• 융합신호처리학회논문지
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• 제19권2호
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• pp.35-41
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• 2018

The analysis of plankton species distribution in sea or fresh water is very important in preserving marine ecosystem health. Since manual analysis is infeasible, many automatic approaches were proposed. They usually use images from in situ towed underwater imaging sensor or specially designed, lab mounted microscopic imaging system. Normally they assume that only single plankton is present in an image so that, if there is a clumping among multiple plankton of same species (homogeneous clumping) or if there are multiple plankton of different species scattered in an image (heterogeneous interspersion), they have a difficulty in recognition. In this work, we propose a deep learning based method that can detect and recognize individual plankton in images with homogeneous clumping, heterogeneous interspersion, or combination of both.

• Journal of Information Processing Systems
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• 제19권6호
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• pp.858-869
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• 2023

As one of the pivotal techniques of image restoration, single-channel blind source separation (SCBSS) is capable of converting a visual-only image into multi-source images. However, image degradation often results from multiple mixing methods. Therefore, this paper introduces an innovative SCBSS algorithm to effectively separate source images from a composite image in various mixed modes. The cornerstone of this approach is a novel triple generative adversarial network (TriGAN), designed based on dual learning principles. The TriGAN redefines the discriminator's function to optimize the separation process. Extensive experiments have demonstrated the algorithm's capability to distinctly separate source images from a composite image in diverse mixed modes and to facilitate effective image restoration. The effectiveness of the proposed method is quantitatively supported by achieving an average peak signal-to-noise ratio exceeding 30 dB, and the average structural similarity index surpassing 0.95 across multiple datasets.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.839-844
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• 2003

This paper presents a method to segment wafer ID marks on poor quality images under uncontrolled lighting conditions of the semiconductor process. The active multiple templates matching method is suggested to search ID areas on wafers and segment them into meaningful regions and it would have been impossible to recognize characters using general OCR algorithms. This active template model is designed by applying a snake model that is used for active contour tracking. Active multiple template model searches character areas and segments them into single characters optimally, tracking each character that can vary in a flexible manner according to string configurations. Applying active multiple templates, the optimization of the snake energy is done using Greedy algorithm, to maximize its efficiency by automatically controlling each template gap. These vary according to the configuration of character string. Experimental results using wafer images from real FA environment are presented.

• 전자공학회논문지C
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• 제34C권4호
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• pp.36-44
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• 1997

An efficient context-free multiple-object segmentation using attention operator based on modified generalized symmetry transform is proposed and implemented by modifying a radial basis function network. By using the difference of intensity gradient, instead of te intensity gradient itself, in generalized symmetry tranform so as to make the attention operator to preserve the edges of the objects shape, an efficient context-free multiple-object segementation is proposed in which no a priori shape informtion on the objects is requried. The attention operator is implemented by using a modified radial basis function network which can reflect symmetry, and by using te edge pyramid of the input image, both of the local and the global symmetry of the objects are reflected simultaneously to make the multiple-object with different sizes be segmented with a singel fixed-size $n\timesm$ can be done with O(n) complexity. The simulaton results show that the proposed algorithm can efficiently be used in context-free multiple-object segmentation even for the low contrast IR images as well as for the images from the camera.

• Current Optics and Photonics
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• 제2권4호
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• pp.315-323
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• 2018

A new multiple-image encryption scheme that is based on a compressive ghost imaging concept along with a Fourier transform sampling principle has been proposed. This further improves the security of the scheme. The scheme adopts a Fourier transform to sample the original multiple-image information respectively, utilizing the centrosymmetric conjugation property of the spatial spectrum of the images to obtain each Fourier coefficient in the most abundant spatial frequency band. Based on this sampling principle, the multiple images to be encrypted are grouped into a combined image, and then the compressive ghost imaging algorithm is used to improve the security, which reduces the amount of information transmission and improves the information transmission rate. Due to the presence of the compressive sensing algorithm, the scheme improves the accuracy of image reconstruction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• 한국측량학회지
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• 제36권3호
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• pp.127-134
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• 2018

