• Journal of KIEE
• /
• v.11 no.1
• /
• pp.69-77
• /
• 2001

This paper describes an image processing algorithm capable of recognizing road lanes by using a CDF(cumulative distribution function). The CDF is designed for the model function of road lanes. Based on the assumptions that there are no abrupt changes in the direction and location of road lanes and that the intensity of lane boundaries differs from that of the background, we formulated the CDF, which accumulates the edge magnitude for edge directions. The CDF has distinctive peak points at the vicinity of lane directions due to the directional and the positional continuities of a lane. To obtain lane-related information a scatter diagram was constructed by collecting edge pixels, of which the direction corresponds to the peak point of the CDF, then the principal axis-based line fitting was performed for the scatter diagram. Noises can cause many similar features to appear and to disappear in an image. Therefore, to reduce the noise effect a recursive estimator of the CDF was introduced, and also to prevent false alarms or miss detection a scene understanding index (DUI) was formulated by the statistical parameters of the CDF. The proposed algorithm has been implemented in real time on video data obtained from a test vehicle driven on a typical highway.

• Journal of Radiation Industry
• /
• v.17 no.4
• /
• pp.377-384
• /
• 2023

In this study, the image quality assessment, especially spatial resolution evaluation, for Sparse-view CT reconstructed images was performed. The main goal of the experiment is to evaluate Modulation Transfer Function by using American Standard Method for Measurement of Computed Tomography System Performance(ASTM E1695-95) which uses the edge test object. To compare with the ASTM method, a different method, the radial-type edge profile, to measure MTF using the edge method also performed. Both approaches were tested on the same image acquired by the stationary-gantry sparse-view CT security-screening system using cylindrical test phantom manufactured in accordance with ANSI 42.45. Both of the spatial resolutions at 10% modulation are 0.195, 0.203lp pixel^-1, respectively. The method implemented by ASTM E1695-95 showed higher reliability and had a relatively more accurate spatial resolution result than the radial-type edge profile method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.5
• /
• pp.423-428
• /
• 2005

This paper proposes a new edge detection method using a $3{\times}3$ ideal binary pattern and lookup table (LUT) for the mobile robot localization without any parameter adjustments. We take the mean of the pixels within the $3{\times}3$ block as a threshold by which the pixels are divided into two groups. The edge magnitude and orientation are calculated by taking the difference of average intensities of the two groups and by searching directional code in the LUT, respectively. And also the input image is not only partitioned into multiple groups according to their intensity similarities by the histogram, but also the threshold of each group is determined by fuzzy reasoning automatically. Finally, the edges are determined through non-maximum suppression using edge confidence measure and edge linking. Applying this edge detection method to the mobile robot localization using projective invariance of the cross ratio. we demonstrate the robustness of the proposed method to the illumination changes in a corridor environment.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.4
• /
• pp.89-96
• /
• 2009

In this paper, we propose an efficient deinterlacing algorithm which is an edge dependent interpolation based on edge direction refinement. The conventional edge dependent interpolation algorithms have a visually better performance than any other Intra-field deinterlacing algorithms. However they are very sensitive to noise due to the failure of estimating edge direction. In order to exactly detect edge direction, our method detects edge direction of around interpolated pixel and refines the edge direction using weighted median filter. Simulation results have shown the efficacy of the proposed method with significant improvement over the previous methods in terms of the objective PSNR quality as well as the subjective image quality.

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.4 s.36
• /
• pp.133-141
• /
• 2005

An edge is where the intensity of an image moves from a low value to high value or vice versa. The edges tell where objects are. their shape and size. and something about their texture. Many traditional edge operators are derivative based and perform reasonably well for simple noise-free images. In recent, statistical edge detectors for complex images with noises have been described. This paper compares and analysis the performance of statistical edge detectors based on the T test and Wilcoxon test, and mathematical edge detectors based on Sobel operator, and the well-known Canny detector and Wavelet transformation detector, and provides the implementation of these edge detectors using Java on the web.

