• IEIE Transactions on Smart Processing and Computing
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• 제3권6호
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• pp.345-352
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• 2014

Edge detection considers the important technical details of digital image processing. Many edge detection operators already perform edge detection in digital color imaging. In this study, the edge of many real color images that represent the type of digital image was detected using a new operator in the least square approximation method, which is a type of numerical method. The Linear Fitting algorithm is computationally more expensive compared to the Canny, LoG, Sobel, Prewitt, HIS, Fuzzy, Parametric, Synthetic and Vector methods, and Robert' operators. The results showed that the new method can detect an edge in a digital color image with high efficiency compared to standard methods used for edge detection. In addition, the suggested operator is very useful for detecting the edge in a digital color image.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1331-1346
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• 2018

Aiming at the problem that the gradient-based edge detection operators are sensitive to the noise, causing the pseudo edges, a triqubit-state measurement-based edge detection algorithm is presented in this paper. Combing the image local and global structure information, the triqubit superposition states are used to represent the pixel features, so as to locate the image edge. Our algorithm consists of three steps. Firstly, the improved partial differential method is used to smooth the defect image. Secondly, the triqubit-state is characterized by three elements of the pixel saliency, edge statistical characteristics and gray scale contrast to achieve the defect image from the gray space to the quantum space mapping. Thirdly, the edge image is outputted according to the quantum measurement, local gradient maximization and neighborhood chain code searching. Compared with other methods, the simulation experiments indicate that our algorithm has less pseudo edges and higher edge detection accuracy.

• 전기전자학회논문지
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• 제22권4호
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• pp.1079-1087
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• 2018

최근 딥러닝(deep learning) 인공지능 기반의 컴퓨터 비전 분야는 각종 영상분석 분야에서 화제로 떠오르고 있다. 본 연구에서는 딥러닝 기반의 여러 이미지 인식 알고리즘 중 이미지 내에서 객체를 검출하는 데 사용되는 Faster R-CNN 알고리즘을 이용하여 화재 이미지에서 불꽃을 검출하고자 한다. 학습 과정에서 소량의 데이터셋을 통한 화재검출 정확도 향상을 위해 이미지 오그멘테이션(image augmentation) 기법을 이용하고, 이미지 오그멘테이션을 6가지 유형별로 나누어 학습하여 정확도, 정밀도, 검출률을 비교하였다. 그 결과, 이미지 오그멘테이션의 종류가 늘어날수록 검출률이 상승하지만, 다른 객체 검출 모델들의 일반적인 정확도와 검출률의 관계와 마찬가지로 오검출율 또한 10%에서 최대 30%까지 증가하게 됨을 확인하였다.

• Journal of information and communication convergence engineering
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• 제8권4호
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• pp.405-410
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• 2010

Image processing operations like smoothing and edge detection, and many more are very widely used in areas like Computer Vision. We classify the image processing domain as seven branches-image acquirement and output, image coding and compression, image enhancement and restoration, image transformation, image segmentation, image description, and image recognition and description. We implemented algorithms of gaussian smoothing, laplace sharpening, image contrast effect, image black and white effect, image fog effect, image bright and dark effect, image median filter, and canny edge detection. Such experimental results show the figures respectively.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.27-31
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• 2005

Vehicle detector system based on image processing technology is a significant domain of ITS (Intelligent Transportation System) applications due to its advantages such as low installation cost and it does not obstruct traffic during the installation of vehicle detection systems on the road[1]. In this paper, we propose architecture for vehicle detection by using image processing. The architecture consists of two main parts such as an image processing part, using high speed FPGA, decision and calculation part using CPU. The CPU part takes care of total system control and synthetic decision of vehicle detection. The FPGA part assumes charge of input and output image using video encoder and decoder, image classification and image memory control.

