• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.242-249
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• 2011

In this paper, the correspondence matching method of stereo images was proposed by means of sampling projective transformation matrix in planar region of scene. Though this study is based on RANSAC, it does not use uniform distribution by random sampling in RANSAC, but use multi non-uniform computed from difference in positions of feature point of image or templates matching. The existing matching method sampled that the correspondence is presumed to correct by use of the condition which the correct correspondence is almost satisfying, and applied RANSAC by matching the correspondence into one to one, but by sampling in stages in multi probability distribution computed for image in the proposed method, the correct correspondence of high probability can be sampled among multi correspondence candidates effectively. In the result, we could obtain many correct correspondence and verify effectiveness of the proposed method in the simulation and experiment of real images.

• The Magazine of the IEIE
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• v.23 no.12
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• pp.115-125
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using one degree perspective Invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of a simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two locally parallel sloe-lines are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points. Feature points for cross ratio are extracted robustly using a vanishing point and intersection points between two locally parallel side-lines and vertical lines. Also the local position estimation problem has been treated when feature points exist less than 4points in the viewed scene. The robustness and feasibility of our algorithms have been demonstrated through real world experiments In Indoor environments using an indoor mobile robot, KASIRI-II(KAist Simple Roving Intelligence).

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.175-182
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• 2017

In this paper, we present a multi-depth generation method using a time-of-flight (ToF) fusion camera system. Multi-view color cameras in the parallel type and ToF depth sensors are used for 3D scene capturing. Although each ToF depth sensor can measure the depth information of the scene in real-time, it has several problems to overcome. Therefore, after we capture low-resolution depth images by ToF depth sensors, we perform a post-processing to solve the problems. Then, the depth information of the depth sensor is warped to color image positions and used as initial disparity values. In addition, the warped depth data is used to generate a depth-discontinuity map for efficient stereo matching. By applying the stereo matching using belief propagation with the depth-discontinuity map and the initial disparity information, we have obtained more accurate and stable multi-view disparity maps in reduced time.

• Journal of electromagnetic engineering and science
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• v.17 no.2
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• pp.51-56
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• 2017

This study proposes a modeling process of equivalent terrains to reduce the computational load and time of a full-wave electromagnetic (EM) simulation. To verify the suitability of the proposed process, an original terrain model with a size of $3m{\times}3m$ is equivalently quantized based on the minimum range resolution of a radar, and the radar image of the quantized model is compared with that of the original model. The results confirm that the simulation time can be reduced from 407 hours to 162 hours without a significant distortion of the radar images, and an average estimation error of the quantized model (20.4 mm) is similar to that of the original model (20.3 mm).

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.127-131
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• 2007

This paper presents a new matching scheme for creating a single panoramic image from a sequence of partially overlapping images of the same object or scene. This matching scheme is based directly on the searching algorithm, using a multiscale approach to the Hooke-Jeeves algorithm. Matching scheme evaluation was performed using simulated pattern images. The proposed matching scheme reveals good results and could be effectively applied to real ultrasound applications.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.177-180
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• 2019

To improve the accuracy rate of guided weapons, many studies have been conducted on the accurate detection and identification of targets from sea clutter. Because of the variety and complicated characteristics of both sea-clutter and target signals, an active target recognition technique is required. In this study, we propose an algorithm to distinguish clutter and recognize targets by applying a fractal signature(FS) classifier, which is a fractal dimension, and a high-resolution target image(HRTI) classifier, which applies scene matching to an image formed from a scanned image. Simulation results using the algorithm revealed that the HRTI classifier recognized targets 1 and 2 at a 100 % rate, whereas the FS classifier recognized targets 1 and 2 at rates of 90 % and 93 %, respectively.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.1
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• pp.1-9
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• 2015

Template matching is used for many applications in image processing. One of the most researched topics is object tracking. Normalized Cross Correlation (NCC) is the basic statistical approach to match images. NCC is used for template matching or pattern recognition. A template can be considered from a reference image, and an image from a scene can be considered as a source image. The objective is to establish the correspondence between the reference and source images. The matching gives a measure of the degree of similarity between the image and the template. A problem with NCC is its high computational cost and occasional mismatching. To deal with this problem, this paper presents an algorithm based on the Sum of Squared Difference (SSD) and an adaptive template matching to enhance the quality of the template matching in object tracking. The SSD provides low computational cost, while the adaptive template matching increases the accuracy matching. The experimental results showed that the proposed algorithm is quite efficient for image matching. The effectiveness of this method is demonstrated by several situations in the results section.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.7
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• pp.59-69
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• 1993

In this paper, a new matching strategy for 3-D object recognition, based on the Surface Normal Images (SNIs), is proposed. The matching strategy using the similarity decision function [9,10] lost the efficiency and the reliability of matching, because all features of models within model base must be compared with the scene object features, and the weights of the attributes of features is given by heuristic manner. However, the proposed matching strategy can solve these problems by using a new approach. In the approach, by searching the model base, a model object whose features are fully matched with the features of sceme object is selected. In this paper, the model base is constructed for the total 26 objects, and systhetic and real range images are used in the test of the system operation. Experimental result is performed to show the possibility that this strategy can be effectively used for the SNI based recognition.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.92-94
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• 2008

In this paper we describe an approach for template based detection and tracking of objects by chamfer matching in real time video. Detecting and tracking of any objects is the key problem in computer vision. In our case we try for hand and head of human for detection and tracking by chamfer matching technique. Matching involves correlating the templates with the distance transformed scene and determining the locations where the mismatch is below a certain user defined threshold.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.55-62
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• 1998

Matching two images is an essential step for many computer vision applications. A new approach to the scale and rotation invariant scene matching, using linear features, is presented. Scene or model images are described by a set of linear features approximating edge information, which can be obtained by the conventional edge detection, thinning, and piecewise linear approximation. A set of candidate parameters are hypothesized by mapping the angular difference and a new distance measure to the Hough space and by detecting maximally consistent points. These hypotheses are verified by a fast linear feature matching algorithm composed of a single-step relaxation and a Hough technique. The proposed method is shown to be much faster than the conventional one where the relaxation process is repeated until convergence, while providing matching performance robust to the random alteration of the linear features, without a priori information on the geometrical transformation parameters.