• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.582-585
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• 2005

The Geostationary Operational Environmental Satellite (GOES) 9, which is currently located at 155°E geostationary orbits, has transmitted earth observation data acquired by imager to CDA at NOAA. After the acquisition on ground, observation data are corrected on ground and re-transmitted to GOES-9 for the dissemination to users. In this paper, the procedure and result from raw data acquisition and pre-processing for earth observation imagery retrieval from GOES-9 Raw data acquired in Korea at May 2005 are introduced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.12C
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• pp.1167-1174
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• 2005

In this paper, we propose a priority method on same co-occurrence count in adaptive rank-based reindexing scheme for lossless indexed image compression. The priority on same co-occurrence count in co-occurrence count matrix depends on a front count value on each raw of co-occurrence count matrix, a count value around diagonal line on each raw of the matrix, and a count value around large co-occurrence count on each raw of the matrix. Experimental results show that our proposed method can be reduced up to 1.71 bpp comparing with Zeng's and Pinho's method.

• Proceedings of the KSMRM Conference
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• 2003.10a
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• pp.72-72
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• 2003

In SENSE, division process is used in order to get a raw sensitivity map. This process requires denominator which is estimated by "sum-of-squres". However, this image does not have uniformbrightness because of the non-symmetrical property of RF coil arrays. Thus, this study is focused on better estimation of the denominator image.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.151-156
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• 2011

The analysis and image evaluation the Ray-sum technique and Shaded Surface Display (under SSD) technique which is the reconstruction image processing technique after the CT scan was evaluated and the usability of the three-dimensional information offering was confirmed in the patient with stomach cancer. After obtaining the raw data by using 64-MDCT in 20 patient with stomach cancers, the image reconstruction processing was done. It was evaluated to describe accurately the analyzed result Ray-sum and SSD reconstruction image everyone anatomical structure. In the precision estimation of the image, the lesion location could coincide in the Ray-sum and SSD reconstruction image majority with the gastro fiberscope and we can know than the gastro fiberscope over 6cm that there was the error. In addition, We could know that degree of accordance of the results of the image interpretation about the lesion and endoscope and pathological opinion were high.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.13-21
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• 2022

In this paper, we propose a learning based joint demosaicing and super-resolution framework which uses only the mosaiced color filter array(CFA) image as the input. As the proposed method works only on the mosaicied CFA image itself, there is no need for a large dataset. Based on our framework, we proposed two different structures, where the first structure uses one deep image prior network, while the second uses two. Experimental results show that even though we use only the CFA image as the training image, the proposed method can result in better visual quality than other bilinear interpolation combined demosaicing methods, and therefore, opens up a new research area for joint demosaicing and super-resolution on raw images.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.33 no.4
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• pp.35-41
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• 2001

A newly developed functional fiber for textile, Tencel, which is known to have some advantages over wood fiber such as fibrillation, absorbency and so on, was examined to see the possibility of a raw material for hanji. Hanji was manufactured by the conventional handmade method using Tencel of three different fiber lengths with three different levels of mixing ratio of Tencel and paper mullberry fiber, and their physical and calligraphic properties were evaluated and compared with one another. It was needed to develop more efficient beating methods than conventional one such as valley beating for Tencel to be used effectively as a raw material for hanji. It was found out by image analysis that the calligraphic properties of hanji could be improved by mixing of 10 to 20% of Tencel of relatively short-length fiber with paper mulberry.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1986.10a
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• pp.56-58
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• 1986

This paper propose a conversion method to reduce prediction error produced when PRA(Pel Recursive Algorithm) motion estimation method is used in real image. The method is th get a spatial difference image from a given raw image and then to apply any PRA method to the difference image. The algorithm proposed in this paper is compared with several algorithm including the ubiquitious Netravali and Robbins` based on the performance and the hardware complexity. Computer simulation shows that the difference image conversion method is about 4.5dB better than the other algorithm with regard to prediction error power.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.2
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• pp.38-44
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• 2003

This paper describes the development of stand-alone image processing module on Strong Arm CPU employing an embedded Linux. Stand-alone image Processing module performs various functions such as thresholding, edge detection, and image enhancement of a raw image data in real time. The comparison of execution time between similar PC and developed module shows the satisfactory results. This Paper provides the possibility of applying embedded Linux successfully in industrial devices.

• Journal of Biosystems Engineering
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• v.19 no.2
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• pp.124-137
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• 1994

Visual features of a mushroom(Lentinus Edodes, L.) are critical in grading and sorting as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. To realize the automatic handling and grading of mushrooms in real time, the computer vision system should be utilized and the efficient and robust processing of the camera captured visual information be provided. Since visual features of a mushroom are distributed over the front and back sides, recognizing sides and states of the stalk including the stalk orientation from the captured image is a prime process in the automatic task processing. In this paper, the efficient and robust recognition process identifying the front and back side and the state of the stalk was developed and its performance was compared with other recognition trials. First, recognition was tried based on the rule set up with some experimental heuristics using the quantitative features such as geometry and texture extracted from the segmented mushroom image. And the neural net based learning recognition was done without extracting quantitative features. For network inputs the segmented binary image obtained from the combined type automatic thresholding was tested first. And then the gray valued raw camera image was directly utilized. The state of the stalk seriously affects the measured size of the mushroom cap. When its effect is serious, the stalk should be excluded in mushroom cap sizing. In this paper, the stalk removal process followed by the boundary regeneration of the cap image was also presented. The neural net based gray valued raw image processing showed the successful results for our recognition task. The developed technology through this research may open the new way of the quality inspection and sorting especially for the agricultural products whose visual features are fuzzy and not uniquely defined.