• Journal of Digital Contents Society
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• v.10 no.2
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• pp.319-326
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• 2009

In this paper, we present an algorithm which recognize hand shape in real time using only image without adhering separate sensor. Hand recognizes using edge orientation histogram, which comes under a constant quantity of 2D appearances because hand shape is intricate. This method suit hand pose recognition in real time because it extracts hand space accurately, has little computation quantity, and is less sensitive to lighting change using color information in complicated background. Method which reduces recognition error using principal component analysis(PCA) method to can recognize through hand shape presentation direction change is explained. A case that hand shape changes by turning 3D also by using this method is possible to recognize. Human interface system manufacture technique, which controls a home electric appliance or game using, suggested method at experience could be applied.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.2
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• pp.16-22
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• 2001

In this paper we mose a new scheme for recognition of deformed letters by extracting feature vectors based on Gibbs distributions which are well suited for representing the spatial continuity. The extracted feature vectors are comprised of 2-D conditional moments which are invariant under translation, rotation, and scale of an image. The Algorithm for pattern recognition of deformed letters contains two parts: the extraction of feature vector and the recognition process. (i) We extract feature vector which consists of an improved 2-D conditional moments on the basis of estimated conditional Gibbs distribution for an image. (ii) In the recognition phase, the minimization of the discrimination cost function for a deformed letters determines the corresponding template pattern. In order to evaluate the performance of the proposed scheme, recognition experiments with a generated document was conducted. on Workstation. Experiment results reveal that the proposed scheme has high recognition rate over 96%.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.1-6
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• 1999

We propose a new algorithm for pattern classification by extracting feature vectors based on Gibbs distributions which are well suited for representing the characteristic of an images. The extracted feature vectors are comprised of 2-D moments which are invariant under translation rotation, and scale of the image less sensitive to noise. This implementation contains two puts: feature extraction and pattern classification First of all, we extract feature vector which consists of an improved 2-D moments on the basis of estimated Gibbs distribution Next, in the classification phase the minimization of the discrimination cost function for a specific pattern determines the corresponding template pattern. In order to evaluate the performance of the proposed scheme, classification experiments with training document sets of characters have been carried out on SUN ULTRA 10 Workstation Experiment results reveal that the proposed scheme had high classification rate over 98%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.1
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• pp.35-45
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• 1991

Multi layer neural network using backpropagation learning algorithm is used to achieve identification and orientation estimation of different classes of aircraft in the variety of 3-D orientations. In-plane distortion invarient$(L,\;{\Phi})$ feature was extracted from each aircraft image to be used for training neural network aircraft classifier. For aircraft identification the optimum structure of the neural network classifier is studied to obtain high classification performance. Effective reductioin of learning time was achieved by using modified backpropagation learning algorithm and varying, learning parameters.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.309-317
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• 2022

In this paper, we propose a machine learning-based copy-move forgery detection network with dual branches. Because the rotation or scaling operation is frequently involved in copy-move forger, the conventional convolutional neural network is not effectively applied in detecting copy-move tampering. Therefore, we divide the input into rotation-invariant and scaling-invariant features based on the wavelet coefficients. Each of the features is input to different branches having the same structure, and is fused in the combination module. Each branch comprises feature extraction, correlation, and mask decoder modules. In the proposed network, VGG16 is used for the feature extraction module. To check similarity of features generated by the feature extraction module, the conventional correlation module used. Finally, the mask decoder model is applied to develop a pixel-level localization map. We perform experiments on test dataset and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.221-232
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• 2012

The double-density discrete wavelet transform(DWT) is an improvement upon the critically sampled DWT with important additional properties. It employs one scaling function and two distinct wavelets, which are designed to be offset from one another by one half. And it is overcomplete by a factor of two. Also, this transformation is nearly shift-invariant. But there is room for improvement because not all of the wavelets are directional. That is, although the double-density DWT utilizes more wavelets, some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. Proposed method is a DWT that combines the double-density DWT and quincunx sampling, each of which has its own characteristics and advantages. Especially, the quincunx sampling treats the different directions more homogeneously. As a result, since proposed method can generate sub-images of multiple degrees rotated versions, this method provides an improved performance in image processing fields.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1692-1700
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• 2009

This paper proposes the direction distribution of surface normal vectors as a feature descriptor of three-dimensional models. Proposed the feature descriptor handles rotation invariance using a principal component analysis(PCA) method, and performs mesh simplification to make it robust and nonsensitive against noise addition. Our method picks samples for the distribution of normal vectors to be proportional to the area of each polygon, applies weight to the normal vectors, and applies interpolation to enhance discrimination so that the information on the surface with less area may be less reflected on composing a feature descriptor. This research measures similarity between models with a L1-norm in the probability density histogram where the distances of feature descriptors are normalized. Experimental results have shown that the proposed method has improved the retrieval performance described in an average normalized modified retrieval rank(ANMRR) by about 17.2% and the retrieval performance described in a quantitative discrimination scale by 9.6%~17.5% as compared to the existing method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.411-419
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• 2013

Research on developing algorithms for building modeling such as extracting outlines of the buildings and segmenting patches of the roofs using aerial images or LiDAR data are active. However, utilizing information from the building model is not well implemented yet. This study aims to propose a scheme for search identical or similar shape of buildings by utilizing graph topology pattern matching under the assumptions: (1) Buildings were modeled beforehand using imagery or LiDAR data, or (2) 3D building data from digital maps are available. Side walls, segmented roofs and footprints were represented as nodes, and relationships among the nodes were defined using graph topology. Topology graph database was generated and pattern matching was performed with buildings of various shapes. The results show that efficiency of the proposed method in terms of reliability of matching and database structure. In addition, flexibility in the search was achieved by altering conditions for the pattern matching. Furthermore, topology graph representation could be used as scale and rotation invariant shape descriptor.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.394-400
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• 2012

SURF is a robust local invariant feature descriptor and has been used in many applications such as object recognition. Even though this algorithm has similar matching accuracy compared to the SIFT, which is another popular feature extraction algorithm, it has advantage in matching time. However, these descriptors do not consider relative location information of extracted interesting points to guarantee rotation invariance. Also, since they use gray image of original color image, they do not use the color information of images, either. In this paper, we propose a method for improving matching performance of SURF descriptor using the color and relative location information of interest points. The location information is built from the angles between the line connecting the centers of interest points and the orientation line constructed for the center of each interest points. For the color information, color histogram is constructed for the region of each interest point. We show the performance of our scheme through experiments.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.345-355
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• 2008

SIFT is popularly used in computer vision application such as object recognition, motion tracking, and 3D reconstruction among various shape descriptors. However, it is not easy to apply SIFT into the image similarity search as it is since it uses many high dimensional keypoint vectors. In this paper, we present a SIFT based image similarity search method using an edge image pyramid and an interesting region detection. The proposed method extracts keypoints, which is invariant to contrast, scale, and rotation of image, by using the edge image pyramid and removes many unnecessary keypoints from the image by using the hough transform. The proposed hough transform can detect objects of ellipse type so that it can be used to find interesting regions. Experimental results demonstrate that the retrieval performance of the proposed method is about 20% better than that of traditional SIFT in average recall.