Hyun-hwa Oh

An improved binarization algorithm based on a water flow model for document image with inhomogeneous backgrounds

A segmentation algorithm using a water flow model [Kim et al., Pattern Recognition 35 (2002) 265-277] has already been presented where a document image can be efficiently divided into two regions, characters and background, due to the property of locally adaptive thresholding. However, this method has not decided when to stop the iterative process and required long processing time. Plus, characters on poor contrast backgrounds often fail to be separated successfully. Accordingly, to overcome the above drawbacks to the existing method, the current paper presents an improved approach that includes extraction of regions of interest (ROIs), an automatic stopping criterion, and hierarchical thresholding. Experimental results show that the proposed method can achieve a satisfactory binarization quality, especially for document images with a poor contrast background, and is significantly faster than the existing method.

Exact corner location using attentional generalized symmetry transform

Local symmetry is often visible at the corner points. In this paper, generalized symmetry transform (GST) [Reisfeld et al., Internat. J. Comput. Vision 14 (1995) 119] is combined with the proposed parametric corner equation in order to detect corners and improve their localization. Experimental results show that the proposed corner detection algorithm is efficient and robust to noise when tested on the real and artificial images and compared to the other detectors.

Motion artifact-free HDR imaging under dynamic environments

High dynamic range (HDR) imaging is one of the most important emerging fields of the next generation digital cameras. It is hard to handle a problem so-called ghosting artifact caused by camera shake and/or object motion in the method of fusing a set of differently exposed images. Some object motions around under or over saturation region still produce severe artifacts due to the reference images dynamic range limitation. For the commercial product, it is the important problem to be solved completely. We analyze this problem and propose a new HDR deghosting scheme capable of dealing with various motions. In order to avoid the ghosting artifacts, we capture only two uncompressed Bayer raw images with different exposures, select the wider dynamic range image as a reference, and process them in the Bayer domain. The experimental results show that our proposed method provides motion artifact-free under dynamic environments with various moving objects.

Moving object-High Dynamic Range Imaging (HDRI) for artifact-free digital camera

We present a new artifact-free HDRI technology for a consumer digital camera that is based on de-ghosting with a dual-brightness mapping. The proposed approach reduces motion artifacts and the number of capturing images.

Active Motion High Dynamic Range Imaging for digital still camera

Active Motion High Dynamic Range Imaging (HDRI) is a new artifact-free HDRI technology for a consumer digital still camera. This technology allows us to optimize capturing time and to remove motion artifacts by a camera motion and a moving object. To optimize the capturing time, the key component of our approach is a dual-brightness mapping to detect and compensate the artifacts with just two images. This paper shows that the proposed HDRI approach effectively corrects the artifacts.

Mammogram enhancement using multi-energy x-ray

This paper proposes a new method to improve contrast of a mammogram using multi-energy x-ray (MEX) images. The x-ray attenuation differences among breast tissues increase as incident photons have lower energy. Thus an image obtained by a narrow low energy spectrum has higher contrast than a full (wide) energy spectrum image. The proposed mammogram enhancement utilizes this fact using MEX images. Lowpass data of a low energy spectrum image and high frequency components of a wide energy spectrum image are combined to have high contrast and low noise. Nonsubsampled contourlet transform (NSCT) is employed to decompose image data into multi-scale and multidirectional information. The NSCT overcomes the shortage of directions of wavelet transform by expressing smoothness along contours sufficiently. The outcome of the transform is a lowpass subband and multiple bandpass directional subbands. First, the lowpass subband coefficients of a wide energy spectrum image are substituted by those of a low energy spectrum image. Before the coefficient modification, the low energy spectrum image is processed to have high contrast and sharp details. Next, for the bandpass directional subbands, the locally adaptive bivariate shrinkage of contourlet coefficients is applied to suppress noise. The bivariate shrinkage function exploits interscale dependency of coefficients. Local contrast of the resultant mammogram is considerably enhanced and shows clear fibroglandular tissue structures. Experimental results illustrate that the proposed method produces a high contrast and low noise level image, as compared to the conventional mammography based on a single energy spectrum image.

High contrast soft tissue imaging based on multi-energy x-ray

Breast soft tissues have similar x-ray attenuations to mass tissue. Overlapping breast tissue structure often obscures mass and microcalcification, essential to the early detection of breast cancer. In this paper, we propose new method to generate the high contrast mammogram with distinctive features of a breast cancer by using multiple images with different x-ray energy spectra. On the experiments with mammography simulation and real breast tissues, the proposed method has provided noticeable images with obvious mass structure and microcalifications.

Compensation method for color defects in PDP due to different time responses of phosphors

On a plasma display panel (PDP), luminous elements of red, green, and blue have different time responses. Therefore, a colored trails and edges appear behind and in front of moving objects. In order to reduce the color artifacts, this paper proposes a motion-based discoloring method. Discoloring values are modeled as linear functions of a motion vector to reduce hardware complexity. Experimental results show that the proposed method has effectively removed the colored trails and edges of moving objects. Moreover, the clear image sequences have been observed compared to the conventional ones.

Robust character segmentation system for Korean printed postal images

This paper proposes a character segmentation system for Korean printed postal images. The proposed method is composed of two main processes, which are robust skew correction and character segmentation. Experimental results on real postal images show that the proposed system effectively segments characters to be suitable for the input of OCR system.

Improvement of binarization method using a water flow model for document images with complex backgrounds

Binarization algorithm using a water flow model has been presented [6], in which a document image is efficiently separated into two regions, characters and backgrounds, due to the property of locally adaptive thresholding. However, this method has not decided when to stop the iterative process and required long processing time. Moreover, characters on poor contrast backgrounds often fail to be separated successfully. In the current paper, an improved approach is proposed to overcome above shortcomings of the existing method, by introducing a hierarchical thresholding technique as well as extracting the regions of interest (ROIs) for speed-up and an automatic stopping criterion.