• Journal of Korea Multimedia Society
• /
• v.11 no.12
• /
• pp.1649-1657
• /
• 2008

In this paper, to overcome the failure of binarization for the characters suffered from low contrast and non-uniform illumination in license plate character recognition system, we improved the binarization method by combining local thresholding with global thresholding and edge detection. Firstly, apply the local thresholding method to locate the characters in the license plate image and then get the threshold value for the character based on edge detector. This method solves the problem of local low contrast and non-uniform illumination. Finally, back-propagation Neural Network is selected as a powerful tool to perform the recognition process. The results of the experiments i1lustrate that the proposed binarization method works well and the selected classifier saves the processing time. Besides, the character recognition system performed better recognition accuracy 95.7%, and the recognition speed is controlled within 0.3 seconds.

• 한국정보컨버전스학회:학술대회논문집
• /
• 2008.06a
• /
• pp.113-116
• /
• 2008

In this paper we suggest two novel methods for an implementation of the spot detection phase in the 2-DE gel image analysis program. The one is the adaptive thresholding method for eliminating noises and the other is the asymmetric diffusion model for spot matching. Remained noises after the preprocessing phase cause the over-segmentation problem by the next segmentation phase. To identify and exclude the over-segmented background regions, il we use a fixed thresholding method that is choosing an intensity value for the threshold, the spots that are invisible by one's human eyes but mean very small amount proteins which have important role in the biological samples could be eliminated. Accordingly we suggest the adaptive thresholding method which comes from an idea that is got on statistical analysis for the prominences of the peaks. There are the Gaussian model and the diffusion model for the spot shape model. The diffusion model is the closer to the real spot shapes than the Gaussian model, but spots have very various and irregular shapes and especially asymmetric formation in x-coordinate and y-coordinate. The reason for irregularity of spot shape is that spots could not be diffused perfectly across gel medium because of the characteristics of 2-DE process. Accordingly we suggest the asymmetric diffusion model for modeling spot shapes. In this paper we present a brief explanation ol the two methods and experimental results.

• Korean Journal of Remote Sensing
• /
• v.24 no.4
• /
• pp.341-349
• /
• 2008

This study proposes new automatic thresholding method, which is important step for detecting binary change/non-change information using satellite images. Result value through pixel-based similarity measurement is calculated cumulatively with regular interval, and thresholding is pointed at the steep slope position. The proposed method is assessed in comparison with expectation-maximization algorithm and coner method using synthetic images, ALI images, and Hyperion images. Throughout the results, we validated that our method can guarantee the similar accuracy with previous algorithms. It is simpler than EM algorithm, and can be applied to the binormal histogram unlike the coner method.

• Transactions on Control, Automation and Systems Engineering
• /
• v.4 no.3
• /
• pp.224-230
• /
• 2002

In this paper, a counting algorithm hybridized with an adaptive automatic thresholding method based on Otsu's method and the algorithm that elongates markers obtained by the well-known watershed algorithm is proposed to enhance the exactness of the microcell counting in microscopic images. The proposed counting algorithm can be stated as follows. The transformed full image captured by CCD camera set up at microscope is divided into cropped images of m$\times$n blocks with an appropriate size. The thresholding value of the cropped image is obtained by Otsu's method and the image is transformed into binary image. The microbial cell images below prespecified pixels are regarded as noise and are removed in tile binary image. The smoothing procedure is done by the area opening and the morphological filter. Watershed algorithm and the elongating marker algorithm are applied. By repeating the above stated procedure for m$\times$n blocks, the m$\times$n segmented images are obtained. A superposed image with the size of 640$\times$480 pixels as same as original image is obtained from the m$\times$n segmented block images. By labeling the superposed image, the counting result on the image of microbial cells is achieved. To prove the effectiveness of the proposed mettled in counting the microbial cell on the image, we used Acinetobacter sp., a kind of ammonia-oxidizing bacteria, and compared the proposed method with the global Otsu's method the traditional watershed algorithm based on global thresholding value and human visual method. The result counted by the proposed method shows more approximated result to the human visual counting method than the result counted by any other method.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.43 no.5 s.311
• /
• pp.29-37
• /
• 2006

Thresholding is a fundamental approach to segmentation that utilizes a significant degree of pixel popularity or intensity. Otsu's thresholding employed the normalized histogram as a discrete probability density function. Also it utilized a criterion that minimizes the between-class variance of pixel intensity to choose a threshold value for segmentation. However, the Otsu's method has a disadvantage of repeatedly searching optimal thresholds for the entire range. In this paper, a simple but fast multi-level thresholding approach is proposed by means of extending the Otsu's method. Rather than invoke the Otsu's method for the entire gray range, we advocate that the gray-level range of an image be first divided into smaller sub-ranges, and that the multi-level thresholds be achieved by iteratively invoking this dividing process. Initially, in the proposed method, the gray range of the object image is divided into 2 classes with a threshold value. Here, the threshold value for segmentation is selected by invoking the Otsu's method for the entire range. Following this, the two classes are divided into 4 classes again by applying the Otsu's method to each of the divided sub-ranges. This process is repeatedly performed until the required number of thresholds is obtained. Our experimental results for three benchmark images and fifty faces show a possibility that the proposed method could be used efficiently for pattern matching and face recognition.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.8 no.3
• /
• pp.92-99
• /
• 1999

