• Journal of Internet Computing and Services
• /
• v.7 no.3
• /
• pp.143-153
• /
• 2006

Facial feature detection is a basic technology in applications such as human computer interface, face recognition, face tracking and image database management. The speed of feature detection algorithm is one of the main issues for facial feature detection in real-time environment. Primary factors like a variation by lighting effect, location, rotation and complex background give an effect to decrease a detection ratio. A facial feature detection algorithm is proposed to improve the detection ratio and the detection speed. The proposed algorithm detects skin regions over the entire image improved by CLAHE, an algorithm for light compensation against varying lighting conditions. To extract facial feature points on detected skin regions, it uses appearance-based geometrical characteristics of a face. Since the method shows fast detection speed as well as efficient face-detection ratio, it can be applied in real-time application to face tracking and face recognition.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.6
• /
• pp.819-824
• /
• 2021

As technologies related to image processing such as autonomous driving and CCTV develop, fog removal algorithms using a single image are being studied to improve the problem of image distortion. As a method of predicting fog density, there is a method of estimating the depth of an image by generating a depth map, and various fog features may be used as training data of the depth map. In addition, it is essential to implement a hardware capable of processing high-definition images in real time in order to apply the fog removal algorithm to actual technologies. In this paper, we implement NLCV (Normalize Local Coefficient of Variation), a feature of fog based on coefficient of variation, in hardware. The proposed hardware is an FPGA implementation of Xilinx's xczu7ev-2ffvc1156 as a target device. As a result of synthesis through the Vivado program, it has a maximum operating frequency of 479.616MHz and shows that real-time processing is possible in 4K UHD environment.

• Asian-Australasian Journal of Animal Sciences
• /
• v.16 no.11
• /
• pp.1599-1603
• /
• 2003

The physical feature, physiological character and behavior studies were conducted with fifteen newly collected gayals in Bandarban hill tract area of Bangladesh. Their morphology is different from domestic cattle. The range of pulse rate, body temperature and respiration rate were 47 to 75 per minute, 37.78 to $38.88^{\circ}C$ and 20 to 40 per minute, respectively. These physiological values vary with different age group and seasonal variation. In hematological feature, the average findings were RBC $7.01{\pm}0.52$ million/cu.mm, WBC $14.3{\pm}3.69$ thousand/cu.mm, hemoglobin concentration $9.81{\pm}2.25gm%$, PCV $35.86{\pm}3.68%$. In differential WBC count neutrophils $28.23{\pm}1.75%$, lymphocytes $62{\pm}2.05%$, monocytes $4.4{\pm}1.34%$, eosinophils $5{\pm}2.49%$ and basophils $0.4{\pm}0.51%$. In behavior study, the animal shows browsing nature on hill slopes. They are watchful in new environment, become excited and nervous with strangers. Heated female gayals response for mating with domestic bull.

• Journal of Advanced Navigation Technology
• /
• v.13 no.1
• /
• pp.142-150
• /
• 2009

It is very important to generate a simplified model from a complex 3D character in computer game. We propose a new method of extracting feature lines from a 3D game character. Given an unstructured 3D character model containing texture information, we use model feature map (MFM), which is a 2D map that abstracts the variation of texture and curvature in the 3D character model. The MFM is created from both a texture map and a curvature map, which are produced separately by edge-detection to locate line features. The MFM can be edited interactively using standard image-processing tools. We demonstrate the technique on several data sets, including, but not limited to facial character.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.6
• /
• pp.1686-1692
• /
• 1999

This paper describes the feature area-based vehicle plate recognition system(VPRS). For the extraction of vehicle plate in a vehicle image, we used the method which extracts vehicle plate area from a s vehicle image using intensity variation. For the extraction of the feature area containing character from the extracted vehicle plate, we used the histogram-based approach and the relative location information of individual characters in the extracted vehicle plate. The extracted feature area is used as the input vector of ART2 neural network. The proposed method simplifies the existing complex preprocessing the solves the problem of distortion and noise in the binarization process. In the difficult cases of character extraction by binarization process of previous method, our method efficiently extracts characters regions and recognizes it.

