• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.6
• /
• pp.81-89
• /
• 2004

In this paper, we compare face recognition rate by PCA algorithm using distance change and embedded data being input left side and right side image in stereo images. The proposed method detects face region from RGB color space to YCbCr color space. Also, The extracted face image's scale up/down according to distance change and extracts more robust face region. The proposed method through an experiment could establish standard distance (100cm) in distance about 30∼200cm, and get 99.05% (100cm) as an average recognition result by scale change. The definition of super state is specification region in normalized size (92${\times}$112), and the embedded data extracts the inner factor of defined super state, achieved face recognition through PCA algorithm. The orignal images can receive specification data in limited image's size (92${\times}$112) because embedded data to do learning not that do all learning, in image of 92${\times}$112 size averagely 99.05%, shows face recognition rate of test 1 99.05%, test 2 98.93%, test 3 98.54%, test 4 97.85%. Therefore, the proposed method through an experiment showed that if apply distance change rate could get high recognition rate, and the processing speed improved as well as reduce face information.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.4
• /
• pp.68-74
• /
• 2017

This paper presents a joint demosaicking and arbitrary-ratio down sampling algorithm for color filter array (CFA) images. Color demosaiking is a necessary part of image signal processing pipeline for many types of digital image recording system using single sensor. Also, such as smart phone, obtained high resolution image from image sensor has to be down-sampled to be displayed on the screen. The conventional solution is "Demosaicking first and down sampling later". However, this scheme requires a significant amount of memory and computational cost. Also, artifacts can be introduced or details get damaged during demosaicking and down sampling process. In this paper, we propose a method in which demosaicking and down sampling are working simultaneously. We use inverse mapping of Bayer CFA and then joint demosaicking and down sampling with arbitrary-ratio scheme based on signal decomposition of high and low frequency component in input data. Experimental results show that our proposed algorithm has better image quality performance and much less computational cost than those of conventional solution.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.6
• /
• pp.267-274
• /
• 2023

In this paper, a method for estimating the illuminant chromaticity of a scene for an input image is proposed. The illuminant chromaticity is estimated using the illuminant reference region. The conventional method uses a certain number of reference lighting information. By comparing the chromaticity distribution of pixels from the input image with the chromaticity set prepared in advance for the reference illuminant, the reference illuminant with the largest overlapping area is regarded as the scene illuminant for the corresponding input image. In the process of calculating the overlapping area, the weights for each reference light were applied in the form of a Gaussian distribution, but a clear standard for the variance value could not be presented. The proposed method extracts an independent reference chromaticity region from a given reference illuminant, calculates the characteristic values in the r-g chromaticity plane of the RGB color coordinate system for all pixels of the input image, and then calculates the independent chromaticity region and features from the input image. The similarity is evaluated and the illuminant with the highest similarity was estimated as the illuminant chromaticity component of the image. The performance of the proposed method was evaluated using the database image and showed an average of about 60% improvement compared to the conventional basic method and showed an improvement performance of around 53% compared to the conventional Gaussian weight of 0.1.

• The Korean Journal of Community Living Science
• /
• v.24 no.2
• /
• pp.251-258
• /
• 2013

Korean oil and honey Yukwa has been paid attention as formal cake for traditional national seasons' holiday and religious service. Quality of Yukwa, however, has been maintained arbitrarily by each Yukwa manufacturer. Since even same Yukwa had severe differences in size, weight, and pattern, it has given the negative effect to the consumer. Yukwa industries need to setup the quantitative quality specifications instead of qualitative ones to maintain the uniformity of Yukwa quality. Efficient and economical inspection and process control system should be developed. In developing quality standards of Yukwa, features which can measure quality quantitatively in real time should be properly chosen. Existing quality features such as acidity, oxidization, hardness, viscosity, and texture were measured by the chemical or physical base destructive methods. Many research and developments have been performed in investigating and analyzing chemical transition states of those quality features as environment or storage condition changes. Most methods, however, require either off-line or complex treatment or time consuming process of analysis in evaluating quality features. Consumer, however, selects products mostly based on the external features such as shape, size, and color. Therefore, critical visual quality features should be chosen and the efficient real time measurement system must be developed. In this paper, computer image acquisition and processing system were developed and software modules were developed to extract the quantitative data of those features in real-time. Computer image processing system will promote in maintaining uniform quality of Yukwa and establishing quality standards of Yukwa.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.90-93
• /
• 2006

The first Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meteorological Satellite (COMS) is scheduled for launch in 2008. GOCI includes the eight visible-to-near-infrared (NIR) bands, 0.5km pixel resolution, and a coverage region of 2500 ${\times}$ 2500km centered at 36N and 130E. GOCI has had the scope of its objectives broadened to understand the role of the oceans and ocean productivity in the climate system, biogeochemical variables, geological and biological response to physical dynamics and to detect and monitor toxic algal blooms of notable extension through observations of ocean color. The special feature with GOCI is that like MODIS, MERIS and GLI, it will include the band triplets 660-680-745 for the measurements of sun-induced chlorophyll-a fluorescence signal from the ocean. The GOCI will provide SeaWiFS quality observations with frequencies of image acquisition 8 times during daytime and 2 times during nighttime. With all the above features, GOCI is considered to be a remote sensing tool with great potential to contribute to better understanding of coastal oceanic ecosystem dynamics and processes by addressing environmental features in a multidisciplinary way. To achieve the objectives of the GOCI mission, we develop the GOCI Data Processing System (GDPS) which integrates all necessary basic and advanced techniques to process the GOCI data and deliver the desired biological and geophysical products to its user community. Several useful ocean parameters estimated by in-water and other optical algorithms included in the GDPS will be used for monitoring the ocean environment of Korea and neighbouring countries and input into the models for climate change prediction.

