• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2121-2127
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• 2017

In this paper, we propose a computer vision based automatic defect detection method from ceramic image for non-destructive testing. From region of interest of the image, we apply brightness enhancing stretching algorithm first. One of the strength of our method is that it is designed to detect defects of images obtained from various thicknesses, that is, 8, 10, 11, 16, and 22 mm. In other cases we apply histogram based binarization algorithm. However, for 8 mm case, it may have false positive cases due to weak brightness contrast between defect and noise. Thus, we apply modified fuzzy binarization algorithm for 8 mm case. From the experiment, we verify that the proposed method shows stronger result than our previous study that used Blob labelling for all five thickness cases as expected.

• Journal of Korea Multimedia Society
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• v.10 no.4
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• pp.441-449
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• 2007

In clustering for image segmentation, large amount of computation and typical segmentation errors have been important problems. In the paper, we suggest a new method for minimizing these problems. Markers in marker-controlled watershed transform represent segmented areas because they are starting-points of extending areas. Thus, clustering restricted by marker pixels can reduce computational complexity. In our proposed method, the markers are selected by Gabor texture energy, and cluster information of them are generated by FCM (fuzzy c-mean) clustering. Generated areas from watershed transform are merged by using cluster information of markers. In the test of Brodatz' texture images, we improved typical partition-errors obviously and obtained less computational complexity compared with previous FCM clustering algorithms. Overall, it also took regular computational time.

• Journal of Advanced Navigation Technology
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• v.17 no.1
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• pp.98-106
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• 2013

In this paper, we propose a moving object detection and tracking method for multi-view camera environment. Based on the similarity and characteristics of motion vectors and coding block modes extracted from compressed bitstreams, validation of moving blocks, labeling of the validated blocks, and merging of neighboring blobs are performed. To continuously track objects for temporary stop, crossing, and overlapping events, a window based object updating algorithm is proposed for single- and multi-view environments. Object detection and tracking could be performed with an acceptable level of performance without decoding of video bitstreams for normal, temporary stop, crossing, and overlapping cases. The rates of detection and tracking are over 89% and 84% in multi-view environment, respectively. The rates for multi-view environment are improved by 6% and 7% compared to those of single-view environment.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.581-588
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• 2008

Old films or photographs usually have damages from physical or chemical effects, and the damage and digitalization make stain, scratch, scribbling, noise, and digital drop out in frames. Damages include global damage and local damage, and it is well known that local damage restoration is a main factor for improving image quality. Previous researches have focused on impairment localization (esp. for line scratch impairments) and restoration techniques for line scratch, dirt, blob, and intentional scratch. Inpainting is a key technique using partial derivatives to restore damages in images. It does not show good quality for the complex images because it is based on finite order for partial derivatives, and it takes much time complexity. In this paper, we present a modified inpainting scheme, where we use Sobel edge operator's and angle to compute isophotes, and compare our scheme with Bertalmio's scheme. We experiment our scheme with two old Korean films, and Simulation results show that our scheme requires smaller time complexity than Bertalmio's scheme with comparable reconstructed image quality.

• Journal of Fashion Business
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• v.7 no.5
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• pp.108-118
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• 2003

Surface properties, including the texture and the luster, of cotton fibers and yarns thereof play an important role in textile technology. The convolutions and the cross-sectional shape of the cotton fiber affect the fabric texture and the luster accordingly. Mercerization of the cotton fabric affects the luster, strength, and other properties of the fabric. In this study, the effect of mercerization was examined on the luster of the cotton fabric, together with the effect of polishing treatment. One of the traditional methods determining the fabric luster is the use of glossmeter or goniometric glossmeter. The use of glossmeter gives successful results in determining the gloss of rather flat and continuous surface such as plastic sheet, painted surface, or paper products. Since the textile fabrics have diverse surface structures and textures, these could be regarded as having three-dimensional surface. Such complexity imposes some difficulties for differentiating subtle surface luster properties of diverse textile fabrics. The advancement in the area of imaging technologies has enabled the micro-scale analysis of the surface textures and the fabric luster recently. Using a CCD camera, the surface luster images were taken at various incident illumination conditions. Microscale analysis, including the blob analysis, of the images could differentiate the subtle luster properties present in a group of cotton fabric samples comprising mercerized cotton fabric, non-mercerized cotton fabric, polished cotton fabric, and a 'standard' cotton fabric. The glossmeter measurement gave satisfactory but limited differentiation among the samples, whose luster differences are easily recognizable with visual observation, except for the mercerized cotton fabric sample and the non-mercerized cotton fabric. The microscale analysis of the fabric luster could, therefore, help understand the nature of diverse textile fabric luster.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.47-54
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• 2010

Old archived film shows two major defects: line scratch and blobs. In this paper, we present a design and implementation of an automatic video restoration system for line scratches observed in archived film. We use autoregressive (AR) image model because we can make stochastic and specifically autoregressive image generation process with our PAST-PRESENT model and Sampling Pattern. We designed locality maximizing scanning pattern, which can generate nearly stationary time-like series of pixels, which is a strong requirement for a stochastic series to be autoregressive. The sampled pixel series undergoes filtering and model fitting using Durbin-Levinson algorithm before interpolation process. We designed three-stage film restoration system, which includes (1) film acquisition from VHS tapes, (2) simple line scratch detection and restoration, and (3) manual blob identification and sophisticated inpainting scheme. We implemented film acquisition and simple inpainting scheme on Texas Instruments DSP board TMS320DM642 EVM, and implemented our AR inpainting scheme on PC for sophisticated restoration. We experimented our scheme with two old Korean films: "Viva Freedom" and "Robot Tae-Kwon-V", and the experimental results show that our scheme improves Bertalmio's scheme for subjective quality (MOS), objective quality (PSNR), and especially restoration ratio (RR), which reflects how much similar to the manual inpainting results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.312-321
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• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.