• Journal of KIISE:Software and Applications
• /
• v.29 no.11
• /
• pp.809-817
• /
• 2002

This paper describes a new face detection method that is a pre-processing algorithm for on-line face recognition. To complement the weakness of using only edge or rotor features from previous face detection method, we propose the two types of face detection method. The one is a combined method with edge and color features and the other is a center area color sampling method. To prevent connecting the people's face area and the background area, which have same colors, we propose a new adaptive edge detection algorithm firstly. The adaptive edge detection algorithm is robust to illumination variance so that it extracts lots of edges and breakouts edges steadily in border between background and face areas. Because of strong edge detection, face area appears one or multi regions. We can merge these isolated regions using color information and get the final face area as a MBR (Minimum Bounding Rectangle) form. If the size of final face area is under or upper threshold, color sampling method in center area from input image is used to detect new face area. To evaluate the proposed method, we have experimented with 2,100 face images. A high face detection rate of 96.3% has been obtained.

• Journal of the Korean Society of Radiology
• /
• v.12 no.2
• /
• pp.123-131
• /
• 2018

This study compared PENELOPE with measured values from low energy peak to high energy peak to reduce peak to compton ratio and continuum background spectrum using $^{60}Co$, $^{137}Cs$ and mixed volume source. In addition, the change in backscattering and compton edge efficiency was compared with that of PENELOPE through changes in the vicinity of low energy. The results from the mixed volume source are applied to the soil samples to determine how much the minimum detection limits of the soil samples are reduced in the suppression and unsuppressed mode. The compton suppression of the low energy region of $^{60}CO$ (1,173 keV) was considerable, and the Compton edge RF for the $^{137}Cs$ (661 keV) peak was 2.8. In particular, the $^{60}Co$ source emits coincidence gamma rays of 1,173.2 keV and 1,332.5 keV, so compton inhibition was reduced by approximately 21%. RF of compton edges of 1,173 keV and 1,332 keV emitted from a $^{60}Co$ source was 3.2 and 3.4, and the peak to compton edge ratio was improved to 8: 1. And Compared with Penelope, the uncertainty was well within 2%. In compton unsuppressed mode, MDA values of 661 keV, 1,173 keV and 1,332 keV were 0.535, 0.173 and 0.136 Bq/kg, respectively, but decreased in compton suppressed mode to 0.121, 0.00826 and 0.00728 Bq/kg. Thus, Compton suppressed could reduce the background radioactivity and the radioactivity contained in the detector itself.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.5 no.2
• /
• pp.57-62
• /
• 1996

Basically, the idea underlying most edge-detection technique is the computation of a local derivative operator used for edge detection in gray level image. This concept can be easily illustrated with the aid of object which shows an image of a simple lilght on a dark background, Using the gray level profile along a horizontal scan line of the image. the first and second derivatives of it were acquired. This study is to develop an automatic measuring system based on the digital image processing which can be applied to the real time measurement of the characteristics of the ultra-thin thickness. The experimental results indicate that the developed automatic inspection can be applied in real situation.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.10
• /
• pp.120-129
• /
• 1996

This paper presents an efficient block segmentation and classification using the edge information of the document image. We extract four prominent features form the edge gradient and orientaton, all of which, and thereby the block clssifications, are insensitive to the background noise and the brightness variation of of the image. Using these four features, we can efficiently classify a document image into the seven categrories of blocks of small-size letters, large-size letters, tables, equations, flow-charts, graphs, and photographs, the first five of which are text blocks which are character-recognizable, and the last two are non-character blocks. By introducing the clumn interval and text line intervals of the document in the determination of th erun length of CRLA (constrained run length algorithm), we can obtain an efficient block segmentation with reduced memory size. The simulation results show that the proposed algorithm can rigidly segment and classify the blocks of the documents into the above mentioned seven categories and classification performance is high enough for all the categories except for the graphs with too much variations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.107-108
• /
• 2016

Our study proposes the methods of distinguishing vehicle types using the interval and size of the car. The car videos converts the basic RGB model to Gray model for use and through Canny Edge Direction, it eliminates the background of the car while obtaining feature points through the detection of contours.