Electric transmission towers are facilities to transport electrical power from a plant to an electrical substation. The towers are connected using power lines that are installed with a proper sag by loosening the cable to lower the tension and to secure the sufficient clearance from the ground or nearby objects. The power line sag may extend over the tolerance due to the weather such as strong winds, temperature changes, and a heavy snowfall. Therefore the periodical mapping of the power lines is required but the poor accessibility to the power lines limit the work because most power lines are placed at the mountain area. In addition, the manual mapping of the power lines is also time-consuming either using the terrestrial surveying or the aerial surveying. Therefore we utilized multiple overlapping images acquired from a low-cost drone to automatically reconstruct the power lines in the object space. Two overlapping images are selected for epipolar image resampling, followed by the line extraction for the resampled images and the redundant images. The extracted lines from the epipolar images are matched together and reconstructed for the power lines primitive that are noisy because of the multiple line matches. They are filtered using the extracted line information from the redundant images for final power lines points. The experiment result showed that the proposed method successfully generated parabolic curves of power lines by interpolating the power lines points though the line extraction and reconstruction were not complete in some part due to the lack of the image contrast.

• 한국지능시스템학회논문지
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• 제22권6호
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• pp.741-747
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• 2012

본 논문에서는 감시 시스템에서 다중 물체를 감지하고 추적하기 위한 빠르고 강인한 알고리즘을 제안한다. 제안된 시스템은 감지 모듈과 추적 모듈, 2개의 모듈로 구성된다. 이동 물체의 감지 모듈에서는 우리는 영상 이진화 기법과 프레임별 영상을 이용하여 움직이는 물체를 추출하고, 모폴로지 기법을 이용하여 각종 노이즈를 제거한다. 또한, 블록 기반 히스토그램기법을 사용하여 인간과 다른 물체를 구분하는 방법을 제안한다. 이동 물체의 추적 모듈에서는 색상 기반 추적 알고리즘과 칼만 필터가 이용된다. 먼저 RGB 영상을 HSV 영상으로 변환한 후, 다중 물체를 추적하기위해 색상 기반 추적 알고리즘을 사용한다. 이때 다른 물체와의 충돌시 물체를 추적하기 위해 칼만 필터를 사용한다. 마지막으로, 제안된 방법을 몇 가지 실험을 통해 그 효용성 및 응용 가능성을 보인다.

• 한국정보통신학회논문지
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• 제22권11호
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• pp.1462-1467
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• 2018

단일 노출 영상, 또는 다중 노출 영상을 사용하여 저조도 영상의 화질 개선 알고리즘이 수많이 연구되고 있다. 저조도 영상은 명암이 낮고, 잡음이 많아 피사체의 정보를 식별하기에 한계가 있다. 본 논문에서는 듀얼카메라로 촬영한 다중 노출 영상 2개를 이용하여 저조도 영상의 화질 개선하는 알고리즘의 하드웨어 설계를 제안한다. 제안하는 하드웨어 구조는 전달함수를 사용하여 프레임 메모리와 라인 메모리를 쓰지 않는 방식으로 실시간 처리로 설계되었다. 그리고 제안하는 하드웨어 설계는 Verilog로 설계했고, Modelsim을 사용하여 검증했다. 마지막으로 Xilinx사의 xc7z045-2ffg900을 목표 보드로 이용하여 FPGA를 구현했을 때 최대 동작 주파수 167.617MHz로 확인하였고, 영상 크기가 $1920{\times}1080$ 일 때, 소요된 총 클럭 사이클은 2,076,601이며 80.7fps로 실시간 처리가 가능하다.

• 한국컴퓨터정보학회논문지
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• 제19권1호
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• pp.23-30
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• 2014

이미지 기술자(descriptor)를 이용한 정합은 최근까지 컴퓨터 비전과 패턴인식 분야에서 사용되고 있는 강력한 정합 방법이다. 그러나 3차원 시점이 변화되거나 밝기가 변화된 이미지, 반복된 패턴이 포함된 이미지 등에서 잘못된 정합들이 발생한다. 본 논문에서는 반복된 패턴이 포함되어 있는 이미지에서 잘못된 정합들이 많이 발생하는 문제점에 대해 기술하고 이를 분석하여 잘못된 정합들을 제거할 수 있는 방법을 제안한다. MDMF(Multiple Descriptors-based Mismatch Filtering) 방법은 각 특징점에 대해 인접한 여러 개의 특징점들의 기술자들을 사용하여 다중 기술자를 생성한 후 이를 활용하여 잘못된 정합들을 제거한다. 실험에서는 크기 변환, 회전 변환, 어파인 변환에 대해 기존 SIFT와 ASIFT의 정합율을 MDMF를 이용해 제거한 정합율과 비교하여 MDMF가 잘못된 정합을 성공적으로 제거할 수 있음을 보였다.