• The KIPS Transactions:PartB
• /
• v.14B no.3 s.113
• /
• pp.171-182
• /
• 2007

We propose a simple method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Then we present a method to eliminate the tracked contour object by replacing with the background scene we get from other frame. First we track the object using LOD (Level-of-Detail) canny edge maps, then we generate background of each image frame and replace the tracked object in a scene by a background image from other frame that is not occluded by the tracked object. Our tracking method is based on level-of-detail (LOD) modified Canny edge maps and graph-based routing operations on the LOD maps. We get more edge pixels along LOD hierarchy. Our accurate tracking is based on reducing effects from irrelevant edges by selecting the stronger edge pixels, thereby relying on the current frame edge pixel as much as possible. The first frame background scene is determined by camera motion, camera movement between two image frames, and other background scenes are computed from the previous background scenes. The computed background scenes are used to eliminate the tracked object from the scene. In order to remove the tracked object, we generate approximated background for the first frame. Background images for subsequent frames are based on the first frame background or previous frame images. This approach is based on computing camera motion. Our experimental results show that our method works nice for moderate camera movement with small object shape changes.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.4
• /
• pp.51-58
• /
• 2012

Recently, 3D technology based on depth image is widely used in various fields including 3D space recognition, image acquisition, interaction, and games. Depth camera is used in order to produce depth image, various types of effort are made to improve quality of the depth image. In this paper, we suggests using area-based Canny edge detector to improve depth image in applying 3D technology based on depth camera. The suggested method provides improved depth image with pre-processing and post-processing by fixing image quality deterioration, which may take place in acquiring depth image in a limited environment. For objective image quality evaluation, we have confirmed that the image is improved by 0.42dB at maximum, by applying and comparing improved depth image to virtual view reference software. In addition, with DSCQS(Double Stimulus Continuous Quality Scale) evaluation method, we are reassured of the effectiveness of improved depth image through objective evaluation of subjective quality.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.6
• /
• pp.112-122
• /
• 2009

In this paper, we present a novel space-frequency adaptive image restoration approach using vaguelette-wavelet decomposition (VWD). The proposed algorithm classifies a degraded image into flat and edge regions by using spatial information of the wavelet coefficient. For reducing the noise we perform an adaptive wavelet shrinkage process. At edge region candidates, we adopt entropy approach for estimating the noise and remove it by using relative between sub-bands. After shrinking wavelet coefficients process, we restore the degraded image using the VWD. The proposed algorithm can reduce the noise without affecting the sharpness details. Based on the experimental results, the proposed algorithm efficiently proved to be able to restore the degraded image while preserving details.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.3
• /
• pp.96-104
• /
• 2009

In this paper, we propose a no-reference objective Quality assessment metrics of image. The blockiness and blurring of edge areas which are sensitive to the human visual system are modeled as step functions. Blocking and blur metrics are obtained by estimating local visibility of blockiness and edge width, For the blocking metric, horizontal and vertical blocking lines are first determined by accumulating weighted differences of adjacent pixels and then the local visibility of blockiness at the intersection of blocking lines is obtained from the total difference of amplitudes of the 2-D step function which is modelled as a blocking region. The blurred input image is first re-blurred by a Gaussian blur kernel and an edge mask image is generated. In edge blocks, the local edge width is calculated from four directional projections (horizontal, vertical and two diagonal directions) using local extrema positions. In addition, the kurtosis and SSIM are used to compute the blur metric. The final no-reference objective metric is computed after those values are combined using an appropriate function. Experimental results show that the proposed objective metrics are highly correlated to the subjective data.

• International Journal of Precision Engineering and Manufacturing
• /
• v.7 no.1
• /
• pp.47-50
• /
• 2006

An active contour model has been used to detect the edges in a still image. In order to apply the active contour model to edge detection, the energy function which consists of internal, external and image energies should be defined. After defining the energy function, the edge of an object is detected through minimization of the value of the energy function. In this paper, the modified internal energy function is proposed to improve the convergence of the energy function when the active contour model is applied to the tracking of deformable objects using the greedy algorithm. In order to show the performance of the proposed energy function, experiments were carried out for the still and animated images.