• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2581-2584
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• 2003

In this paper, we propose a self-localization algorithm using vertical line segments. Indoor environment is consist of horizontal and vertical line features such as doors, furniture, and so on. From the input image, vertical line edges are detected by an edge operator, Then, line segments are obtained by projecting edge image vertically and detecting local maximum from the projected histogram. From the relation of horizontal position of line segments and the location of the robot, nonlinear equations are come out Localization is done by solving the equations by using Newton's method. Experimental results show that the proposed algorithm using one camera is simple and applicable to indoor environment.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1405-1408
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• 1987

X-ray filmed data converted by Image digitizer were processed through the digital computer for edge detections. Sobel operator with the high-pass and the low-pass filtering of the histogram of the images were applied to this work and 3 examples were processed.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.423-428
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• 2005

This paper proposes a new edge detection method using a $3{\times}3$ ideal binary pattern and lookup table (LUT) for the mobile robot localization without any parameter adjustments. We take the mean of the pixels within the $3{\times}3$ block as a threshold by which the pixels are divided into two groups. The edge magnitude and orientation are calculated by taking the difference of average intensities of the two groups and by searching directional code in the LUT, respectively. And also the input image is not only partitioned into multiple groups according to their intensity similarities by the histogram, but also the threshold of each group is determined by fuzzy reasoning automatically. Finally, the edges are determined through non-maximum suppression using edge confidence measure and edge linking. Applying this edge detection method to the mobile robot localization using projective invariance of the cross ratio. we demonstrate the robustness of the proposed method to the illumination changes in a corridor environment.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.147-159
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• 2002

This paper proposes a method to recognize the noisiness of road images connected with the extraction of lane-related information in order to prevent the usage of erroneous information. The proposed method uses a fuzzy neural network(FNN) with the back-Propagation loaming algorithm. The U decides road images good or bad with respect to visibility of lane marks on road images. Most input parameters to the FNN are extracted from an edge distribution function(EDF), a function of edge histogram constructed by edge phase and norm. The shape of the EDF is deeply correlated to the visibility of lane marks of road image. Experimental results obtained by simulations with real images taken by various lighting and weather conditions show that the proposed method was quite successful, providing decision-making of noisiness with about 99%.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.1
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• pp.11-16
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• 2006

To manipulate large video databases, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-w]so user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm to extract key frames using color histograms and to match the video sequences using edge features. To effectively match video sequences with low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with several real sequences show that the proposed video retrieval algorithm using color and edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

• International Journal of Automotive Technology
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• v.8 no.4
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• pp.493-504
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• 2007

This paper proposes a stereo vision-based forward obstacle detection and distance measurement method. In general, stereo vision-based obstacle detection methods in automotive applications can be classified into two categories: IPM (Inverse Perspective Mapping)-based and disparity histogram-based. The existing disparity histogram-based method was developed for stop-and-go applications. The proposed method extends the scope of the disparity histogram-based method to highway applications by 1) replacing the fixed rectangular ROI (Region Of Interest) with the traveling lane-based ROI, and 2) replacing the peak detection with a constant threshold with peak detection using the threshold-line and peakness evaluation. In order to increase the true positive rate while decreasing the false positive rate, multiple candidate peaks were generated and then verified by the edge feature correlation method. By testing the proposed method with images captured on the highway, it was shown that the proposed method was able to overcome problems in previous implementations while being applied successfully to highway collision warning/avoidance conditions, In addition, comparisons with laser radar showed that vision sensors with a wider FOV (Field Of View) provided faster responses to cutting-in vehicles. Finally, we integrated the proposed method into a longitudinal collision avoidance system. Experimental results showed that activated braking by risk assessment using the state of the ego-vehicle and measuring the distance to upcoming obstacles could successfully prevent collisions.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.39-42
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• 1994

An automatic multi-threshold algorithm for segmentation of 2D ultrasound images based on average filtering and the characteristics of speckle noise in 2D ultrasound image is proposed. To do this, we investigate the histogram of difference between $7{\times}7$ averaging histogram and $3{\times}3$ averaging histogram. And, we find zero crossing points in the positive portion of the differenced histogram and select middle points of the zero crossing points. We assign these selected points to characteristic points. The thresholds are the center of two characteristic points. Then we segment 2D ultrasound image by using these thresholds and extract edges from applying edge operator to optimal segmented image. Experimental results show that the segmented regions are devided accurately around the homogeneous region.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.316-324
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• 2012

In this paper, a novel descreening method using HOG(histogram of gradient)-based adaptive smoothing filter is proposed. Conventional edge-oriented smoothing methods does not provide enough smoothing to the halftone image due to the edge-like characteristic of the halftone noise. Moreover, clustered-dot halftoning method, which is commonly used in printing tends to create Moire pattern because of the intereference in color channels. Therefore, the proposed method uses HOG to distinguish edges and the amount of smoothing to be performed on the halftone image is then calculated according to the magnitude of the HOG in the edge and edge normal orientation. The proposed method was tested on various scanned halftone materials, and the results show that it effectively removes halftone noises as well as Moire pattern while preserving image details.

• International Journal of Automotive Technology
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• v.6 no.2
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• pp.171-181
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• 2005

The purpose of this paper is two-fold: 1) A novel algorithm in order to extract lane-related information from road images is presented; 2) Design specifications of an image processing onboard unit capable of extracting lane­related information in real-time is also presented. Obtaining precise information from road images requires many features due to the effects of noise that eventually leads to long processing time. By exploiting a FPGA and DSP, we solve the problem of real-time processing. Due to the fact that image processing of road images relies largely on edge features, the FPGA is adopted in the hardware design. The schematic configuration of the FPGA is optimized in order to perform 3 $\times$ 3 Sobel edge extraction. The DSP carries out high-level image processing of recognition, decision, estimation, etc. The proposed algorithm uses edge features to define an Edge Distribution Function (EDF), which is a histogram of edge magnitude with respect to the edge orientation angle. The EDF enables the edge-related information and lane-related to be connected. The performance of the proposed system is verified through the extraction of lane-related information. The experimental results show the robustness of the proposed algorithm and a processing speed of more than 25 frames per second, which is considered quite successful.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.137-140
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• 2001

This paper has been studied for alphabet recognition by using MPEG-7 descriptors. As an application of MPEG-7, the algorithms in this paper have employed Homogeneous Texture descriptor and Edge Histogram descriptor. For confident matching a voting system is used with a tree structure,