• Journal of the Korean Society for Precision Engineering
• /
• v.11 no.5
• /
• pp.124-133
• /
• 1994

Many of non-contact measuring systems are used to estimate surface characteristics owing to their advantages of high speed and undanaged test. In this paper, a new measuring system is proposed to acquire image from CCD camera through back light illumination. Lowpass filter is very useful in view of noise removal and optimum binary image can be made through histogram equalization which is one of the histogram technique to maximize brightness intensity between workpiece and background. Laplacian operator is used to detect workpiece edge from binary image. In case of image treatment applying Laplacian operator, surface roughness is calculated by introducing conversion coefficient for coordinate of pixel which edge is composed of. In summary, the work is concerned with the development of a new technique for roughness measurement by the image processing in turning.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.1
• /
• pp.210-216
• /
• 2014

The edge in the image is a part which the brightness changes rapidly between the object and the object or objects and background, and includes information of the features such as size, position, orientation, and texture of the object. The edge detection is the technique that acquires these information of the images, and now the researches to detect edges are making steady progress. Typical conventional edge detection methods are Sobel, Prewitt, Roberts using the first derivative operator and Laplacian method using the second derivative operator and so on. These methods is more or less insufficient that the characteristics of the edge detection in the image added salt and pepper noise. therefore, in this paper, an edge detection algorithm using modified mask that applies different size mask according to noise density of local mask is proposed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.9
• /
• pp.2199-2205
• /
• 2013

In modern society the image processing has been applied to various digital devices such as smartphone, digital camera, and digital TV. In the field of image processing the edge detection is one of the important parts in the image processing procedure. The image edge means point that the pixel value is changed between background and object rapidly, and includes the important information such as magnitude, location, and orientation. The performance of the existing edge detection method is insufficient for the image degraded by AWGN(additive white Gaussian noise) because it detects edges by using small weighted masks. Therefore, in this paper, to detect edge in AWGN environment effectively, we proposed an algorithm that detects edge as calculated gradient of sorting vector which is transformed by estimated mask from new pixel according to each region.

• Journal of Korea Entertainment Industry Association
• /
• v.5 no.1
• /
• pp.127-133
• /
• 2011

Characters in natural image are an important information with various context. Previous work of character region detection algorithms is not detect of character region in case of image complexity and the surrounding lighting, similar background to character, so this paper propose an method of character region detection in natural image using edge and connected component by morphological reconstructions. Firstly, we detect edge using Canny-edge detector and connected component with local min/max value by morphological reconstructed-operation in gray-scale image, and labeling each of detected connected component elements. lastly, detected candidate of text regions was merged for generation for one candidate text region, Final text region detected by checking the similarity and adjacency of neighbor of text candidate individual character. As the results of experiments, proposed algorithm improved the correctness of character regions detection using edge and connected components.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.11 no.2
• /
• pp.41-47
• /
• 2007

A fine segmentation algorithm is proposed for extracting objects in an image, which have both weak boundaries and highly non-convex shapes. The image has simple background colors or simple object colors. Two concepts, geometric attraction-driven flow (GADF) and edge-regions are combined to detect boundaries of objects in a sub-pixel resolution. The main strategy to segment the boundaries is to construct initial curves close to objects by using edge-regions and then to make a curve evolution in GADF. Since the initial curves are close to objects regardless of shapes, highly non-convex shapes are easily detected and dependence on initial curves in boundary-based segmentation algorithms is naturally removed. Weak boundaries are also detected because the orientation of GADF is obtained regardless of the strength of boundaries. For a fine segmentation, we additionally propose a local region competition algorithm to detect perceptible boundaries which are used for the extraction of objects without visual loss of detailed shapes. We have successfully accomplished the fine segmentation of objects from images taken in the studio and aphids from images of soybean leaves.