• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.145-150
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• 2008

This thesis explores the problem of reconstructing a three-dimensional(3D) scene given a set of images or image sequences of the scene. It describes efficient methods for the 3D reconstruction of static and dynamic scenes from stereo images, stereo image sequences, and images captured from multiple viewpoints. Novel methods for image-based and volumetric modelling approaches to 3D reconstruction are presented, with an emphasis on the development of efficient algorithm which produce high quality and accurate reconstructions. For image-based 3D reconstruction a novel energy minimisation scheme, Iterated Dynamic Programming, is presented for the efficient computation of strong local minima of discontinuity preserving energyy functions. Coupled with a novel morphological decomposition method and subregioning schemes for the efficient computation of a narrowband matching cost volume. the minimisation framework is applied to solve problems in stereo matching, stereo-temporal reconstruction, motion estimation, 2D image registration and 3D image registration. This thesis establishes Iterated Dynamic Programming as an efficient and effective energy minimisation scheme suitable for computer vision problems which involve finding correspondences across images. For 3D reconstruction from multiple view images with arbitrary camera placement, a novel volumetric modelling technique, Embedded Voxel Colouring, is presented that efficiently embeds all reconstructions of a 3D scene into a single output in a single scan of the volumetric space under exact visibility. An adaptive thresholding framework is also introduced for the computation of the optimal set of thresholds to obtain high quality 3D reconstructions. This thesis establishes the Embedded Voxel Colouring framework as a fast, efficient and effective method for 3D reconstruction from multiple view images.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.30-38
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• 2003

This paper presents an efficient region-based motion segmentation method for segmenting of moving objects in a traffic scene with a focus on a Video Monitoring System (VMS). The presented method consists of two phases: motion detection and motion segmentation. Using the adaptive thresholding technique, the differences between two consecutive frames are analyzed to detect the movements of objects in a scene. To segment the detected regions into meaningful objects which have the similar intensity and motion information, the regions are initially segmented using a k-means clustering algorithm and then, the neighboring regions with the similar motion information are merged. Since we deal with not the whole image, but the detected regions in the segmentation phase, the computational cost is reduced dramatically. Experimental results demonstrate robustness in the occlusions among multiple moving objects and the change in environmental conditions as well.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.12
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• pp.683-689
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• 2000

Two-dimensional(2-D) fiber-optic code-division multiple-access(FO-CDMA) system utilizes the optical orthogonal signature pattern code(OOSPC) to encode and decode 2-D data. Encoded 2-D data are spatially multiplexed and transmitted through an image fiber and receiver recovers the intended data by means of thresholding process. OOSPC's construction methods based on expansion of the optical orthogonal code, which is used in one-dimensional(1-D) FO-CDMA system, are introduced. Each OOSPC's performances are compared by using the bit error rate(BER) of interfering OOSPC's of other users. From the results we verify that a balanced incomplete block design(BIBD) construction has the best performance among other mehtods. We also propose a decomposed bit-plane method for parallel transmission and recovery of 256 gray-scale images using OOSPC's constructed by the BIBD method. The simulation result encourages the feasibility of parallel transmission and recovery of multiuser's images.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.29-30
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• 2014

본 논문에서는 컨테이너 BIC-code 자동인식 시스템의 전 처리 과정으로서 다중 임계값을 이용한 BIC-code 영역 추출 방법을 제안한다. 기후요소, 빛, 카메라 위치, 컨테이너의 색과 같은 다양한 요인으로 인해 고정된 임계값을 사용할 수 없다. 따라서 각 영상에 대해 다양한 임계값으로 실험하여 컨테이너 BIC-code 인식에 가장 우수한 임계값을 결정하여 영상을 이진화한다. 다음으로, 가장 우수한 임계값으로 이진화 한 영상에 대하여 레이블링, 닫힘 연산을 통해 BIC-code의 가로, 세로 여부를 판단하게 된다. 또한 레이블링 된 각 영역을 판단하여 잡음을 제거하고, 개별 code를 분리한다. 제안된 방법에 대한 실험결과 성공적인 코드 영역 분리가 가능함을 확인할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.361-363
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• 2001

