Two Calla Lillies on Pink'. Second, we found modem images, including urban, up-to-date and cool images by G, GY and B group colors, in 'Blue and Green Music'. Third, we found sexy images, including brilliant, tempting and daring by R, B and G group colors, in 'Music-Pink and Blue'. To summarize, the images of the paintings were similar to those of the make-up models.

• Journal of the Korean Society for Precision Engineering
• /
• v.24 no.11
• /
• pp.126-133
• /
• 2007

Tuberculosis (TB) is a chronic or acute infectious disease which damages more people than any other infectious diseases according to WHO estimates. In this paper, a new automatic detection system of tuberculosis bacilli by using their color information is proposed. Through the deep investigation of color and intensity compositions of tuberculosis images, new pre-processing and segmentation algorithms are suggested. Specific features of bacilli are extracted from the processed images and number counting is done by using domain-specific knowledge rules.

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.421-424
• /
• 1999

Automatic human face detection in a complex background is one of the difficult problems In this paper. we propose an effective automatic face detection system that can locate the face region in natural scene images when the system is used as a pre-processor of a face recog- nition system. We use two natural and powerful visual cues, the color and the human head shape. The outline of the human head can be generally described as being roughly elliptic in nature. In the first step of the proposed system, we have tried the approach of fitting the best Possible ellipse to the outline of the head In the next step, the method based on the human skin color space by selecting flesh tone regions in color images and histogramming their r(＝R/(R＋G＋B)) and g(＝G/R＋G＋B)) values. According to our experiment. the proposed system shows robust location results

• Proceedings of the IEEK Conference
• /
• 1999.11a
• /
• pp.531-535
• /
• 1999

Automatic human face detection in a complex background is one of the difficult problems. In this paper, we propose an effective and robust automatic face detection approach that can locate the face region in natural scene images when the system is used as a pre-processor of a face recognition system . We use two natural and powerful visual cues, the skin color and the eyes. In the first step of the proposed system, the method based on the human skin color space by selecting flesh tone regions using normalized r-g space in color images. In the next step, we extract eye features by calculating moments and using geometrical face model. Experimental results demonstrate that the approach can efficiently detect human faces and satisfactory deal with the problems caused by bad lighting condition, skew face orientation.

• Journal of the Semiconductor & Display Technology
• /
• v.12 no.4
• /
• pp.33-38
• /
• 2013

Image retrieval is one of the most exciting and fastest growing research fields in the area of multimedia technology. As the amount of digital contents continues to grow users are experiencing increasing difficulty in finding specific images in their image libraries. This paper proposes an efficient image descriptor which uses a local color and texture in the non-overlapped block images. To evaluate the performance of the proposed method, we assessed the retrieval efficiency in terms of ANMRR with common image dataset. The experimental trials revealed that the proposed algorithm exhibited a significant improvement in ANMRR, compared to Dominant Color Descriptor and Edge Histogram Descriptor.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.27 no.3
• /
• pp.59-65
• /
• 2004

This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.581-584
• /
• 2002

In this study, an algorithm for dithering true color image to 8-bit indexded color image using Artificial Neural Network was proposed. An adaptive vector quantization algorithm based on Artificial neural network was proposed for dithering color images. To evaluate the proposed algorithm, Mean Square Error(MSE) and quality between original image and dithered image was compared to those of other algorithm. As a results, MSE of proposed algorithm was lower than that of other algorithm used in commercial application and quality of dithered image was also highly improved.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.12
• /
• pp.3149-3165
• /
• 2013

Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

• Journal of the Semiconductor & Display Technology
• /
• v.15 no.2
• /
• pp.43-48
• /
• 2016

In this paper, we propose a traffic light detection and recognition (TLDR) algorithm in the daytime. The proposed algorithm utilizes the color and shape information for the TLDR. At first, a traffic light is detected and recognized based on its shape information. Then, the color range of the detected traffic light is investigated in HSV color space. The input data of the proposed TLDR algorithm is the color image captured using the black box camera during driving. Our simulations demonstrate that the proposed algorithm can achieve a high detection and recognition performance for the images including traffic lights.