• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.53-61
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• 2010

In this paper, we present a color interpolation algorithm that uses novel edge direction estimator and region classifier. The proposed edge direction estimator accurately determines the edge direction based on the correlation between the images obtained by the channel separated and down-sampled Bayer color filter array(CFA) pattern. The correlation is defined based on the similarity between the edge direction in the local region of the image and the shifting direction of the images. Also, the region of an image is defined as the flat, the edge, and the pattern-edge regions, where the edges are appeared repeatedly. When all the pixels in the image are classified into the three different regions, each pixel is interpolated horizontally or vertically according to the estimated direction. Experimental results show that the proposed algorithm outperforms the conventional edge-directed methods on objective and subjective criteria.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.159-170
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• 2009

It is very important to extract the expression data and capture a face image from a video for online-based 3D face animation. In recently, there are many researches on vision-based approach that captures the expression of an actor in a video and applies them to 3D face model. In this paper, we propose an automatic data extraction system, which extracts and traces a face and expression data from realtime video inputs. The procedures of our system consist of three steps: face detection, face feature extraction, and face tracing. In face detection, we detect skin pixels using YCbCr skin color model and verifies the face area using Haar-based classifier. We use the brightness and color information for extracting the eyes and lips data related facial expression. We extract 10 feature points from eyes and lips area considering FAP defined in MPEG-4. Then, we trace the displacement of the extracted features from continuous frames using color probabilistic distribution model. The experiments showed that our system could trace the expression data to about 8fps.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.41-51
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• 2010

In the research for driver assistance systems, traffic sign information to the driver must be a very important information. Therefore, the detection system of traffic signs located on the road should be able to handel real-time. To detect the traffic signs, color and shape of traffic signs is to use the information after images obtained using the CCD camera. In the road environment, however, using color information to detect traffic sings will cause many problems due to changes of weather and environmental factors. In this paper, to solve it, the candidate traffic sign regions are detected from road images obtained in a variety of the illumination changes using the HSI eign-color model. And then, using the invariant moment-based SVM classifier to detect traffic signs are proposed. Experimental results show that, traffic sign detection rate is 91%, and the processing time per frame is 0.38sec. Proposed method is useful for real-time intelligent traffic guidance systems can be applied.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.55-59
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• 2008

For effective mobility control of UGV(unmanned ground vehicle), the terrain cover classification is an important component as well as terrain geometry recognition and obstacle detection. The vision based terrain cover classification algorithm consists of pre-processing, feature extraction, classification and post-processing. In this paper, we present a method to classify terrain covers based on the color and texture information. The color space conversion is performed for the pre-processing, the wavelet transform is applied for feature extraction, and the SVM(support vector machine) is applied for the classifier. Experimental results show that the proposed algorithm has a promising classification performance.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1226-1232
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• 2010

A door can serve as a feature for place classification and localization for navigation of a mobile robot in indoor environments. This paper proposes a door detection method based on the recognition of various door handles using the general Hough transform (GHT) and support vector machine (SVM). The contour and color histogram of a door handle extracted from the database are used in GHT and SVM, respectively. The door recognition scheme consists of four steps. The first step determines the region of interest (ROI) images defined by the color information and the environment around the door handle for stable recognition. In the second step, the door handle is recognized using the GHT method from the ROI image and the image patches are extracted from the position of the recognized door handle. In the third step, the extracted patch is classified whether it is the image patch of a door handle or not using the SVM classifier. The door position is probabilistically determined by the recognized door handle. Experimental results show that the proposed method can recognize various door handles and detect doors in a robust manner.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.671-674
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• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.214-216
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• 2006

Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.372-378
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• 2009

For effective mobility control of unmanned ground vehicles in outdoor off-road environments, terrain cover classification technology using passive sensors is vital. This paper presents a novel method far terrain classification based on color and texture information of off-road images. It uses a neural network classifier and wavelet features. We exploit the wavelet mean and energy features extracted from multi-channel wavelet transformed images and also utilize the terrain class spatial coordinates of images to include additional features. By comparing the classification performance according to applied features, the experimental results show that the proposed algorithm has a promising result and potential possibilities for autonomous navigation.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.853-854
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• 2008

The terrain perception technology using passive sensors plays a key role to enhance autonomous mobility for UGV. We present an effective method to classify terrain covers based on the color information. Considering a real-time implementation, neural network is applied for the terrain classifier and wavelet features extracted from the images are used. Test results show that the proposed algorithm has a promising classification performance.