• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.329-332
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• 2009

Speckle noise reduction for ultrasound CT image using morphological adaptive median filtering based on edge preservation is presented in this paper. Speckle noise is multiplicative feature and causes ultrasound image to degrade widely from transducer. An input image is classified into edge region and homogeneous region in preprocessing. The speckle is reduced by morphological operation on the 2D gray scale by using convolution and correlation, and edges are preserved. The adaptive median is processed to reduce an impulse noise. As the result the proposed method enhances the image to about 20% in comparison with Winer filter by Edge Preservation Index and PSNR.

• 한국정밀공학회지
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• 제10권4호
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• pp.170-179
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• 1993

In this study, self-organized neural network is used to solve the vorrespondence problem of the axial stereo image. Edge points are extracted from a pair of stereo images and then the edge points of rear image are assined to the output nodes of neural network. In the matching process, the two input nodes of neural networks are supplied with the coordi- nates of the edge point selected randomly from the front image. This input data activate optimal output node and its neighbor nodes whose coordinates are thought to be correspondence point for the present input data, and then their weights are allowed to updated. After several iterations of updating, the weights whose coordinates represent rear edge point are converged to the coordinates of the correspondence points in the front image. Because of the feature map properties of self-organized neural network, noise-free and smoothed depth data can be achieved.

• 인지과학
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• 제11권2호
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• pp.53-58
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• 2000

본 논문은 FCM 군집화 알고리즘을 사용하여 표정영상에서 특징점들을 추출한 후 추출된 특징점으로부터 Gabor 웨이브렛들을 이용하여 표정영상의 국소영역을 복원한다. 얼굴의 특징점 추출은 두단계로 이루어진다. 1단계는 이차원 Gabor 웨이브렛 계수 히스토그램의 평균값을 적용하여 얼굴의 주요 요소성분들의 경계선을 추출한 후, 2단계에서는 추출된 경계선 정보로부터 FCM 군집화 알고리즘을 사용하여 얼굴의 주요 요소성분들의 최종적인 특징점들을 추출한다. 본 연구에서는 FCM 군집화 알고리즘을 이용하여 추출된 적은 수의 특징점들 만으로도 표정영상의 주요 요소들을 복원할 수 있음을 제시한다. 이것은 인간의 얼굴 표정인식 뿐만아니라 물체인식에도 적용되어질 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.894-907
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• 2022

The features extracted by convolutional neural networks are more descriptive of images than traditional features, and their convolutional layers are more suitable for retrieving images than are fully connected layers. The convolutional layer features will consume considerable time and memory if used directly to match an image. Therefore, this paper proposes a feature weighting and region integration method for convolutional layer features to form global feature vectors and subsequently use them for image matching. First, the 3D feature of the last convolutional layer is extracted, and the convolutional feature is subsequently weighted again to highlight the edge information and position information of the image. Next, we integrate several regional eigenvectors that are processed by sliding windows into a global eigenvector. Finally, the initial ranking of the retrieval is obtained by measuring the similarity of the query image and the test image using the cosine distance, and the final mean Average Precision (mAP) is obtained by using the extended query method for rearrangement. We conduct experiments using the Oxford5k and Paris6k datasets and their extended datasets, Paris106k and Oxford105k. These experimental results indicate that the global feature extracted by the new method can better describe an image.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.1492-1494
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• 1987

This paper describes a hardware architecture called Edge Painting Machine for real time generation of scan line images for raster scan graphics display. The Edge Painting Machine consists of Scanline Processor which converts polygon data sorted in their depth priority into a set of scan line commands for each scan line, and Edge Painting Tree which converts the scanline commands set into a raster line image. Edge painting tree has been designed using combinational logic circuit. The designed circuit has been simulated to verify the proper functioning. A salient feature of the EPT is that hardware composition is simple, because each processor is constituted by only combinational logic circuit.

• 대한전자공학회논문지
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• 제26권8호
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• pp.1247-1256
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• 1989

In this paper, we propose the dynamic scene segmentation algorithm using a cross mask and edge information. This method, a combination of the conventioanl feature-based and pixel-based approaches, uses edges as features and determines moving pixels, with a cross mask centered on each edge pixel, by computing similarity measure between two consecutive image frames. With simple calcualtion the proposed method works well for image consisting of complex background or several moving objects. Also this method works satisfactorily in case of rotaitional motion.

• 한국산학기술학회논문지
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• 제7권6호
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• pp.1214-1218
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• 2006

본 논문에서는 파노라마 영상 생성의 처리 속도 개선 방법을 제안한다. 그 방법은 불변 특징에 기반한 파노라마 생성 방법으로 영상 축소와 영상 에지 정보를 이용하는 방법이다. 영상을 축소하고 에지의 위치에 대해서만 특징 묘사자를 적용함으로써 특징점의 개수를 줄여 속도 개선을 실현한다. 실험에서 640$\times$480 크기의 24비트 칼라 영상에 대해 기존의 방법보다 3.26$\sim$13.87%의 속도 개선의 효과를 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2213-2216
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• 2003

In this paper, we propose the face identification method which is robust for lighting based on the feature points method. First of all, the proposed method extracts an edge of facial feature. Then, by the hough transform, it determines ellipse parameters of each facial feature from the extracted edge. Finally, proposed method performs the face identification by using parameters. Even if face image is taken under various lighting condition, it is easy to extract the facial feature edge. Moreover, it is possible to extract a subject even if the object has not appeared enough because this method extracts approximately the parameters by the hough transformation. Therefore, proposed method is robust for the lighting condition compared with conventional method. In order to show the effectiveness of the proposed method, computer simulations are done by using the real images.

• 센서학회지
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• 제30권5호
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• pp.286-289
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• 2021

Image classification is one of the fundamental applications of computer vision. It enables a system to identify an object in an image. Recently, image classification applications have broadened their scope from computer applications to edge devices. The convolutional neural network (CNN) is the main class of deep learning neural networks that are widely used in computer tasks, and it delivers high accuracy. However, CNN algorithms use a large number of parameters and incur high computational costs, which hinder their implementation in edge hardware devices. To address this issue, this paper proposes a lightweight image classifier that provides good accuracy while using fewer parameters. The proposed image classifier diverts the input into three paths and utilizes different scales of receptive fields to extract more feature maps while using fewer parameters at the time of training. This results in the development of a model of small size. This model is tested on the CIFAR-10 dataset and achieves an accuracy of 90% using .26M parameters. This is better than the state-of-the-art models, and it can be implemented on edge devices.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1991년도 추계종합학술발표회논문집
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• pp.77-80
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• 1991

This paper proposes a new feature extraction method which is effectively used in character recognition, and validate the effectiveness through various computational methods for similiarity degree. To get feature vectors used in this method, Hough transform is applied to character image, which is used for edge extraction in image processing. By that transformation technique, strokes could be extracted and feature vectors constructed suitably. The characteristic of this method is solving the difficulties in stroke extraction through transform space analysis, which is induced by noise and blurring, and representing high recognition rate 99.3% within 10 candidates in relative low dimension.