The inspection unit which is developed and used in this study, is processed the shape data from the CCD camera to seek welding bite section shape, and then calculated as a real dimension from measuring the value of each inspection item. The reason of measuring with the real in this study is came out from the image method which used for a long time, which is extricated the characteristic as the dimension of pixel by recognize pixel. The measurement method of the section shape is that we decide the thresholding value after we drew the histogram to binarizate the object. After that, we make flat the object to get rid of the noise and measure the shape of welded part through the boundarization of the object. The shape measurement is that measure the value of the welding part to adapt the actual operation program from using the ratio between the actual dimension of the standard specimen and the dimension of image, to measure the ratio between the actual product and the camera image. The inspection algorithm which estimates the quality of welded product is developed and also, the software GUI(Graphic User Interface) which processes the automatic test function of the inspection system is developed. We make the foundation of the inspection automatic system and we will help to apply other welding machine.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.6
• /
• pp.276-286
• /
• 2013

SVD-based deconvolution algorithm has been known as the most effective technique for CT perfusion image analysis. In this algorithm, in order to reduce noise effects, SVD coefficients smaller than a certain threshold are removed. As the truncation threshold, either a fixed value or a variable threshold yielding a predetermined OI (oscillation index) is frequently employed. Each of these two thresholding methods has an advantage to the other either in accuracy or efficiency. In this paper, we propose a Monte Carlo simulation method to evaluate the accuracy of the two methods. An extension of the proposed method is presented as well to measure the effects of image smoothing on the accuracy of the thresholding methods. In this paper, after the simulation method is described, experimental results are presented using both simulated data and real CT images.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.42 no.6
• /
• pp.93-100
• /
• 2005

We propose an optimal thresholding method for the voxel coloring in the reconstruction of a 3D shape. Our purposed method is a new approach to resolve the trade-off error of the threshold value on determining the photo-consistency in the conventional method. Optimal thresholding value is decided to compare the photo-consistency of a surface with inside voxel on the optic ray of the center camera. As iterating the process of the vokels, the threshold is approached to the optimal value for the individual surface voxel. And also, graph cut method is reduced to the surface noise on eliminating neighboring voxel. To verify the proposed algorithm, we simulated in the virtual and real environment. It is advantaged to speed up and accuracy of a 3D face reconstruction by applying the methods of optimal threshold and graph as compare with conventional algorithms.

• The Korean Journal of Nuclear Medicine
• /
• v.37 no.6
• /
• pp.374-381
• /
• 2003

Purpose: Determining an appropriate thresholding is crucial for PDG PET analysis since strong control of Type I error could fail to find pathological differences between eariy Alzheimer' disease (AD) patients and healthy normal controls. We compared the SPM results on FDG PET imaging of early AD using uncorrected p-value, random-field based corrected p-value and false discovery rate (FDR) control. Materials and Methods: Twenty-eight patients ($66{\pm}7$ years old) with early AD and 18 age-matched normal controls ($68{\pm}6$ years old) underwent FDG brain PET. To identify brain regions with hypo-metabolism in group or individual patient compared to normal controls, group images or each patient's image was compared with normal controls usingthe same fixed p-value of 0.001 on uncorrected thresholding, random-field based corrected thresholding and FDR control. Results: The number of hypo-metabolic voxels was smallest in corrected p-value method, largest in uncorrected p-value method and intermediate in FDG thresholding in group analysis. Three types of result pattern were found. The first was that corrected p-value did not yield any voxel positive but FDR gave a few significantly hypometabolic voxels (8/28, 29%). The second was that both corrected p-value and FDR did not yield any positive region but numerous positive voxels were found with the threshold of uncorrected p-values (6/28, 21%). The last was that FDR was detected as many positive voxels as uncorrected p-value method (14/28, 50%). Conclusions FDR control could identify hypo-metaboiic areas in group or individual patients with early AD. We recommend FDR control instead of uncorrected or random-field corrected thresholding method to find the areas showing hypometabolism especially in small group or individual analysis of FDG PET.

• Journal of applied mathematics & informatics
• /
• v.26 no.3_4
• /
• pp.661-672
• /
• 2008

In this paper an algorithm, based on extracting a certain target of an image, is proposed that is capable of performing bilevel thresholding of image with multimodal distribution. Each pixel in the image has a membership value which is used to denote the characteristic relationship between the pixel and its belonging region (i.e. the object or background). Using the membership values of image set, a new measurement, which simultaneously measures the measure of fuzziness and the conditional entropy of the image, is calculated. Then, thresholds are found by optimally minimizing calculated measurement. In addition, a fuzzy range is defined to improve the threshold values. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively extract the meaningful target from the input image. The resulting image can preserve the object region we target very well.