반도체 패키지의 마킹검사(marking inspection)를 위해서는 입력 영상으로부터 검사할 패키지의 정확만 위치 검출과 패키지 윗면에 나타난 제작사 로고, 문자, Pin1 딤플의 추출이 필수적이다. 본 연구는 마킹검사를 위한 선행 연구로 마킹검사를 수행할 때, 검사할 IC 패키지의 위치와 방향을 정확하게 검출하는 것을 목적으로 하고 있다. IC 패키지의 외곽을 구성하는 리드의 명도 값은 트레이의 명도 값과 큰 차이를 나타낸다. 그러나 IC 패키지의 방향을 나타내는 Pin1 딤플은 배경과 동일한 색상으로 다만 약간 오목하게 들어가서 명도 값의 차이가 미세하다. 이러한 두 가지 상이한 특징을 효과적으로 처리하기 위하여 적응적 다중 이진화 방법을 제시하였다. 76개의 명도 영상에 대한 실험 결과 제안된 이진화 방법은 매우 효과적이었으며, 이진화된 영상으로부터 IC 패키지의 정확한 위치 검출과 방향 확인이 가능하였다.

• Journal of the Optical Society of Korea
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• v.17 no.2
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• pp.188-199
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• 2013

This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions- horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.128-134
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• 2014

In this paper, we propose a detection method of the leukocyte motions in a microvessel by using spatiotemporal image analysis. The leukocyte motions that adhere to blood vessel walls can be visualized to move along the blood vessel wall's contours in a sequence of images. In this proposal method, we use the constraint that the leukocytes move along the blood vessel wall's contours and detect the leukocyte motions by using the spatiotemporal image analysis method. The generated spatiotemporal image is processed by a special-purpose orientation-selective filter and then subsequent grouping processes are done. The subsequent grouping processes select and group the leukocyte trace segments among all the segments obtained by simple thresholding and skeletonizing operations. Experimental results show that the proposed method can stably detect the leukocyte motions even when multiple leukocyte traces intersect each other.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.423-428
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• 2005

This paper proposes a new edge detection method using a $3{\times}3$ ideal binary pattern and lookup table (LUT) for the mobile robot localization without any parameter adjustments. We take the mean of the pixels within the $3{\times}3$ block as a threshold by which the pixels are divided into two groups. The edge magnitude and orientation are calculated by taking the difference of average intensities of the two groups and by searching directional code in the LUT, respectively. And also the input image is not only partitioned into multiple groups according to their intensity similarities by the histogram, but also the threshold of each group is determined by fuzzy reasoning automatically. Finally, the edges are determined through non-maximum suppression using edge confidence measure and edge linking. Applying this edge detection method to the mobile robot localization using projective invariance of the cross ratio. we demonstrate the robustness of the proposed method to the illumination changes in a corridor environment.

• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.133-160
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• 1994

In this paper, we propose a comprehensive geometric correction algorithm of Along Track Scanning Radiometer(ATSR) images. The procedure consists of two cascaded modules; precorrection and fine co-location. The pre-correction algorithm is based on the navigation model which was derived in mathematical forms. This model was applied for correction raw(un-geolocated) ATSR images. The non-systematic geometric errors are also introduced as the limitation of the geometric correction by this analytical method. A fast and automatic algorithm is also presented in the paper for co-locating nadir and forward views of the ATSR images by using a binary cross-correlation matching technique. It removes small non-systematic errors which cannot be corrected by the analytic method. The proposed algorithm does not require any auxiliary informations, or a priori processing and avoiding the imperfect co-registratio problem observed with multiple channels. Coastlines in images are detected by a ragion segmentation and an automatic thresholding technique. The matching procedure is carried out with binaty coastline images (nadir and forward), and it gives comparable accuracy and faster processing than a patch based matching technique. This technique automatically reduces non-systematic errors between two views to .$\pm$ 1 pixel.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.