• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.287-294
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• 2013

Binary connected-component labeling is widely used in the fields of the image processing and the computer vision. Many kinds of labeling techniques have been developed, and two-scan is known as the fastest method among them. Traditionally pixel-based scan masks have been used for the first stage of the two-scan. Recently, block-based labeling techniques were introduced by C. Grana et. al. and L. He et. al. They are faster than pixel-based labeling methods. In this paper, we propose a new binary connected-component labeling technique with block-based labels and a pixel-based scan mask. The experimental results with various images show that the proposed method is faster than the He's which is known as the fastest method currently. The amount of performance enhancement is averagely from 3.9% to 22.4% according to the sort of the images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1740-1747
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• 2004

In this paper, we proposed an effective method for detecting fingerprint ridge direction based on the run-length and chain codes. First, a fingerprint image is normalized, and it is thresholded to obtain binary image with foreground and background regions. The foreground regions is composed of fingerprint ridges, and the ridges is encoded with the run-length and chain codes. To detect directional information, the boundary of ridge codes is traced, and curvature is calculated at ecah point of boundary. And the detected direction value is smoothed with appropriate window locally. The proposed method is applied to NIST and FVC2002 fingerprint database to evaluate performance. By the experimental results, the proposed method can be used to obtain ridge direction value in fingerprint image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2302-2316
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• 2015

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1194-1198
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• 2005

A computer vision system was designed and validated to recognize an individual Holstein cattle by processing images of their body patterns. This system involves image capture, image pre-processing, algorithm processing, and an artificial neural network recognition algorithm. Optimum management of individuals is one of the most important factors in keeping cattle healthy and productive. In this study, an image-processing system was used to recognize individual Holstein cattle by identifying the body-pattern images captured by a charge-coupled device (CCD). A recognition system was developed and applied to acquire images of 49 cattles. The pixel values of the body images were transformed into input data comprising binary signals for the neural network. Images of the 49 cattle were analyzed to learn input layer elements, and ten cattles were used to verify the output layer elements in the neural network by using an individual recognition program. The system proved to be reliable for the individual recognition of cattles in natural light.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.427-438
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• 2011

In this paper, we propose a content retrieval system using pictures taken from a display for more intelligent mobile services. We focus on the search robustness by minimizing the influence of photographing conditions such as changes in the illumination intensity. For an efficient search and precise detection, as well as robustness, we use a two-step comparison method based on indexing features and a binary map based on luminance and chrominance difference with the adjacent blocks. We also evaluate the proposed algorithm by comparing with the existing algorithms, and we show the content retrieval system that we've implemented using the proposed algorithm.

• Journal of Sensor Science and Technology
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• v.11 no.1
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• pp.28-38
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• 2002

People are important targets for video surveillance and monitoring(VSAM) but difficult to be analyzed. In this paper, a technique to count people in image sequences is dealt as a prerequisite procedure for automatic tracking and behaviour analysis. A group of people is divided at local minima of the line connecting the highest pixels on the binary image of the people extracted from the image taken by a stationary video camera. As the properties of the divided regions vary according to the relative positions of the people in a group, different states are assigned for the completely occluded, partially occluded, completed separated individual, and wrongly divided regions. By analyzing the transition of the states of divided regions, the number of people on the site monitored is estimated. The technique is checked in real experimental situations.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.17-22
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• 2017

In this paper, a password recognition system that can overcome a shoulder-surfing attack is developed. During the time period of password insertion, the developed system can prevent the attack and enhance the safety of the password. In order to raise the detection rate of the password image, the mopology technique is utilized. By adapting 4 times of the expansion and dilation, the niose from the binary image of the password is removed. Finally, the mobile phone application is also developed to recognize the one time password and the detection rate is measured. It is shown that the detection rate of 90% is achieved under the dark light condition.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07a
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• pp.575-579
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• 2005

A beautiful, flexible active-matrix electrophoretic display (AM-EPD) device is reported. The flexible AM-EPD device has a $40.0{\times}30.0\;mm^2$ display area, measures about 0.27 mm in thickness, weighs about 0.45 g and possesses only 20 external connections. The flexible AM-EPD device displays clear black-and-white images with 5 gray-scales on $160{\times}120$ pixels. The display is free from residual image problems, because we use an area-gray-scale method on $320{\times}240$ EPD elements, each of which is driven with binary signals. Each pixel consists of 4 EPD elements. In addition, since the response time of the electrophoretic material is as long as approximately 400 ms and since the display possesses a large number of EPD elements, we have developed a special driving method suitable for changing EPD images comfortably. A complete image is formed on the AM-EPD device, consisting of a reset frame and several, typically 6, image frames.

• Journal of the Semiconductor & Display Technology
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• v.11 no.1
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• pp.79-83
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• 2012

We use adaptive method and determine threshold coefficient so that the algorithm could decide a suitable binarization threshold coefficient of the image to detecting a marker; therefore, we solve the light influence on the shadow area and dark region. In order to improve the speed for reducing computation we created Integral Image. The algorithm detects an outline of the image by using canny edge detection for getting damage or obscured markers as it receives the noise removed picture. The strength of the line of the outline is extracted by Hough transform and it extracts the candidate regions corresponding to the coordinates of the corners. Markers extracted using the equation of a straight edge to find the coordinates. By using the equation of straight the algorithm finds the coordinates the corners. of extracted markers. As a result, even if all corners are obscured, the algorithm can find all of them and this was proved through the experiment.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.32-37
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• 2016

Sidewall angle (SWA) of an absorber stack in extreme ultraviolet lithography mask is considered to be $90^{\circ}$ ideally, however, it is difficult to obtain $90^{\circ}$ SWA because absorber profile is changed by complicated etching process. As the imaging performance of the mask can be varied with this SWA of the absorber stack, more complicated optical proximity correction is required to compensate for the variation of imaging performance. In this study, phase shift mask (PSM) is suggested to reduce the variation of imaging performance due to SWA change by modifying mask material and structure. Variations of imaging performance and lithography process margin depending on SWA were evaluated through aerial image and developed resist simulations to confirm the advantages of PSM over the binary intensity mask (BIM). The results show that the variations of normalized image log slope and critical dimension bias depending on SWA are reduced with PSM compared to BIM. Process margin for exposure dose and focus was also improved with PSM.