• Journal of Information Processing Systems
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• v.10 no.1
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• pp.55-68
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• 2014

Automatic segmentation of foreground text from the background in degraded document images is very much essential for the smooth reading of the document content and recognition tasks by machine. In this paper, we present a novel approach to the binarization of degraded document images. The proposed method uses a new local contrast feature extracted based on the stroke width of text. First, a pre-processing method is carried out for noise removal. Text boundary detection is then performed on the image constructed from the contrast feature. Then local estimation follows to extract text from the background. Finally, a refinement procedure is applied to the binarized image as a post-processing step to improve the quality of the final results. Experiments and comparisons of extracting text from degraded handwriting and machine-printed document image against some well-known binarization algorithms demonstrate the effectiveness of the proposed method.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1137-1139
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• 2003

This paper proposed a practical method for building detection and extraction using airborne laser scanning data. The proposed method consists mainly of two processes: low and high level processes. The major distinction from the previous approaches is that we introduce a concept of pseudogrid (or binning) into raw laser scanning data to avoid the loss of information and accuracy due to interpolation as well as to define the adjacency of neighboring laser point data and to speed up the processing time. The approach begins with pseudo-grid generation, noise removal, segmentation, grouping for building detection, linearization and simplification of building boundary , and building extraction in 3D vector format. To achieve the efficient processing, each step changes the domain of input data such as point and pseudo-grid accordingly. The experimental results shows that the proposed method is promising.

• Journal of Animal Environmental Science
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• v.5 no.3
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• pp.139-148
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• 1999

This study was conducted to develop an image processing system which can classify the pig's thermoregulatory behavior under the different environmental conditions. The 4 pigs of 25kg were housed in the environmentally controlled chamber(1.4m$\times$2.2m floor space). Postural behavior of the pigs was captured with an CCD color camera. The raw behavioral images were processed by thresholoding, reduction, separation of slightly contacted pigs, labeling, noise removal, computation of number of labels, and classification of the pig's behavior. The correct classification rate of the image processing system was 97.8%(88 out of 90 testing images). The results of this study showed that the image processing system could be used for a behavior-based automatic environmental controller.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.124-133
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• 1994

Many of non-contact measuring systems are used to estimate surface characteristics owing to their advantages of high speed and undanaged test. In this paper, a new measuring system is proposed to acquire image from CCD camera through back light illumination. Lowpass filter is very useful in view of noise removal and optimum binary image can be made through histogram equalization which is one of the histogram technique to maximize brightness intensity between workpiece and background. Laplacian operator is used to detect workpiece edge from binary image. In case of image treatment applying Laplacian operator, surface roughness is calculated by introducing conversion coefficient for coordinate of pixel which edge is composed of. In summary, the work is concerned with the development of a new technique for roughness measurement by the image processing in turning.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.136-142
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• 2024

Haze is a very common phenomenon that degrades or reduces the visibility. It causes various problems where high quality images are required such as traffic and security monitoring. So haze removal from images receives great attention for clear vision. Due to its huge impact, significant advances have been achieved but the task yet remains a challenging one. Recently, different types of deep generative adversarial networks (GAN) are applied to suppress the noise and improve the dehazing performance. But it is unclear how these algorithms would perform on hazy images acquired "in the wild" and how we could gauge the progress in the field. This paper aims to bridge this gap. We present a comprehensive study and experimental evaluation on diverse GAN models in single image dehazing through benchmark datasets.

• ETRI Journal
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• v.46 no.3
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• pp.367-378
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• 2024

We propose a number-theory-based quantized mathematical optimization scheme for various NP-hard and similar problems. Conventional global optimization schemes, such as simulated and quantum annealing, assume stochastic properties that require multiple attempts. Although our quantization-based optimization proposal also depends on stochastic features (i.e., the white-noise hypothesis), it provides a more reliable optimization performance. Our numerical analysis equates quantization-based optimization to quantum annealing, and its quantization property effectively provides global optimization by decreasing the measure of the level sets associated with the objective function. Consequently, the proposed combinatorial optimization method allows the removal of the acceptance probability used in conventional heuristic algorithms to provide a more effective optimization. Numerical experiments show that the proposed algorithm determines the global optimum in less operational time than conventional schemes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.165-168
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• 2008

With the development of the information technology in recent years, the innovation of multimedia information technology also has been accelerated. Many fields of the digital image processing technologies as image data compression, recognition. restoration, etc. are now being studied actively. When transmitting and saving digital images, noise would be made, and we are using the image filters to remove the noise. The Image Filter used Digital Image Process basically has a two-dimensional structure. There are two methods of the filter's creation - One is reiterating one dimension, and the other is using an indivisible two-dimension image filter. The space domain method using mask is the latter one. That is inserting the value-multiplied pixels values faced each other when the two-dimension filter overlapped on input image- to the filter value's center position and the same position in the image. The image filter is being used widely along with one-dimension filter, according each noise. Most people are using various median filters to remove the impulse noise. However, in this paper, I suggested a powerful switching median filter and compared with conventional method for verification.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.

• Journal of the Korean Society of Radiology
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• v.13 no.7
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• pp.1035-1043
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• 2019

The object of is this research is to find out the optimal Tesla by evaluating SNR and CNR, after testing 1.5 T and 3.0 T. The randomly selected patients tested by nasopharynx MRI transmitted in PACS were applied to the research. Two MRI units(1.5 T, 3.0 T) was used for analyzing the data. As a method of analysis, in T1W highlighting and T1 fat removal images, we set up a certain area of interest and evaluated the SNR and CNR on tongue, spinal cord, masseter muscle, fat, parotid gland, and tumor tissue. We evaluated the SNR and CNR by quantitative analysis of six tissue, measuring the quality of images for uniform fat removal, magnetic sensitivity artifact on a four-point scale by qualitative analysis. The statistical significance of this date analysis was based on independent sample verification and was accepted when the P value was less than 0.05. As a result of analysis of both devices, 3.0 T was high in the quantitative evaluation, while 1.5 T was high in the qualitative evaluation. Considering the advantages and disadvantages of each device, and if the device is selected complementarily and applied to patients, it is believed that it will provide the optimal information.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.413-419
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• 2023

Light Emitting Diodes(LEDs) are widely utilized not only in lighting but also in various applications such as mobile phones, automobiles, displays, etc. The integration of LED lighting with communication, specifically Visible Light Communication(VLC), has gained significant attention. This paper presents the direct implementation and experimentation of a Vehicle-to-Vehicle(V2V) Visible Light Communication system using commonly used red and yellow LEDs in typical vehicles. Data collected from the leading vehicle, including positional and speed information, were modulated using Non-Return-to-Zero On-Off Keying(NRZ-OOK) and transmitted through the rear lights equipped with red and yellow LEDs. A photodetector(PD) received the visible light signals, demodulated the data, and restored it. To mitigate the interference from fluorescent lights and natural light, a PD for interference removal was installed, and an interference removal device using a polarizing filter and a differential amplifier was employed. The performance of the proposed visible light communication system was analyzed in an ideal case, indoors and outdoors environments. In an outdoor setting, maintaining a distance of approximately 30[cm], and a transmission rate of 4800[bps] for inter-vehicle data transmission, the red LED exhibited a performance improvement of approximately 13.63[dB], while the yellow LED showed an improvement of about 11.9[dB].