Jung Gap Kuk

MAP-MRF approach for binarization of degraded document image

We propose an algorithm for the binarization of document images degraded by uneven light distribution, based on the Markov Random Field modeling with Maximum A Posteriori probability (MAP-MRF) estimation. While the conventional algorithms use the decision based on the thresholding, the proposed algorithm makes a soft decision based on the probabilistic model. To work with the MAP-MRF framework we formulate an energy function by a likelihood model and a generalized Potts prior model. Then we construct a graph for the energy, and obtain the optimized result by using the well-known graph cut algorithm. Experimental results show that our approach is more robust to various types of images than the previous hard decision approaches.

Feature Based Binarization of Document Images Degraded by Uneven Light Condition

This paper proposes a document image binarization method, which is especially robust to the images degraded by uneven light condition, such as the camera captured document images. A descriptor that captures the regional properties around a given pixel is first defined for this purpose. For each pixel, the descriptor is defined as a vector composed of filter responses with varying length. This descriptor is shown to give highly discriminating pattern with respect to the background region, text region, and near text region. Of course there are misclassified pixels, which are then relabeled using an energy optimization method, specifically by using the graph cut method. For this, we devise an appropriate energy function that leads to clear and correct binarization. The proposed descriptor is also used for the skew detection, and thus correcting the skewed documents.

A multi-exposure image fusion algorithm without ghost effect

This paper proposes a new multi-exposure fusion algorithm for HDR imaging. The advantage of the fusion based method is that it does not actually generate HDR images so that the compression to LDR is also not needed. However, the conventional exposure fusion suffers from ghost effects when there are moving object or hand trembling because the output is just a weighted sum of multiple exposure input images. In order to alleviate this problem, we propose two kinds of weights in addition to the conventional weights that reflect the contrast, saturation and well-exposedness. The first is the weight that removes the pixels that can cause ghost effects, based on the observation that pixels near the boundary of moving objects often violate the rule that more highly exposed pixel have higher intensity level. The second is to reduce the weight when the correlation between an exposed image and the reference image is low. Experimental results show that the proposed method removes the ghost effectively, and yield less artifacts than the conventional methods.

A color to grayscale conversion considering local and global contrast

For the conversion of a color image to a perceptually plausible grayscale one, the global and local contrast are simultaneously considered in this paper. The contrast is measured in terms of gradient field, and the energy function is designed to have less value when the gradient field of the grayscale image is closer to that of original color image (called target gradient field). For encoding both of local and global contrast into the energy function, the target gradient field is constructed from two kinds of edges : one that connects each pixel to neighboring pixels and the other that connects each pixel to predetermined landmark pixels. Although we can have exact solution to the energy minimization in the least squares sense, we also present a fast implementation for the conversion of large image, by approximating the energy function. The problem is then reduced to reconstructing a grayscale image from the modified gradient field over the standard 4-neighborhood system, and this can be easily solved by the fast 2D Poisson solver. In the experiments, the proposed method is tested on various images and shown to give perceptually more plausible results than the existing methods.

Fast lane detection & tracking based on Hough transform with reduced memory requirement

In this paper, we present a computationally efficient and robust lane detection algorithm based on Hough transform. The proposed method first extracts lane markings by applying 1D ridge detector to each row of an image. Given the extracted ridge points, the lane is then detected by applying Hough transform. Unlike the conventional methods, we consider only small set of line candidates which pass through a circle centered at previously detected vanishing point. The set is quite small and it is represented by a small region on a parametric domain. Hence, the proposed method needs much smaller accumulation array than conventional one and reduces the required memory. In addition, we propose modified parametric domain which encodes approximated lateral positions and the current lateral positions are searched on the modified parametric domain within certain region centered at previously detected positions. This proposed tracking scheme on the parametric domain considers temporal coherency and enables robust detection even when actual lane is weakly represented on the parametric domain. Experimental results show that the proposed method robustly detects the lane with reduced memory requirement.

