• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.90.1-90
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• 2002

Counters for the various kinds of bank notes require high-speed distinctive point extraction and recognition for notes. In this paper we propose a new point extraction and recognition algorithm for bank notes. For distinctive point extraction we use a coordinate data extraction method from specific parts of a bank note representing the same color. The recognition algorithm uses a back-propagation neural network that has coordinate data input. The proposed algorithm is designed to minimize recognition time.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.201-206
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• 2004

Counters for the various kinds of banknotes require high-speed distinctive point extraction and recognition. In this paper we propose a new point extraction and recognition algorithm for Euro banknotes. For distinctive point extraction we use a coordinate data extraction method from specific parts of a banknote representing the same color. To recognize banknotes, we trained 5 neural networks. One is used for inserting direction and the others are used for face value. The algorithm is designed to minimize recognition time by using a minimal amount of recognition data. The simulated results show a high recognition rate and a low training period. The proposed method can be applied to high speed banknote counting machines.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2032-2035
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• 2003

This paper focused on the possibility of face recognition using Flexible let Point Setting method in Gabor Filter Based Face Recognition. Gabor Filter is very sensible to the Texture variation. Therefore, any little change in the face expression or rotation of posture make recognition rate down significantly. A suggested solution for this problem is the Flexible Jet Point Setting. A significant effect of this method is that the number of Jet Point has been reduced from over 150 to under 30 even though the change of recognition rate between two methods is neglectable, Furthermore a set of feature values which results from a set of Gabor filtering became insensible to face variation such as expression, rotation, and light effect. Retinex Algorithm which has been developed by NASA are used as pre-processing.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.46-53
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• 2011

This paper proposes a SURF (Speeded Up Robust Features) based face recognition method which is one of typical interest point extraction algorithms. In general, SURF based object recognition is performed in interest point extraction and matching. In this paper, although, proposed method is employed not only in interest point extraction and matching, but also in face image rotation and interest point verification. image rotation is performed to increase the number of interest points and interest point verification is performed to find interest points which were matched correctly. Although proposed SURF based face recognition method requires more computation time than PCA based one, it shows better recognition rate than PCA algorithm. Through this experimental result, I confirmed that interest point extraction algorithm also can be adopted in face recognition.

• Journal of the Korean Dietetic Association
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• v.6 no.2
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• pp.71-78
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• 2000

Environmental pollution induced by food wastes is considered as one of very serious problems in the world, and it is the most important to reduce the production of food wastes. In this study, environmental campaign for reducing food waste was conducted by applying various campaign methods using such as a bulletin board, intra-network, slogans & posters and news letters, as well as some systems such as penalty and prize in a business & industry foodservice. We investigated customers' recognition and execution degree before and after environmental campaign for the purpose of analyzing the changes of customers' attitude by the campaign. The subjects of this study had generally high level of recognition of environmental problem(3.09 point) compared to the execution degree(1.88 point)(Max. 5 points), implying necessity for the induction of actual execution of food wastes reduction by continuous environmental campaign. After environmental campaign, the recognition of environmental problem related to food wastes was significantly increased from 3.09 point to 3.29 point (p<0.001), and the execution degree for food wastes reduction was also greatly increased from 1.88 to 2.70 point (p<0.001). These changes indicated that campaign for food wastes reduction has raised customers' recognition and execution for environmental protection.

• Speech Sciences
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• v.6
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• pp.219-233
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• 1999

This paper presents a real-time implementation of a speaker independent speech recognition system based on a discrete hidden markov model(DHMM). This system is developed for a car navigation system to design on-chip VLSI system of speech recognition which is used by fixed point Oak DSP core of DSP GROUP LTD. We analyze recognition procedure with C language to implement fixed point real-time algorithms. Based on the analyses, we improve the algorithms which are possible to operate in real-time, and can verify the recognition result at the same time as speech ends, by processing all recognition routines within a frame. A car noise is the colored noise concentrated heavily on the low frequency segment under 400 Hz. For the noise robust processing, the high pass filtering and the liftering on the distance measure of feature vectors are applied to the recognition system. Recognition experiments on the twelve isolated command words were performed. The recognition rates of the baseline recognizer were 98.68% in a stopping situation and 80.7% in a running situation. Using the noise processing methods, the recognition rates were enhanced to 89.04% in a running situation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.797-803
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• 2014

In this paper, 3D face recognition system is designed by using polynomial based on RBFNNs. In case of 2D face recognition, the recognition performance reduced by the external environmental factors such as illumination and facial pose. In order to compensate for these shortcomings of 2D face recognition, 3D face recognition. In the preprocessing part, according to the change of each position angle the obtained 3D face image shapes are changed into front image shapes through pose compensation. the depth data of face image shape by using Multiple Point Signature is extracted. Overall face depth information is obtained by using two or more reference points. The direct use of the extracted data an high-dimensional data leads to the deterioration of learning speed as well as recognition performance. We exploit principle component analysis(PCA) algorithm to conduct the dimension reduction of high-dimensional data. Parameter optimization is carried out with the aid of PSO for effective training and recognition. The proposed pattern classifier is experimented with and evaluated by using dataset obtained in IC & CI Lab.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2585-2598
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• 2015

Hand posture recognition has played a very important role in Human Computer Interaction (HCI) and Computer Vision (CV) for many years. The challenge arises mainly due to self-occlusions caused by the limited view of the camera. In this paper, a robust hand posture recognition approach based on 3D point cloud from two RGB-D sensors (Kinect) is proposed to make maximum use of 3D information from depth map. Through noise reduction and registering two point sets obtained satisfactory from two views as we designed, a multi-viewed hand posture point cloud with most 3D information can be acquired. Moreover, we utilize the accurate reconstruction and classify each point cloud by directly matching the normalized point set with the templates of different classes from dataset, which can reduce the training time and calculation. Experimental results based on posture dataset captured by Kinect sensors (from digit 1 to 10) demonstrate the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.958-964
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• 2015

In this paper, an object recognition method based on the depth information from the RGB-D camera, Xtion, is proposed for an indoor mobile robot. First, the RANdom SAmple Consensus (RANSAC) algorithm is applied to the point cloud obtained from the RGB-D camera to detect and remove the floor points. Next, the removed point cloud is classified by the k-means clustering method as each object's point cloud, and the normal vector of each point is obtained by using the k-d tree search. The obtained normal vectors are classified by the trained multi-layer perceptron as 18 classes and used as features for object recognition. To distinguish an object from another object, the similarity between them is measured by using Levenshtein distance. To verify the effectiveness and feasibility of the proposed object recognition method, the experiments are carried out with several similar boxes.