• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.57-61
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• 1999

In the field of image compression, Classified Vector Quantization(CVQ) reveals attractive characteristics for preserving perceptual features, such as edges. However, the classification scheme is not generalized to effectively reconstruct different kinds of edge patterns in the original CVQ that predefines several linear-type edge classes: vortical edge horizontal edge diagonal edge classes. In this paper, we propose a new classification scheme, especially for edge blocks based on the similarity measure for edge patterns. An edge block is transformed to a feature vector that describes the detailed shape of the edge pattern The classes for edges are formulated automatically from the training images to result in the generalization of various shapes of edge patterns. The experimental results show the generated linear/nonlinear types of edge classes. The integrity of all the edges is faithfully preserved in the reconstructed image based on the various type of edge codebooks generated at 0.6875bpp.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.85-92
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• 2018

When using complicated image processing algorithms, we use superpixels to reduce computational complexity. Superpixel segmentation is a method of grouping pixels having similar characteristics into one group. Since superpixel is used as a preprocessing of image processing, it should be generated quickly, and the edge components of the image should be well preserved. In this paper, we propose a method of generating superpixels with a small amount of computation while preserving edge components well. In the proposed method, superpixels of an image are generated by using the existing k-mean method, and similar superpixels among the generated superpixels are merged to make final superpixels. When merging superpixels, the similarity is calculated only for superpixels. Therefore, the amount of computation is maintained small. It is shown by experimental results that the superpixel images produced by the proposed method are conserving edge information of the original image better than those produced by the existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.861-866
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• 2007

A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.461-468
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• 2008

The needs for visualizing interactive multimedia contents on portable devices with realistic three dimensional shapes are increasing as new ubiquitous services are coming into reality. Especially in digital fashion applications with virtual reality technologies for clothes of various forms on different avatars, it is required to provide very high quality visual models over mobile networks. Due to limited network bandwidths and memory spaces of portable devices, it is very difficult to transmit visual data effectively and render realistic appearance of three dimensional images. In this thesis, we propose a compression method to reduce three dimensional data for digital fashion applications. The three dimensional model includes animation of avatar which require very large amounts of data over time. Our proposed method utilizes temporal and spatial coherence of animation data, to reduce the amount. By grouping vertices from three dimensional models, the entire animation is represented by a movement path of a few representative vertices. The existing three dimensional model compression approaches can get benefits from the proposed method by reducing the compression sources through grouping. We expect that the proposed method to be applied not only to three dimensional garment animations but also to generic deformable objects.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.1998-2009
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• 1998

An effective algorithm for tracking rigid or non-rigid moving object(s) which segments local moving parts from image sequence in the presence of backgraound motion by camera movenment, predicts the direction of it, and tracks the object is proposed. It requires no camera calibration and no knowledge of the installed position of camera. In order to segment the moving object, feature points configuring the shape of moving object are firstly selected, feature flow field composed of motion vectors of the feature points is computed, and moving object(s) is (are) segmented by clustering the feature flow field in the multi-dimensional feature space. Also, we propose IRMAS, an efficient algorithm that finds the convex hull in order to cinstruct the shape of moving object(s) from clustered feature points. And, for the purpose of robjst tracking the objects whose movement characteristics bring about the abrupt change of moving trajectory, an improved order adaptive lattice structured linear predictor is used.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.191-199
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• 2023

In this paper, we propose a method to extract the features of five sensor-only facilities built as infrastructure for autonomous cooperative driving, which are from point cloud data acquired by LiDAR. In the case of image acquisition sensors installed in autonomous vehicles, the acquisition data is inconsistent due to the climatic environment and camera characteristics, so LiDAR sensor was applied to replace them. In addition, high-intensity reflectors were designed and attached to each facility to make it easier to distinguish it from other existing facilities with LiDAR. From the five sensor-only facilities developed and the point cloud data acquired by the data acquisition system, feature points were extracted based on the average reflective intensity of the high-intensity reflective paper attached to the facility, clustered by the DBSCAN method, and changed to two-dimensional coordinates by a projection method. The features of the facility at each distance consist of three-dimensional point coordinates, two-dimensional projected coordinates, and reflection intensity, and will be used as training data for a model for facility recognition to be developed in the future.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.547-553
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• 2008

We propose a two-stage document layout segmentation method. At the first stage, as top-down segmentation, morphological distance map algorithm extracts a collection of rectangular regions from a given input image. This preliminary result from the first stage is employed as input parameters for the process of next stage. At the second stage, a machine-learning algorithm is adopted RBF network, one of neural networks based on statistical model, is selected. In order for constructing the hidden layer of RBF network, a data clustering technique bared on the self-organizing property of Kohonen network is utilized. We present a result showing that the supervised neural network, trained by 300 number of sample data, improves the preliminary results of the first stage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7302-7308
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• 2014

Detecting target objects robustly in natural environments is a difficult problem in the computer vision and image processing areas. This paper suggests a method of robustly detecting target objects in the environments where reflection exists. The suggested algorithm first captures scenes with a stereo camera and extracts the line and corner features representing the target objects. This method then eliminates the reflected features among the extracted ones using a homographic transform. Subsequently, the method robustly detects the target objects by clustering only real features. The experimental results showed that the suggested algorithm effectively detects the target objects in reflection environments rather than existing algorithms.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.464-468
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• 2009

In this paper we propose an image-based approach, which is different from the traditional flow cytometric method to detect shape of apoptosis cells. This method can overcome the defects of cytometry and give precise recognition of apoptosis cells. In this work K-means clustering was used to do the rough segmentation and an active contour model, called 'snake' was used to do the precise edge detection. And then some features were extracted including physical feature, shape descriptor and texture features of the apoptosis cells. Finally a Mahalanobis distance classifier classifies the segmentation images as apoptosis and non-apoptosis cell.