• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.784-793
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• 2012

Defects of TFT-LCD is detected by thresholding the difference image between the input image and template one because LCD panel has its inherent patterns. However, the pitch corresponding to pattern period is gradually changed according to the distance from the center of camera due to geometric distortion of camera characteristics. This paper presents a method to detect defects through comparing the pitch area with neighbor pitch areas where the perspective transform is performed with the extracted features to correct the distortion. The experimental results show that the performance of the proposed method is very effective for real data.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.159-175
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• 2015

The two-dimensional QR code has advantages such as directional nature, enough data storage capacity, ability of error correction, and ability of data restoration. There are two major issues like speed and correctiveness of recognition in the two-dimensional QR code. Therefore, this paper proposes a morphology-based algorithm of detecting the interest region of a barcode. Our research contents can be summarized as follows. First, the interest region of a barcode image was detected by close operations in morphology. Second, after that, the boundary of the barcode are detected by intersecting four cross line outside in a code. Three, the projected image is then rectified into a two-dimensional barcode in a square shape by the reverse-perspective transform. In result, it shows that our detection and recognition rates for the barcode image is also 97.20% and 94.80%, respectively and that outperforms than previous methods in various illumination and distorted image environments.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2843-2845
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• 2000

A lane detection based on a road model or feature all need correct acquirement of information on the lane in a image, It is inefficient to implement a lane detection algorithm through the full range of a image when being applied to a real road in real time because of the calculating time. This paper defines two searching range of detecting lane in a road, First is searching mode that is searching the lane without any prior information of a road, Second is recognition mode, which is able to reduce the size and change the position of a searching range by predicting the position of a lane through the acquired information in a previous frame. It is allow to extract accurately and efficiently the edge candidates points of a lane as not conducting an unnecessary searching. By means of removing the perspective effect of the edge candidate points which are acquired by using the inverse perspective transformation, we transform the edge candidate information in the Image Coordinate System(ICS) into the plane-view image in the World Coordinate System(WCS). We define linear approximation filter and remove the fault edge candidate points by using it This paper aims to approximate more correctly the lane of an actual road by applying the least-mean square method with the fault-removed edge information for curve fitting.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.1
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• pp.117-121
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• 2009

The fisheye image acquired with a fisheye camera has wider field of view than a general use camera. But large distortion of the object in the image requires conversion of the fisheye image to the perspective image because of user's difficult perception. The existing Ishii's method[1] has the problem that the object can has sire and geometrical distortion in the transformed image because it uses equidistance projection. This paper presented a conversion technique of the fisheye image to the perspective image using sealing function. In the experiments, it was shown that our method reduced size and geometrical distortion by applying the scaling function.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.3
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• pp.48-57
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• 2005

This paper presents a novel algorithm in order to extract lane-related information based on machine vision techniques. The algorithm makes up for the weak points of the former method, the Edge Distribution Function(EDF)-based approach, by introducing a Lane Boundary Pixel Extractor (LBPE) and the well-known Hough Transform(HT). The LBPE that serves as a filter to extract pixels expected to be on lane boundaries enhances the robustness of machine vision, and provides its results to the HT implementation and EDF construction. The HT forms the accumulator arrays and extracts the lane-related parameters composed of orientation and distance. Furthermore, as the histogram of edge magnitude with respect to edge orientation angle, the EDF has peaks at the orientations corresponding to lane slopes on the perspective image domain. Therefore, by fusing the results from the EDF and the HT the proposed algorithm improves the confidence of the extracted lane-related information. The system shows successful results under various degrees of illumination.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.1
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• pp.22-27
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• 2015

Transform-domain JND (Just Noticeable Difference)-based for PVC (Perceptual Video Coding) is often performed in quantization processes to effectively remove perceptual redundancy. This study examined the JND-suppression effects on quantized coefficients of transform in HEVC (High Efficiency Video Coding). To reveal the JND-suppression effect in quantization, the properties of the floor functions were used for modeling the quantized coefficients, and a JND-adjustment process in an HEVC-compliant PVC scheme was used to tune the JND values by analyzing the JND suppression effect. In the experimental results, the bitrate reduction decreases slightly, but the PSNR and perceptual quality are improved significantly when the proposed JND adjustment process is applied.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.625-631
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• 2010

This paper proposes lane recognition algorithm using lane prominence technique to extract lane candidate. The lane prominence technique is combined with embossing effect, lane thickness check, and lane extraction using mask. The proposed lane recognition algorithm consists of preprocessing, lane candidate extraction and lane recognition. First, preprocessing is executed, which includes gray image acquisition, inverse perspective transform and gaussian blur. Second, lane candidate is extracted by using lane prominence technique. Finally, lane is recognized by using hough transform and least square method. To evaluate the proposed lane recognition algorithm, this algorithm was applied to the detection of lanes in the rainy and night day. The experiment results showed that the proposed algorithm can recognize lane in various environment. It means that the algorithm can be applied to lane recognition to drive unmanned vehicles.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.41-52
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• 2018

This paper presents a method for accurate camera self-calibration based on SIFT Feature Detection and image quality assessment. We performed image quality assessment to select high quality images for the camera self-calibration process. We defined high quality images as those that contain little or no blur, and have maximum contrast among images captured within a short period. The image quality assessment includes blur detection and contrast assessment. Blur detection is based on the statistical analysis of energy and standard deviation of high frequency components of the images using Discrete Cosine Transform. Contrast assessment is based on contrast measurement and selection of the high contrast images among some images captured in a short period. Experimental results show little or no distortion in the perspective view of the images. Thus, the suggested method achieves camera self-calibration accuracy of approximately 93%.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.533-539
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• 2010

The The contactless railway abrasion measurement system have to satisfy two conditions to increase the measurement accuracy as follows. The laser region projected on the rail have to be extracted without the geometrical distortion. The mapping of the acquired laser region data on the rail profile have to be matched with the cross section of rail, exactly. But, the conventional railway abrasion measurement system is required the post image processing with a camera model and a perspective transform for the exact mapping between the cross section of rail and the coordinate data extracted from a line laser region or the raw image obtained from a camera because the image captured from the camera has an oblique viewpoint. So, the measured rail profile data had limits to the measurement accuracy because of a discontinuity point. In this Paper, we propose the 3D rail profile reconstruction method to increase the accuracy of the railway abrasion measurement system applying the modified camera model and perspective transform to the image obtained from the bidirectional rail.