pirahansiah

سلام
من در زمینه Thresholding تحقیق می کنم
خوشحال می شود اگر کسی ایده یا سورس ( به زبان ویژوال سی 6 ) در این زمینه داشته باشه در اختیار بنده بگذارد
Thresholding یکی از مباحث مهم در زمینه پردازش تصویر است ( Preprocessing )
وقتی که ما یک تصویر داریم و می خواهیم آن را به سیاه و سفید تبدیل کنیم از این روش استفاده می کنیم. به این دلیل که نیاز به کاهش حجم حافظه و پردازش سریعتر داریم و البته دلیل دیگر آن جدا سازی زمینه تصویر از اشیاء موجود در تصویر است.
روشهای مختلفی وجود دارد از جمله
Abdullah, N. H. S. e. a. (2010). "Multi- thresholding for license plate recognition system." International Conference on Signal and Image Processing 2010 (accept for publish).

Abdullah, S. N. H. S., M. Khalid, et al. (2007). Comparison of Feature Extractors in License Plate Recognition. Modelling & Simulation, 2007. AMS '07. First Asia International Conference on.
Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates using blob labeling and clustering for segmentation, seven popular and one proposed edge detectors for feature extraction and neural networks for classification. There were eight experiments conducted using eight different edge detectors: Kirsch, Sobel, Laplacian, Wallis, Prewitt, Frei Chen and a proposed edge detector. The result had shown kirsch edge detectors is the best technique for feature exractor while the proposed achieved better results compared to Prewitt, Frei Chen and Wallis

Arora, S., J. Acharya, et al. (2008). "Multilevel thresholding for image segmentation through a fast statistical recursive algorithm." Pattern Recognition Letters 29(2): 119-125.
A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for the next step, until no significant improvement in image quality can be achieved. The method makes use of the fact that a number of distributions tend towards Dirac delta function, peaking at the mean, in the limiting condition of vanishing variance. The procedure naturally provides for variable size segmentation with bigger blocks near the extreme pixel values and finer divisions around the mean or other chosen value for better visualization. Experiments on a variety of images show that the new algorithm effectively segments the image in computationally very less time. © 2007 Elsevier B.V. All rights reserved.

Hae-Yeoun, L., N. C. F. Codella, et al. (2010). "Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI." Biomedical Engineering, IEEE Transactions on 57(4): 905-913.
An automatic left ventricle (LV) segmentation algorithm is presented for quantification of cardiac output and myocardial mass in clinical practice. The LV endocardium is first segmented using region growth with iterative thresholding by detecting the effusion into the surrounding myocardium and tissues. Then the epicardium is extracted using the active contour model guided by the endocardial border and the myocardial signal information estimated by iterative thresholding. This iterative thresholding and active contour model with adaptation (ITHACA) algorithm was compared to manual tracing used in clinical practice and the commercial MASS Analysis software (General Electric) in 38 patients, with Institutional Review Board (IRB) approval. The ITHACA algorithm provided substantial improvement over the MASS software in defining myocardial borders. The ITHACA algorithm agreed well with manual tracing with a mean difference of blood volume and myocardial mass being 2.9 ± 6.2 mL (mean ± standard deviation) and -0.9 ± 16.5 g, respectively. The difference was smaller than the difference between manual tracing and the MASS software (approximately -20.0 ± 6.9 mL and -1.0 ± 20.2 g, respectively). These experimental results support that the proposed ITHACA segmentation is accurate and useful for clinical practice.

Hammouche, K., M. Diaf, et al. (2010). "A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem." Engineering Applications of Artificial Intelligence 23(5): 676-688.
The multilevel thresholding problem is often treated as a problem of optimization of an objective function. This paper presents both adaptation and comparison of six meta-heuristic techniques to solve the multilevel thresholding problem: a genetic algorithm, particle swarm optimization, differential evolution, ant colony, simulated annealing and tabu search. Experiments results show that the genetic algorithm, the particle swarm optimization and the differential evolution are much better in terms of precision, robustness and time convergence than the ant colony, simulated annealing and tabu search. Among the first three algorithms, the differential evolution is the most efficient with respect to the quality of the solution and the particle swarm optimization converges the most quickly.

