• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.626-631
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• 1997

This paper represents the design of a real-time image preprocessing system. The preprocessing system performs hardware-wise mask operations and thresholding operations at the speed of camera output single rate. The preprocessing system consists of the preprocessing board and the main processing board. The preprocessing board includes preprocessing unit that includes a $5\times5$ mask processor and LUT, and can perform mask and threshold operations in real-time. To achieve high-resolution image input data($20485\timesn$), the preprocessing board has a linescan camera interface. The main processing board includes the image processor unit and main processor unit. The image processor unit is equipped with TI's TMS320C32 DSP and can perform image processing algorithms at high speed. The main processor unit controls the operation of total system. The proposed system is faster than the conventional CPU based system.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.573-586
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• 2022

Corrosion can cause dangerous and expensive damage and failures of ship hulls and equipment. Therefore, it is necessary to maintain the vessel by periodic corrosion inspections. During visual inspection, many corrosion locations are inaccessible for many reasons, especially safety's point of view. Including subjective decisions of inspectors is one of the issues of visual inspection. Automation of visual inspection is tried by many pieces of research. In this study, we propose image preprocessing methods by image patch segmentation and thresholding. YOLOv5 was used as an object detection model after the image preprocessing. Finally, it was evaluated that corrosion detection performance using the proposed method was improved in terms of mean average precision.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.785-792
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• 2023

Because hyperspectral imaging provides detailed spectral information across a broad range of wavelengths, it can be utilized in numerous applications, including environmental monitoring, food quality inspection, medical diagnosis, material identification, art authentication, and crime scene analysis. However, hyperspectral images often contain various types of distortions due to the environmental conditions during image acquisition, which necessitates the proper removal of these distortions through a data preprocessing process. In this study, a preprocessing method was investigated to effectively correct the distortion caused by artificial light sources used in indoor hyperspectral imaging. For this purpose, a halogen-tungsten artificial light source was installed indoors, and hyperspectral images were acquired. The acquired images were then corrected for distortion using a preprocessing that does not require complex auxiliary equipment. After the corrections were made, the results were analyzed. According to the analysis, a statistical transformation technique using mean and standard deviation with reference to a reference signal was found to be the most effective in correcting distortions caused by artificial light sources.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.1
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• pp.38-45
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• 2007

We propose a preprocessing method for removing floating particles from underwater images based on an analysis of the image features. We compared baseline JPEG and wavelet codec methods to determine the method best suited for underwater images. The proposed preprocessing method enhanced the compression ratio and resolution, and provided an efficient means of compressing the images. The wavelet codec method yielded better compression ratios and image resolutions. The results suggest that the wavelet codec method linked with the proposed preprocess method provides an efficient codec processor and transmission system for underwater images that are used for searches and transmitted via ultrasonic communications.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.814-818
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• 2004

Off-line handwritten character recognition is in difficulty of incomplete preprocessing because it has not dynamic and timing information besides has various handwriting, extreme overlap of the consonant and vowel and many error image of stroke. Consequently off-line handwritten character recognition needs to study about preprocessing of various methods such as binarization and thinning. This paper considers running time of watershed algorithm and the quality of resulting image as preprocessing For off-line handwritten Korean character recognition. So it proposes application of effective watershed algorithm for segmentation of character region and background region in gray level character image and segmentation function for binarization image and segmentation function for binarization by extracted watershed image. Besides it proposes thinning methods which effectively extracts skeleton through conditional test mask considering running time and quality. of skeleton, estimates efficiency of existing methods and this paper's methods as running time and quality. Watershed image conversion uses prewitt operator for gradient image conversion, extracts local minima considering 8-neighborhood pixel. And methods by using difference of mean value is used in region merging step, Converted watershed image by means of this methods separates effectively character region and background region applying to segmentation function. Average execution time on the previous method was 2.16 second and on this paper method was 1.72 second. We prove that this paper's method removed noise effectively with overlap stroke as compared with the previous method.

• Journal of IKEEE
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• v.27 no.4
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• pp.561-566
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• 2023

With the development of deep learning, it is possible to solve various computer non-specialized problems such as image processing. However, most image processing methods use only the visual information of the image to process the image. Text data such as descriptions and annotations related to images may provide additional tactile and visual information that is difficult to obtain from the image itself. In this paper, we intend to improve image classification accuracy through a deep learning model that analyzes images and texts using image-text pairs. The proposed model showed an approximately 11% classification accuracy improvement over the deep learning model using only image information.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.4-49
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• 2001

Digit recognition based on backpropagation neural networks, as an important application of pattern recognition, was attracted much attention. Although it has the advantages of parallel calculation, high error-tolerance, and learning capability, better recognition effects can only be achieved with some specific fixed format input of the digit image. Therefore, digit image preprocessing ability directly affects the accuracy of recognition. Here using Matlab software, the digit image was enhanced by resizing and neutral-rotating the extracted digit image, which improved the digit recognition capability of the backpropagation neural network under practical conditions. This method may also be helpful for recognition of other patterns with backpropagation neural networks.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.144-152
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• 2017

Among the technologies for implementing autonomous vehicles, advanced driver assistance system is a key technology to support driver's safe driving. In the technology using the vision sensor having a high utility, various preprocessing methods are used prior to feature extraction for lane detection. However, in the existing methods, the unnecessary lane candidates such as cars, lawns, and road separator in the road area are false positive. In addition, there are cases where the lane candidate itself can not be extracted in the area under the overpass, the lane within the dark shadow, the center lane of yellow, and weak lane. In this paper, we propose an efficient preprocessing method using k-means clustering for image division and the HSV color model. When the proposed preprocessing method is applied, the true positive region is maximally maintained during the lane detection and many false positive regions are removed.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2485-2489
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• 2005

This paper introduces a new preprocessing scheme to replace original data of gray scale images with particular ordered data so that performance of lossless compression can be improved more efficiently. As a kind of preprocessing technique to maximize performance of entropy encoder, the proposed method converts the input image data into more compressible form. Before encoding a stream of the input image, the proposed preprocessor counts co-occurrence frequencies for neighboring pixel pairs. Then, it replaces each pair of adjacent gray values with particular ordered numbers based on the investigated co-occurrence frequencies. When compressing ordered image using entropy encoder, we can expect to raise compression rate more highly because of enhanced statistical feature of the input image. In this paper, we show that lossless compression rate increased by up to 37.85% when comparing results from compressing preprocessed and non-preprocessed image data using entropy encoder such as Huffman, Arithmetic encoder.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.8
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• pp.35-43
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• 1992

We have presented the tracing algorithm for center pixel of character image. Character image was read by scanner device. Performing the tracing process, it can be possible to detect feature points, such as branch point, stroke of 4 directions. So, the tracing process covers the thinning and feature point detection process for improving the processing time. Usage of suggested tracing algorithm instead of thinning that is the preprocessing of character recognition increases speed up to 5 times. The preprocessing chip has been designed by using single layer perceptron algorithm.