• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.10
• /
• pp.3406-3418
• /
• 2022

Reversible data hiding is an algorithm that has ability to extract the secret data and to restore the marked image to its original version after data extracting. However, some previous schemes offered the low image quality of marked images. To solve this shortcoming, a new reversible data hiding scheme based on pixel value ordering and edge detection mechanism is proposed. In our proposed scheme, the edge image is constructed to divide all pixels into the smooth regions and rough regions. Then, the pixels in the smooth regions are separated into non overlapping blocks. Then, by taking advantages of the high correlation of current pixels and their adjacent pixels in the smooth regions, PVO algorithm is applied for embedding secret data to maintain the minimum distortion. The experimental results showed that our proposed scheme obtained the larger embedding capacity. Moreover, the greater image quality of marked images are achieved by the proposed scheme than that other previous schemes while the high EC is embedded.

• Proceedings of the KIEE Conference
• /
• 1987.07b
• /
• pp.1492-1494
• /
• 1987

This paper describes a hardware architecture called Edge Painting Machine for real time generation of scan line images for raster scan graphics display. The Edge Painting Machine consists of Scanline Processor which converts polygon data sorted in their depth priority into a set of scan line commands for each scan line, and Edge Painting Tree which converts the scanline commands set into a raster line image. Edge painting tree has been designed using combinational logic circuit. The designed circuit has been simulated to verify the proper functioning. A salient feature of the EPT is that hardware composition is simple, because each processor is constituted by only combinational logic circuit.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.13 no.1
• /
• pp.74-84
• /
• 1988

This paper presents feature extraction technique for linear image using edge detection algorithms. The process of edge finding consists of determining edge magnitud and direction by convolution of an image with a number of edge masks, of thinning and ghresholding these edge magnitudes, of linking the edge elemtnts based on proximity ans orientation, and finally, of approximating the linked elements by piede-wise linear segmentss. These techniques are intened to be general and opplications to terminal detection and road recognition tasks are described. The presentation will be helpful to other researchers attempting to implement similar algorithms.

• Proceedings of the IEEK Conference
• /
• 2000.06d
• /
• pp.70-73
• /
• 2000

The error diffusion is good for reproducing continuous image to binary image. However the reproduction of edge characteristics is weak in power spectrum analysis of display error. It is suggested for us an edge-enhanced error-diffusion method that is included pre-processing algorithm for edge characteristic enhancement. Pre-processing algorithm is organized horizontal and vertical directional 2nd order differential values and weighting function of pre-filter. The improved Error diffusion using pre-filter, presents a good results visually which edge characteristics is enhanced. The performance of the proposed algorithm is compared with that of the conventional edge-enhanced error diffusion by measuring the RAPSD of display error, the egde correlation and the local average accordance.

• Journal of Power System Engineering
• /
• v.2 no.3
• /
• pp.60-67
• /
• 1998

Edge detection is the first step and very important step in image analysis. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that evaluates the quality of edge configurations. The cost function can be used as a basis for comparing the performances of different detectors. This cost function is made of desirable characteristics of edges such as thickness, continuity, length, region dissimilarity. And we use a simulated annealing algorithm for minimum of cost function. Simulated annealing are a class of adaptive search techniques that have been intensively studied in recent years. We present five strategies for generating candidate states. Experimental results(building image and test image) which verify the usefulness of our simulated annealing approach to edge detection are better than other operator.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.4
• /
• pp.51-56
• /
• 2018

This paper proposes the efficient preprocessing method based on curvelet transform for edge enhancement in image. The propose method is generated the edge map by using the Canny algorithm to wavelet transform, which is the sub-step of the curvelet transform. In order to improve the part of edge feature, the selective sharpening according to the generate edge map is applied. In experimental result, the propose method achieves that the enhancement of edge feature is better than conventional methods. This leads that peak to signal noise ratio, edge intensity are improvement on average about 1.92, 1.12dB respectively.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.6
• /
• pp.1-12
• /
• 2008

This paper presents automatic moving object detection method using the rotating camera covering larger area with a single camera. The proposed method is based on the edge segment matching which robust to the dynamic environment with illumination change and background movement. The proposed algorithm presents an edge segment based background panorama image generation method minimizing the distortion due to image stitching, the background image generation method using Generalized Hough Transformation which can reliably register the current image to the panorama image overcoming the stitching distortions, the moving edge segment extraction method that overcome viewpoint difference and distortion. The experimental results show that the proposed method can detect correctly moving object under illumination change and camera vibration.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.6
• /
• pp.704-709
• /
• 2003

The digital image can be distorted by a noise for a transmission or other elements of system. It happen to be vague of a boundary side in the division of an image object, especially, boundary side of an input image is very important because it can be determined to the division and detection element in pattern recognition. Therefore it is proposed an edge detection method of optimal to divide and detect exactly a boundary part. In this paper, it detected the optimal edge with applying this image to Meyer wavelet-CNN algorithm, after it does level up a boundary side of an image by using the adaptive morphology as the threshold of an input image. It confirmed that the proposed algorithm is more superior to the conventional methods and the conventional Sobel method which is an image edge detection algorithm. Especially, it is confirmed by simulation that the proposed algorithm can be got the better result edge at the place of closing to each edges and having smoothly curved line.

• Annual Conference of KIPS
• /
• 2007.05a
• /
• pp.333-336
• /
• 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. Our method consists of two parts: 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. To reduce side-effects because of irrelevant edges, we start our basic tracking by using strong Canny edges generated from large image intensity gradients of an input image. We get more edge pixels along LOD hierarchy. LOD Canny edge pixels become nodes in routing, and LOD values of adjacent edge pixels determine routing costs between the nodes. We find the best route to follow Canny edge pixels favoring stronger Canny edge pixels. 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. 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.

• The Korean Journal of Applied Statistics
• /
• v.33 no.6
• /
• pp.813-821
• /
• 2020

Recently, research using unstructured data such as images and videos has been actively conducted in various fields. Edge detection is one of the most useful image enhancement techniques to improve the quality of the image process. However, it is very difficult to perform edge detection in noise images because the edges and noise having high frequency components. This paper uses a linear model and standard deviation as an effective edge detection method for noise images. The edge is detected by the difference between the standard deviation of the pixels included in the pixel block and the standard deviation of the residual obtained by fitting the linear model. The results of edge detection are compared with the results of the Sobel edge detector. In the original image, the Sobel edge detection result and the proposed edge detection result are similar. Proposed method was confirmed that the edge with reduced noise was detected in the various levels of noise images.