• 전기학회논문지
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• 제58권12호
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Journal of Multimedia Information System
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• 제9권1호
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• pp.1-10
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• 2022

China is a big country in animal fur industry. The total production and consumption of fur are increasing year by year. However, the recognition of fur in the fur production process still mainly relies on the visual identification of skilled workers, and the stability and consistency of products cannot be guaranteed. In response to this problem, this paper proposes a feature fusion-based animal fur recognition network on the basis of typical convolutional neural network structure, relying on rapidly developing deep learning techniques. This network superimposes texture feature - the most prominent feature of fur image - into the channel dimension of input image. The output feature map of the first layer convolution is inverted to obtain the inverted feature map and concat it into the original output feature map, then Leaky ReLU is used for activation, which makes full use of the texture information of fur image and the inverted feature information. Experimental results show that the algorithm improves the recognition accuracy by 9.08% on Fur_Recognition dataset and 6.41% on CIFAR-10 dataset. The algorithm in this paper can change the current situation that fur recognition relies on manual visual method to classify, and can lay foundation for improving the efficiency of fur production technology.

• 한국인터넷방송통신학회논문지
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• 제13권6호
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• pp.63-69
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• 2013

특징 기반 영상 인식 방법은 객체의 특징을 이용하므로 템플릿 정합에 비해 고속으로 수행될 수 있다. 불변 특징 기반의 파노라마 생성은 영상 인식의 한 응용으로서, 두 영상 간의 특징점 정합에 많은 처리 시간이 필요하다. 본 논문에서는 특징 강도 정보를 이용하여 특징점 정합 속도를 향상시키는 방법을 제안한다. SURF 알고리즘으로 특징점들을 추출한 후, 특징 강도 정보를 계산하여 강한 특징점들을 선택하여 특징 정합에 사용한다. 특징 강도가 강한 특징점들은 그렇지 않은 특징점들 보다 더 의미 있다고 볼 수 있다. 실험에서 $320{\times}240$ 크기의 칼라 영상에 대해 제안한 방법은 특징 강도 정보를 사용하지 않았을 때보다 40% 이상 처리 속도의 향상을 보였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.1799-1802
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• 2003

This paper has been studied a Adaptive feature-factors based fingerprints recognition in many biometrics. we study preprocessing and matching method of fingerprints image in various circumstances by using optical fingerprint input device. The Fingerprint Recognition Technology had many development until now. But, There is yet many point which the accuracy improves with operation speed in the side. First of all we study fingerprint classification to reduce existing preprocessing step and then extract a Feature-factors with direction information in fingerprint image. Also in the paper, we consider minimization of noise for effective fingerprint recognition system.

• 한국HCI학회논문지
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• 제2권2호
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• pp.35-43
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• 2007

본 논문은 특징검출(feature detection)과 특징해석(feature description) 기법을 이용하여, 영상 매칭 (matching)과 인식(recognition)에 필요한 다양한 파라미터의 변화에 따른 인식률의 차이를 분석하기 위한 실험 내용을 다룬다. 본 논문에서는 영상의 특징분석과 매칭프로세스를 위해, Lowe의 SIFT(Scale-Invariant Transform Feature)를 이용하며, 영상에서 나타나는 특징을 검출하고 해석하여 특징 데이터베이스로 구축한다. 특징 데이터베이스는 구글 어스를 통해 획득한 위성영상으로부터 50여개 건물에 대해 구축되는데, 이는 각 건물 영상으로부터 추출된 특징 점들의 좌표와 128차원의 벡터의 값으로 이루어진 특징 해석데이터로 저장된다. 구축된 데이터베이스는 각 건물에 대한 정보가 태그의 형식으로 함께 저장되는데, 이는 카메라로부터 획득한 입력영상과의 비교를 통해 입력영상이 가리키는 지역 내에 존재하는 건물에 대한 정보를 제공하는 역할을 한다. 실험은 영상 매칭과 인식과정에서 작용하는 내-외부적 요소들을 제시하고, 각 요소의 상태변화에 따라 인식률의 차이를 비교하는 방법으로 진행되었으며, 본 연구의 최종적인 시스템은 모바일기기의 카메라를 이용하여 카메라가 촬영하고 있는 지도상의 객체를 인식하고, 해당 객체에 대한 기본적인 정보를 제공할 수 있다.