• 대한원격탐사학회지
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• 제16권4호
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• pp.355-365
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• 2000

The interferometric correlation, or coherence, is calculated to measure the variance of the interferometric phase and amplitude within the neighbourhood of any location within the image at a result of SAR (Synthetic Aperture Radar) interferometric process which utilizes the phase information of the images. The coherence contains additional information that is useful for detecting point targets which change their location in an area of interest (AOI). In this research, a RGB colour composite image was generated with a intensity image (master image), a intensity change image as a difference between master image and slave image, and a coherence image generated as a part of SAR interferometric processing. We developed a technique performing detection of a point target movement using SAR interferometry and applied it to suitable tandem pair images of ERS-1 and ERS-2 as test data. The possibility of change detection of a point target in the AOI could be identified with the technique proposed in this research.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2013년도 추계학술대회
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• pp.165-168
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• 2013

Image distortions, such as quantization errors, can have a severe negative impact on the performance of computer vision algorithms, and, more specifically, on object detection algorithms. State-of-the-art implementations of the JPEG-2000 image coder commonly allocate the available bits to minimize the Mean-Squared-Error (MSE) distortion between the original image and the resulting compressed image. However, considering that some state-of-the-art object detection methods use the gradient information as the main image feature, an improved object detection performance is expected for JPEG-2000 image coders that allocate the available bits to minimize the distortions on the gradient content. Accordingly, in this work, the Gradient Mean-Squared-Error (GMSE) based JPEG-2000 coder presents an improved object detection performance over the MSE based JPEG-2000 image coder when the object of interest is located at the same spatial location of the image regions with the strongest gradients and also for high bit-rates. For low bit-rates (e.g. 0.07bpp), the GMSE based JPEG-2000 image coder becomes overly selective in choosing the gradients to preserve, and, as a result, there is a greater chance of mismatch between the spatial locations of the gradients that the coder is trying to preserve and the spatial locations of the objects of interest.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.203-211
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• 2021

Image tampering detection and localization have become an active area of research in the field of digital image forensics in recent times. This is due to the widespread of malicious image tampering. This study presents a new method for image tampering detection and localization that combines the advantages of dilated convolution, residual network, and UNET Architecture. Using the UNET architecture as a backbone, we built the proposed network from two kinds of residual units, one for the encoder path and the other for the decoder path. The residual units help to speed up the training process and facilitate information propagation between the lower layers and the higher layers which are often difficult to train. To capture global image tampering artifacts and reduce the computational burden of the proposed method, we enlarge the receptive field size of the convolutional kernels by adopting dilated convolutions in the residual units used in building the proposed network. In contrast to existing deep learning methods, having a large number of layers, many network parameters, and often difficult to train, the proposed method can achieve excellent performance with a fewer number of parameters and less computational cost. To test the performance of the proposed method, we evaluate its performance in the context of four benchmark image forensics datasets. Experimental results show that the proposed method outperforms existing methods and could be potentially used to enhance image tampering detection and localization.

• 한국멀티미디어학회논문지
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• 제7권12호
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• pp.1692-1699
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• 2004

Many researchers have developed various techniques for detection of human faces in ordinary still images. Face detection is the first imperative step of human face recognition systems. The two main problems of human face detection are how to cutoff the running time and how to reduce the number of false positives. In this paper, we present frontal and near-frontal face detection algorithm in still gray images using a thick edge image and neural network. We have devised a new filter that gets the thick edge image. Our overall scheme for face detection consists of two main phases. In the first phase we describe how to create the thick edge image using the filter and search for face candidates using a whole face detector. It is very helpful in removing plenty of windows with non-faces. The second phase verifies for detecting human faces using component-based eye detectors and the whole face detector. The experimental results show that our algorithm can reduce the running time and the number of false positives.

• 반도체디스플레이기술학회지
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• 제15권3호
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• pp.51-56
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• 2016

This paper proposes a new image enhancement method for an infrared thermal image. The proposed method uses both Laplacian and Prewitt edge detectors. Without a visible light, it uses an infrared image for the edge detection. The method subtracts contour images from the infrared thermal image. It results black contours of objects in the infrared thermal image. That makes the objects in the infrared thermal image distinguished clearly. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiments were conducted by using various infrared thermal images. The results show that the proposed method is successful for image enhancement of an infrared thermal image.