• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1785-1800
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• 2014

In 2011, Chen and Wu proposed their method of sharing n secret images to n+1 shadow images through the concept of a Boolean-based Visual Secret Sharing (VSS) method. However, the shadow images produced by this method are not equally important. If the participant who owns an important shadow image does not want to cooperate with other participants, most secret images can not be reconstructed. In the proposed method, the relationship between the shadows images and secret images are designed in a circular way mostly. Each shadow image only relates to two secret images. This means that if one participant refuses to cooperate with other participants, there are only two secret images which can not be reconstructed. Moreover, our proposed method only needs to produce n shadow images and n secret images can be shared to them.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.773-776
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• 1998

In this paper, we propose an efficient multi-view images coding algorithm which finds the optimal texture from the restricted number of multi-view images. The X-Y plane of the normalized object space is divided into triangular patches. The depth value of the node is determined by applying the block based disparity compensation method and then the texture of the each patch is extracted by applying the affine transformation patch is extracted by applying the affine transformation based disparity compensation method to the multi-view images. We restricted the number of images contributed to determining the texture comapred to traditional methods which use all the multi-view images in the texture extraction. Experimental results show that the SNR of images encoded by the proposed algorithm is better than that of imaes encoded by the traditional method by the amount about 0.2dB for the test sets of multi-view images called dragon, kid, city and santa. The recovered images from the encoded data by the proposed method show the better visual images than the recovered images from the encoded data by the traditional methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1926-1931
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• 2012

We investigated a novel fast non-uniform illumination correction method for bi-level images. The proposed method divides a bi-level image into sub-images and roughly estimates block-wise illumination by low pass filtered maximum values of sub-images. After that, we apply bilinear interpolation using the block-wise illumination to estimate non-uniform illumination, and compensate for the effect of non-uniform illumination using the estimated illumination. Since the proposed method is not based on computation intensive iterative optimization, the proposed method can be used effectively for applications that require fast correction of non-uniform illumination. In simulations, the proposed method showed more than 20 times faster speed than existing entropy minimization method. Moreover, in simulations and experiments, the restored images by the proposed method were more close to true images than images restored by conventional method.

• Journal of Korea Society of Industrial Information Systems
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• v.3 no.1
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• pp.141-151
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• 1998

The importance of fax imagerestoration is constantly increasing with the widespreasd use of facsimile machines in almost every sector of modern society. Recently, Handley and Doutherty proposed a morphological method for restoration of fax images, Their method removed effectively the only salient noise in the fax images. However, it could not remove the white and pepper noise that can appear in fax images since they treated fax images as deterministic sequences rather than random fields. Furthermore, this approach suffers from computational burden since it does not use recursive restoration technique. To cope with those difficulties, in this paper, we propose a new restoration scheme for restoring fax images using Kalman fitering which provides and efficient recursive processor. The proposed restoration method is based on the wide-sense Markov random fields (WSM).In order to verify the performance of the proposed restoration method, several experiments with the CCITT Group 3 fax machine were conducted with the generated document .Experimental results revealed that our proposed restoration method was shown to be superior to Handley et.al's method for restoring fax images.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.43-49
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• 2018

In this paper, we propose a displacement measurement method based on deep learning using image data obtained from tensile tests of a material specimen. We focus on the fact that the sequential images during the tension are generated and the displacement of the specimen is represented in the image data. So, we designed sample generation model which makes sequential images of specimen. The behavior of generated images are similar to the real specimen images under tensile force. Using generated images, we trained and validated our model. In the deep neural network, sequential images are assigned to a multi-channel input to train the network. The multi-channel images are composed of sequential images obtained along the time domain. As a result, the neural network learns the temporal information as the images express the correlation with each other along the time domain. In order to verify the proposed method, we conducted experiments by comparing the deformation measuring performance of the neural network changing the displacement range of images.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.224-229
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• 2007

This paper presents a camera calibration method using several images for three dimensional measurement applications such as stereo systems, mobile robots, and visual inspection systems in factories. Conventional calibration methods that use single image suffer from errors related to reference point extraction in image, lens distortion, and numerical analysis of nonlinear optimization. The camera parameter values obtained from images of same camera is not same even though we use same calibration method. The camera parameters that are obtained from several images of different view for a calibration target is usaully not same with large error values and we can not assume a special probabilistic distribution when we estimate the parameter values. In this paper, the median value of camera parameters from several images is used to improve estimation of the camera values in an iterative step with nonlinear optimization. The proposed method is proved by experiments using real images.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.197-200
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• 2005

Super resolution image reconstruction method refers to image processing algorithms that produce a high resolution(HR) image from observed several low resolution(LR) images of the same scene. This method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, such as satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. In this paper we applied super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and overlapped for high rate. We constructed the observation model between the HR images and LR images applied by the Maximum A Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06b
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• pp.35-40
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• 1996

In this paper, a method for 3-D surface reconstruction with two real cameras is presented. The method, which combines the extraction of binocular disparity and its interpolation can be applied to the synthesis of images from virtual viewpoints. The synthesized virtual images are as natural as the real images even when we observe the images as stereoscopic images. The method opens up many applications, such as synthesizing input images for multi-viewpoint 3-D displays, enhancing the depth impression in 2-D images and so on. We also have developed a video-rate stereo machine able to obtain binocular disparity in 1/30 sec with two cameras. We show the performance of the machine.

• Smart Structures and Systems
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• v.29 no.1
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• pp.195-206
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• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.6
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• pp.886-898
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• 1995

One of the ways to measure the distance in the computer vision is to use the focus and defocus. There are two methods in this way. The first method is caculating the distance from the focused images in a point (MMDFP: the method measuring the distance to the focal plane). The second method is to measure the distance from the difference of the camera parameters, in other words, the apertures of the focal planes, of two images with having the different parameters (MMDCI: the method to measure the distance by comparing two images). The problem of the existing methods in MMDFP is to decide the thresholding vaue on detecting the most optimally focused object in the defocused image. In this case, it could be solved by comparing only the error energy in 3x3 window between two images. In MMDCI, the difficulty is the influence of the deflection effect. Therefor, to minimize its influence, we utilize two differently focused images instead of different aperture images in this paper. At the first, the amount of defocusing between two images is measured through the introduction of regularization and then the distance from the camera to the objects is caculated by the new equation measuring the distance. In the results of simulation, we see the fact to be able to measure the distance from two differently defocused images, and for our approach to be robuster than the method using the different aperture in the noisy image.