• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.261-267
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• 2007

The research about Intelligence Transport Systems(ITS) is actively studied for the traffic problem solution recently. Also, the various methods to detect vehicles moving in the roads are studied. This research using image processing technology is to give the drivers the road information quickly by developing Vehicle Detection System that detects through traffics. Purpose or this research is developing efficient algorithm to facilitate hardware composition. We use morphology method to extract corner points in the images captured by CCD camera. Also, the proposed algorithm detects vehicle's moving area by using motion vectors between corner points. The experiments of the proposed algorithm whose processing time was shortened show good results in vehicle detection on the live road images.

• Journal of the Korean institute of surface engineering
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• v.55 no.6
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• pp.398-402
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• 2022

Vacuum Insulation Panel (VIP) is an high energy efficient insulation system that facilitate slim but high insulation performance, based on based on a porous core material evacuated and encapsulated in a multi-barrier envelope. Although VIP has been on the market for decades now, it wasn't until recently that efforts have been initiated to propose a standard on aging testing. One of the issues regarding VIP is its durability and aging due to pressure and moisture dependent increase of the initial low thermal conductivity with time. It is hard to visually determine at an early stage. Recently, a method of analyzing the damage on the a material surface by applying image processing technology has been widely used. These techniques provide fast and accurate data with a non-destructive way. In this study, the surface VIP images were analyzed using the Harris corner detection algorithm. As a result, 171,333 corner points in the normal packaging were detected, whereas 32,895 of the defective packaging, which were less than the normal packaging. were detected. These results are considered to provide meaningful information for the determination of VIP condition.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.253-261
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• 2017

In this paper, a corner detection method based on a new non-cornerness measure is presented. Rather than evaluating local gradients or surface curvatures, as done in previous approaches, a non-cornerness function is developed that can identify stable corners by testing an image region against a set of desirable corner criteria. The non-cornerness function is comprised of two steps: 1) eliminate any pixel located in a flat region and 2) remove any pixel that is positioned along an edge in any orientation. A pixel that passes the non-cornerness test is considered a reliable corner. The proposed method also adopts the idea of non-maximum suppression to remove multiple corners from the results of the non-cornerness function. The proposed method is compared with previous popular methods and is tested with an artificial test image covering several corner forms and three real-world images that are universally used by the community to evaluate the accuracy of corner detectors. The experimental results show that the proposed method outperforms previous corner detectors with respect to accuracy, and that it is suitable for real-time processing.

• Journal of Sensor Science and Technology
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• v.29 no.3
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• pp.194-200
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• 2020

Human detection is an important aspect in many video-based sensing and monitoring systems. Studies have been actively conducted for the automatic detection of humans in camera images, and various methods have been proposed. However, there are still problems in terms of performance and computational cost. In this paper, we describe a method for efficient human detection in the field of view of a camera, which may be static or moving, through multiple processing steps. A detection line is designated at the position where a human appears first in a sensing area, and only the one-dimensional gray pixel values of the line are monitored. If any noticeable change occurs in the detection line, corner detection and optical flow computation are performed in the vicinity of the detection line to confirm the change. When significant changes are observed in the corner numbers and optical flow vectors, the final determination of human presence in the monitoring area is performed using the Histograms of Oriented Gradients method and a Support Vector Machine. The proposed method requires processing only specific small areas of two consecutive gray images. Furthermore, this method enables operation not only in a static condition with a fixed camera, but also in a dynamic condition such as an operation using a camera attached to a moving vehicle.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.417-425
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• 2006

This Paper Proposes a method of building a panoramic video from several videos captured from adjacent cameras. The panoramic image which constructed from adjacent and overlapped images is used for photogrammetry, satellite photo or many computer graphic applications. The perspective transformation, which is estimated from the appropriate corresponding pairs of images, can be used to construct the panoramic image without unwarranted distortion and its quality is decided by how to find the features needed for transform estimation. We used the corner points for the corresponding features, and morphological structures were utilized for fast and robust corner detection. We used the criterion of the corner strength, which guarantees the robust detection of the corner in most situations. For the transformation, 8 parameters were estimated from perspective equations which use matched points of adjacent images, and bilinear color blending was used to construct a soapless panoramic video. The experiments showed that the proposed method yields fast results with good quality under various conditions.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.313-320
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• 2017

The In-loop filtering methods such as de-blocking filter and SAO(Sample Adaptive Offset) applied to the HEVC standard achieves coding efficiency and subjective quality improvement by reducing the blocking artifacts and the ringing artifacts. However, despite the use of In-loop filtering methods, the artifacts called a corner outlier occurring at the corner points of block boundaries are not removed. In this paper, the corner outlier artifacts are reduced by the detection, determination, and filtering processes on the corner outlier pixels. Experimental results show that the proposed method improves the subjective picture quality and slightly increases the coding efficiency in Inter prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3981-4004
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• 2019

This paper proposes a novel method for locating objects in real space from a single remote image and measuring actual distances between them by automatic detection and perspective transformation. The dimensions of the real space are known in advance. First, the corner points of the interested region are detected from an image using deep learning. Then, based on the corner points, the region of interest (ROI) is extracted and made proportional to real space by applying warp-perspective transformation. Finally, the objects are detected and mapped to the real-world location. Removing distortion from the image using camera calibration improves the accuracy in most of the cases. The deep learning framework Darknet is used for detection, and necessary modifications are made to integrate perspective transformation, camera calibration, un-distortion, etc. Experiments are performed with two types of cameras, one with barrel and the other with pincushion distortions. The results show that the difference between calculated distances and measured on real space with measurement tapes are very small; approximately 1 cm on an average. Furthermore, automatic corner detection allows the system to be used with any type of camera that has a fixed pose or in motion; using more points significantly enhances the accuracy of real-world mapping even without camera calibration. Perspective transformation also increases the object detection efficiency by making unified sizes of all objects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.646-648
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• 2012

In this paper, an image detection technique is proposed to extract image features in a mobile terminal environment. To detect objects, the HSI color model of the image is used. The object's corner points are detected using the Harris corner detection method. Finally we detect the object of interest using region growing The experiment results show that the proposed method improves detection performance and reduces the amount of computation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.713-716
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• 2009

In this paper, we propose a vision-based detection of container hole for container location measuring. We use a method for container position using detection of diagonal container holes, because containers have holes that are linked to spreader headblocks. First, we extract images from spreader and detect straight lines to detect container in images using hough transform. Next, proposed method finds positions of cross at the right angles and set candidates of the corner that is linked to spreader headblocks. Finally, this method performs template matching to detect a right corner of containers. Experimental results show that proposed method performed well at detection of container position.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.173-179
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• 2012

In this paper, we presents a new method for vehicle number plate detection. Our method is basically the method extracting a rectangles from a car image because the shape of a vehicle number plate is a rectangle. For detecting the vehicle number plate, firstly, the contrast of the input image is enhanced. Then, the lines in the image are obtained by using LSD(line segment detector), and rectangles in the image are detected from the line data. These rectangles are the candidates of the car plate, from which the car plate is selected. In this procedure, the method of detecting rectangles is our proposed method, which consists of three stages: (1) extracting corners from the line segments by LSD; (2) extracting diagonal lines from the corner data; and (3) detecting rectangles from diagonal line information. And finally the vehicle number plate is selected from these rectangles by using the feature of the vehicle number plate and the inside information of rectangles. In the experiments with the 100 images captured by our digital camera, we have achieved a detection rate of 94%.