• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.90-93
• /
• 2013

This paper presents a multistage adaptive thresholding method and circular Hough transform for pupil detection. Multistage adaptive thresholding is a thresholding method that applies local image statistic within a neighborhood variable and the global thresholds. Therefore, the method can adopt the benefit of local thresholding and prevent an over segmentation at the same time because of the global image information. To detect a pupil, a circular Hough transform is applied to it in which the pupil pattern is considered as a circle shape. The experimental results show the reliability of our proposed method in detecting pupil properly.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.8
• /
• pp.108-114
• /
• 2003

This research describes a image-processing technique for the scratch detecting algorithm for ITO coated glass. We use the modified logical thresholding method (called adaptive logical thresholding method) for binarization of gray-scale glass image. This method is useful to the algorithm for detecting the scratch of ITO coated glass automatically without need of any prior information of manual fine-tuning of parameters.

• Journal of the Korea Society of Computer and Information
• /
• v.8 no.4
• /
• pp.7-12
• /
• 2003

This paper propose the 1-pass spatially adaptive wavelet thresholding for image denosing. The method of wavelet thresholding for denosing, has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This spatially adaptive thresholding is extended to the overcomplete wavelet expansion, which yields better results than the orthogonal transform. Experiments show that this proposed method does indeed remove noise significantly, especially for large noise power. Experimental results show that the proposed method outperforms level dependent thresholding techniques and is comparable to spatial Wiener filtering method, 2-pass spatially adaptive wavelet thresholding method in matlab.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.38 no.1
• /
• pp.19-32
• /
• 2001

This paper proposes a local adaptive thresholding method based on a water flow model, in which an image surface is considered as a 3-dimensional (3-D) terrain. To extract characters from backgrounds, we pour water onto the terrain surface. Water flows down to the lower regions of the terrain and fills valleys. Then, the amount of filled water is thresholded, in which the proposed thresholding method is applied to gray level document images consisting of characters and backgrounds. The proposed method based on a water flow model shows the property of locally adaptive thresholding. Computer simulation with synthetic and real document images shows that the proposed method yields effective adaptive thresholding results for binarization of document images.

• Journal of the Korea Society of Computer and Information
• /
• v.7 no.4
• /
• pp.163-167
• /
• 2002

This paper propose the new spatially adaptive wavelet thresholding for image denosing. The method of wavelet thresholding for denosing, has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. Experimental results show that the proposed method outperforms level dependent thresholding techniques and is comparable to spatial Wiener filtering method in matlab.

• Proceedings of the IEEK Conference
• /
• summer
• /
• pp.438-441
• /
• 2004

Image binarization using a global threshold value [3] performs at high speed, but usually results in undesired binary images when the source images are of poor quality. In such cases, adaptive local thresholding algorithms [1][2][3] are used to obtain better results, and the algorithm proposed by A.E.Savekis which chooses local threshold using fore­ground and background clustering [1] is one of the best thresholding algorithms. However, this algorithm runs slowly due to its re-computing threshold value of each central pixel in a local window MxM. In this paper, we present a dynamic programming approach for the step of calculating local threshold value that reduces many redundant computations and improves the execution speed significantly. Experiments show that our proposal improvement runs more ten times faster than the original algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.287-287
• /
• 2000

As a method to control the bacteria number in adequate level, a real time control system based on microscope image processing measurement for the bacteria is adopted. For the experiment, Ammonia-oxidizing bacteria such as Acinetobacter sp. are used. This paper proposed hybrid method combined watershed algorithm with adaptive automatic thresholding method to enhance segmentation efficiency of overlapped image. Experiments was done to show the effectiveness of the proposed method compared to traditional Otsu's method, Otsu's method with adaptive automatic thresholding method and human visual method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.11
• /
• pp.2453-2458
• /
• 2012

In this paper, we propose an adaptive thresholding method to binarize two-dimensional barcode images. Adaptive thresholding methods that minimize light effects convert an original image into a binary image. The methods are applied to document image binarization. The methods, however, have problems of determining box size used in adaptive thresholding. thus, they inappropriate to use in recognition of two-dimensional barcode images. To overcome the problem, we analysis the problem and propose a new adaptive threshold method using the integral image. To show the effectiveness of our method, we compared our method with the well-known existing methods in terms of visual quality and processing time. The experimental result indicates that the proposed method is superior to the existing method.

• Journal of information and communication convergence engineering
• /
• v.10 no.1
• /
• pp.45-52
• /
• 2012

Thedenoising of a natural image corrupted by Gaussian noise is a long established problem in signal or image processing. Today the research is focus on the wavelet domain, especially using the wavelet threshold method. In this paper, a waveletbased image denoising modified adaptive thresholding method is proposed. The proposed method computes thethreshold adaptively based on the scale level and adaptively estimates wavelet coefficients by using a modified thresholding function that considers the dependency between the parent coefficient and child coefficient and the soft thresholding function at different scales. Experimental results show that the proposed method provides high peak signal-to-noise ratio results and preserves the detailed information of the original image well, resulting in a superior quality image.

• Journal of Broadcast Engineering
• /
• v.2 no.1
• /
• pp.45-55
• /
• 1997

In this paper, we propose a fast algorithm with adaptive thresholding for the wavelet transform (WT) based blocking artifact reduction. In the fast algorithm, all processings that are equivalent to the processing in WT domain of the first and second scale are performed in spatial domain. In the adaptive thresholding, the threshold values used to classify the block boundary are selected adaptively according to each input image by using the statistical properties of the WT of the coded signal at block boundary and at block center, which can be obtained in spatial domain. Experimental results showed that the proposed fast algorithm is about 10 times faster than the WT-based algorithm. It also was found that the postprocessing with proposed adaptive thresholding yields some PSNR improvement and better subjective quality over that with nonadaptive thresholding which has best performance at high compression ratios of a certain .image, even at low compression ratios.