• Journal of KIISE:Computer Systems and Theory
• /
• v.35 no.7
• /
• pp.305-317
• /
• 2008

As a mobile computing environment is rapidly changing, increasing user demand for multimedia-over-wireless capabilities on embedded processors places constraints on performance, power, and sire. In this regard, this paper proposes color media instructions (CMI) for single instruction, multiple data (SIMD) parallel processors to meet the computational requirements and cost goals. While existing multimedia extensions store and process 48-bit pixels in a 32-bit register, CMI, which considers that color components are perceptually less significant, supports parallel operations on two-packed compressed 16-bit YCbCr (6 bit Y and 5 bits Cb, Cr) data in a 32-bit datapath processor. This provides greater concurrency and efficiency for YCbCr data processing. Moreover, the ability to reduce data format size reduces system cost. The reduction in data bandwidth also simplifies system design. Experimental results on a representative SIMD parallel processor architecture show that CMI achieves an average speedup of 6.3x over the baseline SIMD parallel processor performance. This is in contrast to MMX (a representative Intel's multimedia extensions), which achieves an average speedup of only 3.7x over the same baseline SIMD architecture. CMI also outperforms MMX in both area efficiency (a 52% increase versus a 13% increase) and energy efficiency (a 50% increase versus an 11% increase). CMI improves the performance and efficiency with a mere 3% increase in the system area and a 5% increase in the system power, while MMX requires a 14% increase in the system area and a 16% increase in the system power.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.4
• /
• pp.897-904
• /
• 2005

Face Recognition has been an active research area because it is not difficult to acquire face image data and it is applicable in wide range area in real world. Due to the high dimensionality of a face image space, however, it is not easy to process the face images. In this paper, we propose a method to reduce the dimension of the facial data and extract the features from them. It will be solved using the method which extracts the features from holistic face images. The proposed algorithm consists of two parts. The first is the using of principal component analysis (PCA) to transform three dimensional color facial images to one dimensional gray facial images. The second is integrated linear discriminant analusis (PCA+LDA) to prevent the loss of informations in case of performing separated steps. Integrated LDA is integrated algorithm of PCA for reduction of dimension and LDA for discrimination of facial vectors. First, in case of transformation from color image to gray image, PCA(Principal Component Analysis) is performed to enhance the image contrast to raise the recognition rate. Second, integrated LDA(Linear Discriminant Analysis) combines the two steps, namely PCA for dimensionality reduction and LDA for discrimination. It makes possible to describe concise algorithm expression and to prevent the information loss in separate steps. To validate the proposed method, the algorithm is implemented and tested on well controlled face databases.

• Journal of Korea Multimedia Society
• /
• v.5 no.3
• /
• pp.281-289
• /
• 2002

Few studies have been systematically pursued on a multimedia data mining in despite of the over-whelming amounts of multimedia data by the development of computer capacity, storage technology and Internet. Based on the preliminary image processing and content-based image retrieval technology, this paper presents the methods for discovering association rules from recurrent items with spatial relationships in huge data repositories. Furthermore, multimedia mining algorithm is proposed to find implicit association rules among objects of which content-based descriptors such as color, texture, shape and etc. are recurrent and of which descriptors have spatial relationships. The algorithm with recurrent items in images shows high efficiency to find set of frequent items as compared to the Apriori algorithm. The multimedia association-rules algorithm is specially effective when the collection of images is homogeneous and it can be applied to many multimedia-related application fields.

• The KIPS Transactions:PartB
• /
• v.8B no.5
• /
• pp.515-522
• /
• 2001

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Map(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented about k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.5
• /
• pp.1938-1962
• /
• 2015

Security of information during transmission is a major issue in this modern era. All of the communicating bodies want confidentiality, integrity, and authenticity of their secret information. Researchers have presented various schemes to cope with these Internet security issues. In this context, both steganography and cryptography can be used effectively. However, major limitation in the existing steganographic methods is the low-quality output stego images, which consequently results in the lack of security. To cope with these issues, we present an efficient method for RGB images based on gray level modification (GLM) and multi-level encryption (MLE). The secret key and secret data is encrypted using MLE algorithm before mapping it to the grey-levels of the cover image. Then, a transposition function is applied on cover image prior to data hiding. The usage of transpose, secret key, MLE, and GLM adds four different levels of security to the proposed algorithm, making it very difficult for a malicious user to extract the original secret information. The proposed method is evaluated both quantitatively and qualitatively. The experimental results, compared with several state-of-the-art algorithms, show that the proposed algorithm not only enhances the quality of stego images but also provides multiple levels of security, which can significantly misguide image steganalysis and makes the attack on this algorithm more challenging.