• Journal of the Korea Society for Simulation
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• v.10 no.3
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• pp.59-70
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• 2001

Typical low-level image processing tasks require thousands of operations per pixel for each input image. Traditional general-purpose computers are not capable of performing such tasks in real time. Yet important features of traditional computers are not exploited by low-level image processing tasks. Since storage requirements are limited to a small number of low-precision integer values per pixel, large hierarchical memory systems are not necessary. The mismatch between the demands of low-level image processing tasks and the characteristics of conventional computers motivates investigation of alternative architectures. The structure of the tasks suggests employing an array of processing elements, one per pixel, sharing instructions issued by a single controller. In this paper we implemented various image processing filtering using the format converter. Also, we realized from conventional gray image process to color image process. This design method is based on realized the large processor-per-pixel array by integrated circuit technology This format converter design has control path implementation efficiently, and can be utilize the high technology without complicated controller hardware.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.17-26
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• 2012

This paper presents an image processing-based validation method for unrecognizable numbers in severely distorted license plate images which have been degraded by various factors including low-resolution, low light-level, geometric distortion, and periodic noise. Existing vehicle license plate recognition (LPR) methods assume that most of the image degradation factors have been removed before performing the recognition of printed numbers and letters. If this is not the case, conventional LPR becomes impossible. The proposed method adopts a novel approach where a set of reference number images are intentionally degraded using the same factors estimated from the input image. After a series of image processing steps, including geometric transformation, super-resolution, and filtering, a comparison using cross-correlation between the intentionally degraded reference and the input images can provide a successful identification of the visually unrecognizable numbers. The proposed method makes it possible to validate numbers in a license plate image taken under low light-level conditions. In the experiment, using an extended set of test images that are unrecognizable to human vision, the proposed method provides a successful recognition rate of over 95%, whereas most existing LPR methods fail due to the severe distortion.

• International Journal of Contents
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• v.3 no.2
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• pp.1-5
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• 2007

This paper proposes a technique for principal component analysis (PCA) to raise the recognition rate of a front face in a low dimension by hierarchical image and parallel processing structure. The conventional PCA shows a recognition rate of less than 50% in a low dimension (dimensions 1 to 6) when used for facial recognition. In this paper, a face is formed as images of 3 fixed-size levels: the 1st being a region around the nose, the 2nd level a region including the eyes, nose, and mouth, and the 3rd level image is the whole face. PCA of the 3-level images is treated by parallel processing structure, and finally their similarities are combined for high recognition rate in a low dimension. The proposed method under went experimental feasibility study with ORL face database for evaluation of the face recognition function. The experimental demonstration has been done by PCA and the proposed method according to each level. The proposed method showed high recognition of over 50% from dimensions 1 to 6.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.865-869
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• 2009

An image resolution enhancement is mainly utilized as pre-processing technique for various image processing application. It requires to decrease image quality deterioration such as blurring. In this paper, we propose an image resolution enhancement algorithm using low level interpolation. In the proposed algorithm, we calculate an error using low level interpolation, estimate an error image from the calculated error. The estimated error image is added interpolated high resolution image, it become lastly reconstruction image. Our experiments obtained the average PSNR about 1dB which is improved results better than conventional method for sensitive image quality. Also, subjective image quality with edge region is more clearness. The proposed method may be helpful for applications in various multimedia systems such as image restoration.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.152.4-152
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• 2001

We investigate, in this paper, the use of FPGA(Field Programmable Gate Array) architectures for real-time processing of digital linescan camera. The use of FPGAS for low-level processing represents an excellent tradeoff between software and special purpose hardware implementations. A library of modules that implement common low-level machine vision operations is presented. These modules are designed with gate-level hardware components that are compiled into the functionality of the FPGA chips. This new synchronous unidirectional interface establishes a protocol for the transfer of image and result data between modules. This reduces the design complexity and allows several different low-level operations to be applied to the same input image ...