Autofocus method using dual aperture and color filters

We propose a new autofocus method for digital cameras based on the separation of color components from the incoming light rays and the measure of their disparity. For separating color components, we place two apertures with the red and blue color filters on them. This enables us to get two monochromatic images that have disparities proportional to the distances of the objects from the camera. We also propose a new measure to find the disparity of these color components because the conventional disparity measures show low accuracy for the pair of different color channel images. The measure is based on the observation that the overlap of images with disparity has many weak gradients, whereas the overlap with no disparity has a small number of strong gradients. One of two images is shifted from left to right, and the measure is computed for each position. Then, the position with the maximum measure is considered as the disparity, and the direction and distance of focus are computed from the estimated disparity and camera parameters. The proposed method is implemented in a commercial compact camera, and it has been demonstrated that the method finds the focus in a wide range of distance and illumination conditions.

Eliminating structure misalignments using robust matching and image editing based on seam carving

In this paper, we propose an algorithm that generates a natural composite from the misaligned images. The image stitching for this purpose is usually performed in two steps: correspondence matching of salient features followed by appropriated warping or editing. The proposed correspondence matching problem is formulated as the one-dimensional registration along the stitching boundary, where an appropriate energy function is proposed. The designed energy function consists of three complementary terms that encode appearance, smoothness and ordering of points, whereas the existing method considers the edge strength and ordering. Then we develop an algorithm that makes several salient points move to desired positions by using seam carving/inserting, which produces visually pleasing results compared to the conventional warping methods with less computations. The experimental results show that the proposed method efficiently and robustly generates natural composite images.

A gradient guided deinterlacing algorithm

This paper proposes new intra and inter-deinterlacing algorithm based on the gradient domain image/video processing approach. From the interlaced (field) images, gradient field images are generated and then the gradients of missing lines are estimated to generate gradient images which correspond to progressive frames. The proposed intra-deinterlacing is basically an edge-oriented interpolation, which interpolates the gradients of missing pixels along the optimal spatial orientation. Finding the optimal orientation among all possible ones is formulated as a labeling problem with Markov random field (MRF) framework. For obtaining better results for fast moving video sequences, this method is extended to inter-deinterlacing, which considers the temporal orientations as well as the spatial ones. With the synthesized gradient frame images and the original pixel values of the field images, we then formulate a linear equation that generates the final progressive frame images. Like other gradient domain image processing applications, the integrity of edges is the main advantage of the proposed method.

A New Overlap Save Algorithm for Fast Block Convolution and Its Implementation Using FFT

Convolution of data with a long-tap filter is often implemented by overlap save algorithm (OSA) using fast Fourier transform (FFT). But there are some redundant computations in the traditional OSA because the FFT is applied to the overlapped data (concatenation of previous block and the current block) while the DFT computations are recursive. In this paper, we first analyze the redundancy by decomposing the OSA into two processes related to the previous and current block. Then we eliminate the redundant computations by introducing a new transform which is applied only to the current data, not to the overall overlapped data. Hence the size of transform is reduced by half compared to the traditional OSA. The new transform is in the form of DFT and it can be implemented by defining a new butterfly structure. However we implement it by a cascade of twiddle factor and conventional FFT in this paper, in order to use the FFT libraries in PC and DSP. The computational complexity in this case is analyzed and compared with the existing methods. In the experiment, the proposed method is applied to several block convolutions and partitioned-block convolutions. The CPU time is reduced more than expected from the arithmetic analysis, which implies that the reduced transform size gives additional advantage in data manipulation.

Estimating the coordinates of pillars and posts in the parking lots for intelligent parking assist system

This paper proposes an algorithm for the detection of pillars or posts in the video captured by a single camera implemented on the fore side of a room mirror in a car. The main purpose of this algorithm is to complement the weakness of current ultrasonic parking assist system, which does not well find the exact position of pillars or does not recognize narrow posts. The proposed algorithm is consisted of three steps: straight line detection, line tracking, and the estimation of 3D position of pillars. In the first step, the strong lines are found by the Hough transform. Second step is the combination of detection and tracking, and the third is the calculation of 3D position of the line by the analysis of trajectory of relative positions and the parameters of camera. Experiments on synthetic and real images show that the proposed method successfully locates and tracks the position of pillars, which helps the ultrasonic system to correctly locate the edges of pillars. It is believed that the proposed algorithm can also be employed as a basic element for vision based autonomous driving system.