• Journal of information and communication convergence engineering
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• 제9권4호
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• pp.466-470
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• 2011

This paper proposes the new method based on fuzzy logic which recognizes between normal and abnormal. The object image was the Thyroid Gland cell image that was diagnosed as normal and abnormal(two types of abnormal : follicular neoplastic cell, and papillary neoplastic cell), respectively. The nuclei were successfully diagnosed as normal and abnormal. The multiple feature parameters (pre-obtained 16 feature parameters of image data) were used to extract the features of each nucleus. As a consequence of using fuzzy logic algorithm, proposed in this paper, average recognition rate of 98.25% was obtained.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.322-326
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• 2004

This paper describes a new iris recognition method based on a shift-invariant wavelet sub-images. For the feature representation, we first preprocess an iris image for the compensation of the variation of the iris and for the easy implementation of the wavelet transform. Then, we decompose the preprocessed iris image into multiple subband images using a shift-invariant wavelet transform. For feature representation, we select a set of subband images, which have rich information for the classification of various iris patterns and robust to noises. In order to reduce the size of the feature vector, we quantize. each pixel of subband images using the Lloyd-Max quantization method Each feature element is represented by one of quantization levels, and a set of these feature element is the feature vector. When the quantization is very coarse, the quantized level does not have much information about the image pixel value. Therefore, we define a new similarity measure based on mutual information between two features. With this similarity measure, the size of the feature vector can be reduced without much degradation of performance. Experimentally, we show that the proposed method produced superb performance in iris recognition.

• 한국산업정보학회논문지
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• 제7권3호
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• pp.23-31
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• 2002

본 논문에서는 화상자료의 특성인 이웃 화소간의 종속성을 표현하는데 적합한 깁스분포를 바탕으로 얼굴 표정을 인식을 위한 특징벡터를 추출하는 새로운 방법을 제안하였다. 추출된 특징벡터는 얼굴 이미지의 크기, 위치, 회전에 대하여 불변한 특성을 갖는다. 얼굴 표정을 인식하기 위한 알고리즘은 특징벡터 추출하는 과정과 패턴을 인식하는 두 과정으로 나뉘어진다. 특징벡터는 얼굴 화상에 대하여 추정된 깁스분포를 바탕으로 수정된 2-D 조건부 모멘트로 구성된다. 얼굴 표정인식 과정에서는 패턴인식에 널리 사용되는 이산형 HMM를 사용한다. 제안된 방법에 대한 성능평가를 위하여 4가지의 얼굴 표정 인식 실험을 Workstation에서 실험한 결과, 제안된 얼굴 표정 인식 방법이 95% 이상의 성능을 보여주었다.

• Journal of information and communication convergence engineering
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• 제16권3호
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• pp.160-165
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• 2018

This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.120-121
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• 2006

In this paper, We have designed a face recognition system based on the embedded Linux. This paper has an aim in embedded system to recognize the face more exactly. At first, the contrast of the face image is adjusted with lightening compensation method, the skin and lip color is founded based on YCbCr values from the compensated image. To take advantage of the method based on feature and appearance, these methods are applied to the eyes which has the most highly recognition rate of all the part of the human face. For eyes detecting, which is the most important component of the face recognition, we calculate the horizontal gradient of the face image and the maximum value. This part of the face is resized for fitting the eye image. The image, which is resized for fit to the eye image stored to be compared, is extracted to be the feature vectors using the continuous wavelet transform and these vectors are decided to be whether the same person or not with PNN, to miminize the error rate, the accuracy is analyzed due to the rotation or movement of the face. Also last part of this paper we represent many cases to prove the algorithm contains the feature vector extraction and accuracy of the comparison method.