• Smart Media Journal
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• v.2 no.1
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• pp.31-36
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• 2013

A robust and efficient text extraction is very important for an accuracy of Optical Character Recognition (OCR) systems. Natural scene images with degradations such as uneven illumination, perspective distortion, complex background and multi color text give many challenges to computer vision task, especially in text extraction. In this paper, we propose a method for extraction of the text in signboard images based on a combination of mean shift algorithm and weighting scheme of hue and saturation in HSI color space for clustering algorithm. The number of clusters is determined automatically by mean shift-based density estimation, in which local clusters are estimated by repeatedly searching for higher density points in feature vector space. Weighting scheme of hue and saturation is used for formulation a new distance measure in cylindrical coordinate for text extraction. The obtained experimental results through various natural scene images are presented to demonstrate the effectiveness of our approach.

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1135-1142
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• 2007

Front-projection type displays make it difficult to apply traditional skin color detection for human hand because the projected beam not only reaches to the screen but also to the user's hand. This paper solves this problem by modeling the distortion between original image and its final camera input. Our approach improves hand detection rate by modeling of interference effect among color channels and of intra-frame intensity and also by introducing adaptive threshold for color difference in skin region.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.23-27
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• 2024

Underwater images are typically degraded due to color distortion, light absorption, scattering, and noise from artificial light sources. Restoration of these images is an essential task in many underwater applications. In this paper, we propose a two-phase deep learning-based method, Underwater Deep Curve Estimation (UWDCE), designed to effectively enhance the quality of underwater images. The first phase involves a white balancing and color correction technique to compensate for color imbalances. The second phase introduces a novel deep learning model, UWDCE, to learn the mapping between the color-corrected image and its best-fitting curve parameter maps. The model operates iteratively, applying light-enhancement curves to achieve better contrast and maintain pixel values within a normalized range. The results demonstrate the effectiveness of our method, producing higher-quality images compared to state-of-the-art methods.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1027-1038
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• 2015

For last several decades, the color correction methods have been proposed for HDR(high dynamic range) images. However, color distortion problems take place after correcting the colors such as halos, dominant color as well known. Accordingly, this article presents a novel approach in which the method consists of tone-mapping method and cone response function. In the proposed method, the tone mapping method is used to improve the contrast in the given HDR image based on chromatic and achromatic based on the CIEXYZ tristimulus value, expressed in c/m². The cone response function is used to deal with mismatch between corrected image and displayed image as well as to estimate various human visual effects based on the CMCAT2000 color appearance model. The experimental results show that the proposed method yields better performance of color correction over the conventional method in subjective and quantitative quality, and color reproduction.

• The Journal of Information Technology
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• v.5 no.1
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• pp.99-111
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• 2002

Due to non-linear characteristics of color spaces, color corrections using linear conversions for real image near color reappearance causes color distortions. In order to overcome this problem, the Bezier Curve, constructed with a set of arbitrary plane in the linear theory, has been used. However, the Bezier Curve increases in proportion to the number of data points, resulting in higher computational complexities. This paper attempts to use a Triplicated Piecewise Bezier Cubic-Curve (TPBC-Curve) of which the degree is cubic on the whole interval while keeping the characteristics of Bezier Curves. By Comparing the TPBC-Curve with Bezier Curve of 20 degree, the paper not only reduces the distortion during color correction but also lessens the relative increase of workload that is caused by the color correction in a small zone.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8C
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• pp.497-504
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• 2011

This paper presents an adaptive retinex algorithm, In order to solve typical problems of retinex algorithm such as expensive computational cost, halo artifact, and color distortion, a function of skewness that represents a statistical distribution of pixels is defined to compensate contrast and color distortion. The experimental results show that the proposed algorithm leads to subjectively better performance than typical retinex algorithm, and that the proposed algorithm has the capability to reduce approximately 40% computational cost than typical retinex algorithm.

• Smart Media Journal
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• v.2 no.4
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• pp.60-65
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• 2013

In this paper, we propose a high-resolution disparity map generation method using a low-resolution time-of-flight (TOF) depth camera and color camera. The TOF depth camera is efficient since it measures the range information of objects using the infra-red (IR) signal in real-time. It also quantizes the range information and provides the depth image. However, there are some problems of the TOF depth camera, such as noise and lens distortion. Moreover, the output resolution of the TOF depth camera is too small for 3D applications. Therefore, it is essential to not only reduce the noise and distortion but also enlarge the output resolution of the TOF depth image. Our proposed method generates a depth map for a color image using the TOF camera and the color camera simultaneously. We warp the depth value at each pixel to the color image position. The color image is segmented using the mean-shift segmentation method. We define a cost function that consists of color values and segmented color values. We apply a weighted average filter whose weighting factor is defined by the random walk probability using the defined cost function of the block. Experimental results show that the proposed method generates the depth map efficiently and we can reconstruct good virtual view images.

• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.29-41
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• 2013

The digital camera is impossible to preserve the appearance of a scene containing high dynamic range due to a limitations of the sensing hardware. The Retinex was proposed on the purpose to solve these problems. While the Retinex enhances visibility and color constancy, it sometimes suffers from color distortion, halo effect and reduced global contrast. This paper presents an advanced Retinex algorithm working on the YCbCr color coordinate to reduce the processing time and to improve the global contrast and color. Simulation results show that our algorithm significantly reduces the total processing time, and provide the superior result image by improving the global contrast and color consistency as well as by reducing the halo effect around the boundaries.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.1291-1294
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• 2006

The technique presented in this paper maximizes LCD color performance by way of advanced gamma control technology. First, two gamma curves corresponding to two sub-pixels are mixed to minimize gamma distortion off-axis, then RGB gamma curve control is used to establish accurate on-axis color. Independent RGB curve control for each sub-pixel improves the LCD's performance both on- and off-axis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5039-5055
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• 2018

The quality of natural outdoor images captured by visible camera sensors is usually degraded by the haze present in the atmosphere. In this paper, a fast image dehazing method based on visible image and near-infrared fusion is proposed. In the proposed method, a visible and a near-infrared (NIR) image of the same scene is fused based on the dual-tree complex wavelet transform (DT-CWT) to generate a dehazed color image. The color of the fusion image is regulated through haze concentration estimated by dark channel prior (DCP). The experiment results demonstrate that the proposed method outperforms the conventional dehazing methods and effectively solves the color distortion problem in the dehazing process.