• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1136-1140
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• 2007

In this paper dose a laver surface check using a real time image process. This system does false retrieval of a laver at a laver production line. At first, a laver image was read in real time using a CCD camera. In this paper, we use an area scan CCD camera. Image is converted into a binary code image using a high-speed imaging process board afterwards. A laver feature is extracted by a binary code image. Surface false retrieval is finally executed using a laver feature. In this paper, we use an area feature of a laver image.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.6
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• pp.601-606
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• 2013

3D scanning is a technique to measure the 3D shape information of the object. Shape information obtained by 3D scanning is expressed either as point cloud or as polygon mesh type data that can be widely used in various areas such as reverse engineering and quality inspection. 3D scanning should be performed as accurate as possible since the scanned data is highly required to detect the features on an object in order to scan the shape of the object more precisely. In this study, we propose the method on finding the location of feature more accurately, based on the extended Biplane SNAKE with global optimization. In each iteration, we project the feature lines obtained by the extended Biplane SNAKE into each image plane and move the feature lines to the features on each image. We have applied this approach to real models to verify the proposed optimization algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.3
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• pp.203-216
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• 2003

In this paper a new feature extraction and target classification method is proposed for the recognition part of FLIR(Forwar Looking Infrared)-image-based ATR system. Proposed feature extraction method is "cluster(=set of classes)-based"version of previous fisherfaces method that is known by its robustness to illumination changes in face recognition. Expecially introduced class clustering and cluster-based projection method maximizes the performance of fisherfaces method. Proposed target image classification method is based on the mixture of experts model which consists of RBF-type experts and MLP-type gating networks. Mixture of experts model is well-suited with ATR system because it should recognizee various targets in complexed feature space by variously mixed conditions. In proposed classification method, one expert takes charge of one cluster and the separated structure with experts reduces the complexity of feature space and achieves more accurate local discrimination between classes. Proposed feature extraction and classification method showed distinguished performances in recognition test with customized. FLIR-vehicle-image database. Expecially robustness to pixelwise sensor noise and un-wanted intensity variations was verified by simulation.

• Korean Journal of Cognitive Science
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• v.11 no.2
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• pp.53-58
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• 2000

This paper reconstructs local region of a facial expression image from extracted feature points of facial expression image using FCM(Fuzzy C-Meang) clustering algorithm with Gabor wavelets. The feature extraction in a face is two steps. In the first step, we accomplish the edge extraction of main components of face using average value of 2-D Gabor wavelets coefficient histogram of image and in the next step, extract final feature points from the extracted edge information using FCM clustering algorithm. This study presents that the principal components of facial expression images can be reconstructed with only a few feature points extracted from FCM clustering algorithm. It can also be applied to objects recognition as well as facial expressions recognition.

• The Journal of the Korea Contents Association
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• v.3 no.1
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• pp.9-13
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• 2003

We propose a block motion estimation algorithm based on a statistical image feature for video sequences. The statistical feature of the reference block is obtained, then applied to select the candidate starting points (SPs) in the regular starting points pattern (SPP) by comparing the statistical feature of reference block with that of blocks which are spread ower regular SPP. The final SPs are obtained by their Mean Absolute Difference(MAD) value among the candidate SPs. Finally, one of conventional fast search algorithms, such as BRGDS, DS, and three-step search (TSS), has been applied to generate the motion vector of reference block using the final SPs as its starting points. The experimental results showed that the starting points from fine SPs were as dose as to the global minimum as we expected.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.8
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• pp.759-766
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• 2009

The purpose of this paper is an image processing algorithm development as a base research achieving performance enhancement of integrated navigation system. We used the SIFT (Scale Invariant Feature Transform) algorithm for image processing, and developed feature point matching filter for rejecting mismatched points. By applying the proposed algorithm, it is obtained better result than other methods of parameter tuning and KLT based feature point tracking. For further study, integration with INS and algorithm optimization for the real-time implementation are under investigation.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2036-2038
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• 2001

In this paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using vision system. In the proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Also, we use R,G,B(red, green, blue) color image data and the gray image data to get the highly trust rate of feature point extraction. For this, we propose an algorithm to extract four feature points (eyebrow, eye, nose, mouth) from the face image acquired by the color CCD camera and find some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector(position and distance among the feature points). Finally, we show the practical application possibility of the proposed method.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.319-334
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• 2001

Extracting primitives from imagery plays an important task in visual information processing since the primitives provide useful information about characteristics of the objects and patterns. The human visual system utilizes features without difficulty for image interpretation, scene analysis and object recognition. However, to extract and to analyze feature are difficult processing. The ultimate goal of digital image processing is to extract information and reconstruct objects automatically. The objective of this study is to develop robust method to achieve the goal of the image processing. In this study, an adaptive strategy was developed by implementing Gabor filters in order to extract feature information and to segment images. The Gabor filters are conceived as hypothetical structures of the retinal receptive fields in human vision system. Therefore, to develop a method which resembles the performance of human visual perception is possible using the Gabor filters. A method to compute appropriate parameters of the Gabor filters without human visual inspection is proposed. The entire framework is based on the theory of human visual perception. Digital images were used to evaluate the performance of the proposed strategy. The results show that the proposed adaptive approach improves performance of the Gabor filters for feature extraction and segmentation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.51-58
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• 2009

In this paper. we propose search algorithm using Low Peak Feature of audio and image color value by which similar movies can be identified. Combing through entire video files for the purpose of recognizing and retrieving matching movies requires much time and memory space. Moreover, these methods still share a critical problem of erroneously recognizing as being different matching videos that have been altered only in resolution or converted merely with a different codec. Thus we present here a similar-video-retrieval method that relies on analysis of audio patterns, whose peak features are not greatly affected by changes in the resolution or codec used and image color values. which are used for similarity comparison. The method showed a 97.7% search success rate, given a set of 2,000 video files whose audio-bit-rate had been altered or were purposefully written in a different codec.