• Journal of The Korean Astronomical Society
• /
• v.29 no.spc1
• /
• pp.391-392
• /
• 1996

We have developed and tested a CCD camera (100 $\times$ 100 pixels) system for observing Ha images of the solar flares with time resolution> 25 msec. The 512 $\times$ 512 pixels image of CCD camera at 2 Mpixels/sec can be recorded at the rate of more than 5 frame/sec while 100 $\times$ 100 pixels area image can be obtained 40 frames/sec. The 100 $\times$ 100 pixels image of CCD camera corresponds to 130 $\times$ 130 arc - $sec^2$ of the solar disk.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.11
• /
• pp.1-7
• /
• 2018

In this paper, we propose soft dead pixels detection method by analysing different response at hot and cold images. Abnormal pixels are able to effect detecting a small target. It also makes confusing real target or not cause of changing target size. Almost exist abnormal pixels after image signal processing even if dead pixels are removed by dead pixel compensation are called soft dead pixels. They are showed defect in final image. So removing or compensating dead pixels are very important for detecting object. The key idea of this proposed method, detecting dead pixels, is that most of soft deads have different response characteristics between hot image and cold image. General infrared cameras do NUC to remove FPN. Working 2-reference NUC must be needed getting data, hot & cold images. The way which is proposed dead pixel detection is that we compare response, NUC gain, at each pixel about two different temperature images and find out dead pixels if the pixels exceed threshold about average gain of around pixels.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.5
• /
• pp.251-258
• /
• 2004

This paper proposes a method for estimating the illuminant chromaticity using the distributions of the camera responses obtained by a CCD camera in a real-world scene. Illuminant estimation using a highlight method is based on the geometric relation between a body and its surface reflection. In general, the pixels in a highlight region are affected by an illuminant geometric difference, camera quantization errors, and the non-uniformity of the CCD sensor. As such, this leads to inaccurate results if an illuminant is estimated using the pixels of a CCD camera without any preprocessing. Accordingly, to solve this problem the proposed method analyzes the distribution of the CCD camera responses and selects pixels using the Mahalanobis distance in highlight regions. The use of the Mahalanobis distance based on the camera responses enables the adaptive selection of valid pixels among the pixels distributed in the highlight regions. Lines are then determined based on the selected pixels with r-g chromaticity coordinates using a principal component analysis(PCA). Thereafter, the illuminant chromaticity is estimated based on the intersection points of the lines. Experimental results using the proposed method demonstrated a reduced estimation error compared with the conventional method.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.12
• /
• pp.35-41
• /
• 2016

In this paper, we propose a new real-time dead pixel detection method based on spatial compare filtering, which are usually used in the small target detection. Actually, the soft dead and the small target are cast in the same mold. Our proposed method detect and remove the dead pixels as applying the spatial compare filtering, into the pixel outputs of a detector after the non-uniformity correction. Therefore, we proposed method can effectively detect and replace the dead pixels regardless of the non-uniformity correction performance. In infrared camera, there are usually many dead detector pixels which produce abnormal output caused by manufactural process or operational environment. There are two kind of dead pixel. one is hard dead pixel which electronically generate abnormal outputs and other is soft dead pixel which changed and generated abnormal outputs by the planning process. Infrared camera have to perform non-uniformity correction because of structural and material properties of infrared detector. The hard dead pixels whose offset values obtained by non-uniformity correction are much larger or smaller than the average can be detected easily as dead pixels. However, some dead pixels(soft dead pixel) can remain, because of the difficulty of uncleared decision whether normal pixel or abnormal pixel.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.8 no.2
• /
• pp.25-31
• /
• 2009

The image processing methods are widely used in many industrial fields to detect defections in inspection devices. In this study an image processing method was conducted for the detection of abnormal pixels in a OLED(Organic Light Emitting Diode) type panel which is used for small size displays. The display quality of an OLED device is dependent on the pixel formation quality. So, among the so many pixels, to find out the faulty pixels is very important task in manufacturing processing or inspection division. We used a line scanning type BW(Black & White) camera which has very high resolution characteristics to acquire an image of display pixel patterns. And the various faulty cases in pixel abnormal patterns are considered to detect abnormal pixels. From the results of the research, the normal BW pixel image could be restored to its original color pixel.

• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.176-178
• /
• 2005

In the images of a video camera employed for surveillance, detecting targets by extracting foreground image is of great importance. The foreground regions detected, however, include not only moving targets but also their shadows. This paper presents a novel technique to detect shadow pixels in the foreground image of a video camera. The image characteristics of video cameras employed, a web-cam and a CCD, are first analysed in the HSV color space and a pixel-level shadow detection technique is proposed based on the analysis. Compared with existing techniques where unified criteria are used to all pixels, the proposed technique determines shadow pixels utilizing a fact that the effect of shadowing to each pixel is different depending on its brightness in background image. Such an approach can accommodate local features in an image and hold consistent performance even in changing environment. In experiments targeting pedestrians, the proposed technique showed better results compared with an existing technique.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.05a
• /
• pp.333-336
• /
• 2007

We propose a simple method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Then we present a method to eliminate the tracked contour object by replacing with the background scene we get from other frame. Our method consists of two parts: first we track the object using LOD (Level-of-Detail) canny edge maps, then we generate background of each image frame and replace the tracked object in a scene by a background image from other frame that is not occluded by the tracked object. Our tracking method is based on level-of-detail (LOD) modified Canny edge maps and graph-based routing operations on the LOD maps. To reduce side-effects because of irrelevant edges, we start our basic tracking by using strong Canny edges generated from large image intensity gradients of an input image. We get more edge pixels along LOD hierarchy. LOD Canny edge pixels become nodes in routing, and LOD values of adjacent edge pixels determine routing costs between the nodes. We find the best route to follow Canny edge pixels favoring stronger Canny edge pixels. Our accurate tracking is based on reducing effects from irrelevant edges by selecting the stronger edge pixels, thereby relying on the current frame edge pixel as much as possible. This approach is based on computing camera motion. Our experimental results show that our method works nice for moderate camera movement with small object shape changes.

• Proceedings of the IEEK Conference
• /
• 2002.06d
• /
• pp.81-84
• /
• 2002

This paper presents the real-time PCB(Printed circuit board) inspection system that can detect thin open/short error using the line scan camera. After a overall introduction of our system, the outline of our inspection methods are described. The goal of our inspection system is the real time and detailed inspection using the line scan camera. To perform inspection processing in real-time, we utilize double buffering structure. In order to solve the problem of unexpectable pixels of PCB, we propose melting process which eliminates unexpectable pixels of PCB. The design and development of our prototype of PCB ins- pection system is discussed and test results are presented to show the effectiveness of the developed inspection algorithm.

• Journal of Korea Society of Industrial Information Systems
• /
• v.24 no.5
• /
• pp.1-8
• /
• 2019

In this paper, we propose a correcting method of depth pixels which are failed to measure since temporary camera error. A block is modeled to plane and sphere surfaces through measured depth pixels in the block. Depth values in the block are estimated through each modeled surface and a error for the modeled surface is calculated by comparing the original and estimated pixels, then the surface which has the least error is selected. The pixels which are failed to measure are corrected by estimating depth values through selected surface. Simulation results show that the proposed method increases the correction accuracy by an average of 20% compared with the correction method of $5{\times}5$ median method.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.06c
• /
• pp.477-481
• /
• 2007

A background is a part where do not vary too much or frequently change in an image sequence. Using this assumption, it is presented a background acquisition algorithm for not only static but also dynamic view in this paper. For generating background, we detect a region, where has high correlation rate compared within selected region in the prior pyramid image, from the searching region in the current image. Between a detected region in the current image and a selected region in the prior image, we calculate movement vector for each regions in time sequence. After we calculate whole movement vectors for two successive images, vector histogram is used to determine the camera movement. The vector which has the highest density in the histogram is determined a camera movement. Using determined camera movement, we classify clusters based on pixel intensities which pixels are matched with prior pixels following camera movement. Finally we eliminate clusters which have lower weight than threshold, and combine remained clusters for each pixel to generate multiple background clusters. Experimental results show that we can automatically detect background whether camera move or not.