• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.357-359
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• 2003

There is various problem in grasping change of vegetation by NDVI, PVI, etc. It is very difficult especially to remove various noise ingredients in the received satellite data. Until now, it is difficult to compensate for shadow effect when NDVI is used in vegetation analysis. The essence of this study is to describe data simulation and then applied the result to the NOAA AVHRR data. When a pixel contains shadow more than 60% then this pixe1 is extracted for shadow effects on NDVI.

• Applied Microscopy
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• v.45 no.4
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• pp.214-217
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• 2015

This study introduces metal coating as an effective sample preparation method to remove charge-up caused by the shadow effect during field emission scanning electron microscope (FE-SEM) analysis of dynamic structured samples. During a FE-SEM analysis, charge-up occurs when the primary electrons (input electrons) that scan the specimens are not equal to the output electrons (secondary electrons, backscattered electrons, auger electrons, etc.) generated from the specimens. To remove charge-up, a metal layer of Pt, Au or Pd is applied on the surface of the sample. However, in some cases, charge-up still occurs due to the shadow effect. This study developed a coating method that effectively removes charge-up. By creating a converted sample stage capable of simultaneous tilt and rotation, the shadow effect was successfully removed, and image data without charge-up were obtained.

• Journal of information and communication convergence engineering
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• v.4 no.3
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• pp.123-129
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• 2006

The limited number of roads and the increasing number of vehicles demand the automatic regulation of overspeed vehicles, illegal vehicles, and overloaded vehicles and the automatic charge calculation depending on the type of the vehicle. To meet such requirements, it is important to remove the shadow of the vehicle as processing and recognizing an image captured by a camera. The shadow of the vehicle is likely to cause misclassification of the vehicle type due to diverse errors and mistakes occurring when detecting geometrical properties of the vehicle. In case that shadows of two different vehicles are overlapped, not only the type of the vehicles may be misclassified but also it is difficult to accurately identify the type of the vehicles. In this paper, we propose a robust algorithm to remove the shadow of a vehicle by calculating the luminance, the chrominance, the gradient density of the cast shadow from information acquired using the image subtraction of the background, and to recognize the substantial vehicle figure. Even when it is hard to detect and split a target vehicle from its shadow as shadows of vehicles are attached to each other, our robust algorithm can detect the vehicle figure only. We implemented our system with a general camera and conducted experiments on various vehicles on general roads to find out our vehicle shade removal algorithm is efficient when detecting and recognizing vehicles.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.29-36
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• 2019

When dealing with outdoor images in a variety of computer vision applications, the presence of shadow degrades performance. In order to understand the information occluded by shadow, it is essential to remove the shadow. To solve this problem, in many studies, involves a two-step process of shadow detection and removal. However, the field of shadow detection based on CNN has greatly improved, but the field of shadow removal has been difficult because it needs to be restored after removing the shadow. In this paper, it is assumed that shadow is detected, and shadow-less image is generated by using original image and shadow mask. In previous methods, based on CGAN, the image created by the generator was learned from only the aspect of the image patch in the adversarial learning through the discriminator. In the contrast, we propose a novel method using a discriminator that judges both the whole image and the local patch at the same time. We not only use the residual generator to produce high quality images, but we also use joint loss, which combines reconstruction loss and GAN loss for training stability. To evaluate our approach, we used an ISTD datasets consisting of a single image. The images generated by our approach show sharp and restored detailed information compared to previous methods.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.395-402
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• 2005

Object extraction is needed to track objects in color traffic image sequence. To extract objects, we use background differencing method based on MOG(Mixture of Gaussians). In extracted objects, shadows may be included. Due to shadows, we may not find exact location of objects and sometimes we find adjacent objects are glued together. Many methods have been proposed to remove shadows. Conventional methods usually assume that color and texture information are preserved under the shadow. Thus these methods do not work well if these assumptions do not hold. In this paper, we propose a new robust shadow removal method which works well in those situations. First we extract shadow pixel candidates by analysing color information and compute the ratio of shadow pixel candidates over the total number of Pixels. W the ratio is reasonable, we remove shadow candidate Pixels and if not, we use data in history array containing Previous removal records. We applied the method to real color traffic image sequences and obtained good results.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.02a
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• pp.64-72
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• 2004

Correction of shadow effects is critical step for image interpretation and feature extraction from aerial imagery. In this paper, an efficient algorithm to correct shadow effects from aerial color imagery is presented. The following steps have been performed to remove the shadow effect. First, the shadow regions are precisely located using the solar position and the height of ground objects derived from LIDAR (Light Detection and Ranging) data. Subsequently, segmentation of context regions is implemented for accurate correction with existing digital map. Next step, to calculate correction factor the comparison between the context region and the same non-shadowed context region is made. Finally, corrected image is generated by correcting the shadow effect. The result presented here helps to accurately extract and interpret geo-spatial information from aerial color imagery

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.251-256
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• 1993

In KACC'91 and '92 conference, we proposed a method of automatically detecting the shape of the faulty holes in a shadow mask by use of CCD ca.mera and image data processing technic. In this method, two adjoining test areas from one image data. of the shadow mask are taken and comparing the shape of holes in these two areas, we can detect the faults in the shadow mask. In this paper, a method is described by use of spatial filtering of effectively finding the faulty holes from the difference image data between the two tested image data. The main role of the filter is to remove sampling errors occurring at the edge of the holes. And the second role is not only to find the existence of faulty holes but also exactly express the shape of faulty holes. Computer simulations and actual experiments with shadow masks have shown that this method of fault detection is very effective for practical use.

• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.610-622
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• 2008

Moving object segmentation is an essential technique for various video surveillance applications. The result of moving object segmentation often contains shadow regions caused by the color difference of shadow pixels. Hence, moving object segmentation is usually followed by a shadow elimination process to remove the false detection results. The common assumption adopted in previous works is that, under the illumination variation, the value of chromaticity components are preserved while the value of intensity component is changed. Hence, color transforms which separates luminance component and chromaticity component are usually utilized to remove shadow pixels. In this paper, various color spaces (YCbCr, HSI, normalized rgb, Yxy, Lab, c1c2c3) are examined to find the most appropriate color space for shadow elimination. So far, there have been some research efforts to compare the influence of various color spaces for shadow elimination. However, previous efforts are somewhat insufficient to compare the color distortions under illumination change in diverse color spaces, since they used a specific shadow elimination scheme or different thresholds for different color spaces. In this paper, to relieve the limitations of previous works, (1) the amount of gradients in shadow boundaries drawn to uniform colored regions are examined only for chromaticity components to compare the color distortion under illumination change and (2) the accuracy of background subtraction are analyzed via RoC curves to compare different color spaces without the problem of threshold level selection. Through experiments on real video sequences, YCbCr and normalized rgb color spaces showed good results for shadow elimination among various color spaces used for the experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.574-578
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• 2009

Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.249-256
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• 2015

Shadow is a common physical phenomenon in natural images and may cause problems in computer vision tasks. Therefore, shadow removal is an essential preprocessing process for effective moving object tracking in video image. In this paper, we proposed the method of shadow removal algorithm using chromaticity, brightness distortion and direction of shadow candidate. The proposed method consists of two steps. First, removal process of primary shadow candidate region by using chromaticity, brightness and distortion. The second stage applies the final shadow candidate region to obtain a direction feature of shadow which is estimated by the thinning algorithm after calculating the lowest pixel position of the moving object. To verify the proposed approach, some experiments are conducted to draw a compare between conventional method and that of proposed. Experimental results showed that proposed methodology is simple, but robust and well adaptive to be need to remove a shadow removal operation.