• International Journal of Quality Innovation
• /
• v.3 no.2
• /
• pp.113-131
• /
• 2002

The relationships among multi-dimensional data (such as medical examination data) with ambiguity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back-propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi-dimensional feature selection. The first one proposed is a feature selection procedure from the trained back-propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis-Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD: it can deal with a reduced model, which is the focus of this paper In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.3
• /
• pp.19-29
• /
• 2011

Until now, many face recognition methods have been proposed, most of them use a 1-dimensional feature vector which is vectorized the input image without feature extraction process or input image itself is used as a feature matrix. It is known that the face recognition methods using raw image yield deteriorated performance in databases whose have severe illumination changes. In this paper, we propose a face recognition method using local statistics of gradients and correlations which are good for illumination changes. BDIP (block difference of inverse probabilities) is chosen as a local statistics of gradients and two types of BVLC (block variation of local correlation coefficients) is chosen as local statistics of correlations. When a input image enters the system, it extracts the BDIP, BVLC1 and BVLC2 feature images, fuses them, obtaining feature matrix by $(2D)^2$ PCA transformation, and classifies it with training feature matrix by nearest classifier. From experiment results of four face databases, FERET, Weizmann, Yale B, Yale, we can see that the proposed method is more reliable than other six methods in lighting and facial expression.

• Journal of KIISE:Software and Applications
• /
• v.31 no.4
• /
• pp.420-438
• /
• 2004

Artificially or naturally contained texts in the natural images have significant and detailed information about the scenes. If we develop a method that can extract and recognize those texts in real-time, the method can be applied to many important applications. In this paper, we suggest a new method that extracts the text areas in the natural images using the low-level image features of color continuity. gray-level variation and color valiance and that verifies the extracted candidate regions by using the high-level text feature such as stroke. And the two level features are combined hierarchically. The color continuity is used since most of the characters in the same text lesion have the same color, and the gray-level variation is used since the text strokes are distinctive in their gray-values to the background. Also, the color variance is used since the text strokes are distinctive in their gray-values to the background, and this value is more sensitive than the gray-level variations. The text level stroke features are extracted using a multi-resolution wavelet transforms on the local image areas and the feature vectors are input to a SVM(Support Vector Machine) classifier for the verification. We have tested the proposed method using various kinds of the natural images and have confirmed that the extraction rates are very high even in complex background images.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.5
• /
• pp.48-53
• /
• 2001

In speaker verification based on LPC analysis the prediction residues are ignored and LPCC(LPC cepstrum) are only used to compose feature vectors. In this study, LPCC and RCEP (residual cepstrum) extracted from residues are used as feature parameters in the various environmental speaker verification. We propose the weighting function which can enlarge inter-speaker variation by weighting pitch, speaker inherent vector, included in residual cepstrum. Simulation results show that the average speaker verification rate is improved in the rate of 6％ with RCEP and LPCC at the same time and is improved in the rate of 2.45% with the proposed weighted RCEP and LPCC at the same time compared with no weighting.

• The Bulletin of The Korean Astronomical Society
• /
• v.39 no.2
• /
• pp.96.2-96.2
• /
• 2014

Using solar occultation data obtained by Cassini/Visual Infrared Mapping Spectrometer (VIMS), we were able to retrieve the 1 - 5 mm optical-depth spectra of the Titanian haze, for which only selected wavelength and altitudinal ranges were previously analyzed. We found that the gross 1 - 5 mm shapes of the retrieved haze spectra are significantly different from the spectra of tholin samples in the literature. We also derived the vertical variation of the spectral structure of the $3.3-3.4{\mu}m$ absorption feature of the Titan haze from the VIMS data recorded between 250 and 700 km altitude. We found a marked change between 480 and 580 km in the relative amplitudes of the 3.33 and $3.38{\mu}m$ features which are characteristic of aromatic (double C=Cchains or rings) or aliphatic (single C-C chains) structural groups, respectively. Dicussions on this spectral and altitudinal variation will be presented.