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.27-30
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• 2003

In this paper a new approach of digitalized ‘Takbon’ is introduced. By image signal processing, the letters which were written on stones can be deciphered. Epigraphic letter is detected by digital image device, digital camera. The two dimensional digital image is preprocessed because of sensor noise and detective turbulence. Color image is transformed into grey level. The letter image is analyzed in time/frequency domain. By the resultant analysis data decisive functions are calculated. Signal Processing techniques, such as scaling, clipping, digital negative, high/low filter, morphology and so on, provide algorithms that can extract letter from stones.

• ETRI Journal
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• v.29 no.3
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• pp.353-362
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• 2007

In this paper, we propose a novel unsupervised video object extraction algorithm for individual images or image sequences with low depth of field (DOF). Low DOF is a popular photographic technique which enables the representation of the photographer's intention by giving a clear focus only on an object of interest (OOI). We first describe a fast and efficient scheme for extracting OOIs from individual low-DOF images and then extend it to deal with image sequences with low DOF in the next part. The basic algorithm unfolds into three modules. In the first module, a higher-order statistics map, which represents the spatial distribution of the high-frequency components, is obtained from an input low-DOF image. The second module locates the block-based OOI for further processing. Using the block-based OOI, the final OOI is obtained with pixel-level accuracy. We also present an algorithm to extend the extraction scheme to image sequences with low DOF. The proposed system does not require any user assistance to determine the initial OOI. This is possible due to the use of low-DOF images. The experimental results indicate that the proposed algorithm can serve as an effective tool for applications, such as 2D to 3D and photo-realistic video scene generation.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.310-318
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• 2016

Emerging technologies such as the Internet of Things (IoT) and the Advanced Driver Assistant System (ADAS) often have image transmission functions with tough constraints, like low power and/or low delay, which require that they adopt line-based, low memory compression methods instead of existing frame-based image compression standards. Bit rate control in the conventional frame-based compression systems requires a lot of hardware resources when the scope of handled data falls at the frame level. On the other hand, attempts to reduce the heavy hardware resource requirement by focusing on line-level processing yield uneven image quality through the frame. In this paper, we propose a bit rate control that maintains consistency in image quality through the frame and improves the legibility of text regions. To find the line characteristics, the proposed bit rate control tests each line for ease of compression and the existence of text. Experiments on the proposed bit rate control show peak signal-to-noise ratios (PSNRs) similar to those of conventional bit rate controls, but with the use of significantly fewer hardware resources.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.437-440
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• 2022

In modern society, the use of the image processing field is increasing rapidly due to the 4th industrial revolution and the development of IoT technology. In particular, edge detection is widely used in various fields as an essential preprocessing process in image processing applications such as image classification and object detection. Conventional methods for detecting an edge include a Sobel edge detection filter, a Roberts edge detection filter, a Prewitt edge detection filter, Laplacian of Gaussian (LoG), and the like. However, existing methods have the disadvantage of showing somewhat insufficient performance of edge detection characteristics in a low-light level environment with low contrast. Therefore, this paper proposes an edge detection algorithm based on contrast analysis to increase edge detection characteristics even in low-light level environments.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.350-352
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• 1999

하위레벨 이미지프로세싱(Low-Level Image Processing)과 이미지인식과 해석을 주로하는 상위레벨 이미지프로세싱(High-Level Image Processing)의 접목은 현존하는 기술과 연구소서는 상대적으로 접목이 힘들며 아직까지도 많은 연구가 진행되고 있다. 후자에 더 가까운 접근을 위해서 본 논문에서는 특정 이미지를 인식하는 과정에서 모양-기반 객체(Shaped-Based Object)와 기계학습(Machine Learning) 이론을 바탕으로 두 분야의 연관을 시도하였다. 이미지 내의 객체에 대한 기하학적인 특징을 얻기 위해서 모양-기반의 특징값 추출방법을 제시하고 있으며, 보다 발전된 인식을 위해서 기계학습이론을 적용시키고 있다.