Horng, M. H. (2010). "A multilevel image thresholding using the honey bee mating optimization." Applied Mathematics and Computation 215(9): 3302-3310.
Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the maximum entropy thresholding (MET) has been widely applied. In this paper, a new multilevel MET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. This proposed method is called the maximum entropy based honey bee mating optimization thresholding (MEHBMOT) method. Three different methods such as the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) and the Fast Otsu's method are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed MEHBMOT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other three thresholding methods, the segmentation results using the MEHBMOT algorithm is the best and its computation time is relatively low. Furthermore, the convergence of the MEHBMOT algorithm can rapidly achieve and the results validate that the proposed MEHBMOT algorithm is efficient. © 2009 Elsevier Inc. All rights reserved.

Horng, M. H. (2010). "Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization." Expert Systems with Applications 37(6): 4580-4592.
Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) has been widely applied. In this paper, a new multilevel MCET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. Three different methods included the exhaustive search, the particle swarm optimization (PSO) and the quantum particle swarm optimization (QPSO) methods are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed HBMO-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other two thresholding methods, the segmentation results using the HBMO-based MCET algorithm is the best. Furthermore, the convergence of the HBMO-based MCET algorithm can rapidly achieve, and the results are validated that the proposed HBMO-based MCET algorithm is efficient. © 2009 Elsevier Ltd. All rights reserved.

J. Acharya, G. S. (2007). "A novel electrostatics based image binarization technique." International Conference Systemics, Cybernetics and Informatics.

Kittler, J. and J. Illingworth (1986). "Minimum error thresholding." Pattern Recognition 19(1): 41-47.
A computationally efficient solution to the problem of minimum error thresholding is derived under the assumption of object and pixel grey level values being normally distributed. The method is applicable in multithreshold selection.

Lázaro, J., J. L. Martín, et al. (2010). "Neuro semantic thresholding using OCR software for high precision OCR applications." Image and Vision Computing 28(4): 571-578.
This paper describes a novel approach to binarization techniques. It presents a way of obtaining a threshold that depends both on the image and the final application using a semantic description of the histogram and a neural network. The intended applications of this technique are high precision OCR algorithms over a limited number of document types. The input image histogram is smoothed and its derivative is found. Using a polygonal version of the derivative and the smoothed histogram, a new description of the histogram is calculated. Using this description and a training set, a general neural network is capable of obtaining an optimum threshold for our application.

Liou, R. J., M. H. Horng, et al. (2009). Multi-level thresholding selection by using the honey bee mating optimization, Shenyang.
Image thresholding is an important technique for image processing and pattern recognition. In this paper, a new multilevel image thresholding algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. Three different methods such as the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) and the Fast Otsu's method are also implemented for comparison with the results of the proposed method. The experimental results reveal two important interested results for other three image thresholding methods. One is that the results of PSO and Fast Ostu's method are unstable that extraordinary segmentations are generated. Another is that the results of HCOCLPSO are superior to original PSO method, but it still slower than ones of HBMO and it had similar segmentation results with the ones of the honey bee mating optimization. © 2009 IEEE.

Otsu, N. (1979). "A Threshold Selection Method from Gray-Level Histograms." Systems, Man and Cybernetics, IEEE Transactions on 9(1): 62-66.

Pai, Y.-T., Y.-F. Chang, et al. (2010). "Adaptive thresholding algorithm: Efficient computation technique based on intelligent block detection for degraded document images." Pattern Recognition 43(9): 3177-3187.
Document image binarization involves converting gray level images into binary images, which is a feature that has significantly impacted many portable devices in recent years, including PDAs and mobile camera phones. Given the limited memory space and the computational power of portable devices, reducing the computational complexity of an embedded system is of priority concern. This work presents an efficient document image binarization algorithm with low computational complexity and high performance. Integrating the advantages of global and local methods allows the proposed algorithm to divide the document image into several regions. A threshold surface is then constructed based on the diversity and the intensity of each region to derive the binary image. Experimental results demonstrate the effectiveness of the proposed method in providing a promising binarization outcome and low computational cost.

تشخیص آستانه یا Thresholding اگر درست انتخاب شود تاثیر زیادی در تشخیص صحیح classification and recognition دارد


یک مثال می تواند این موضوع را بهتر توضیح بدهد
به عنوان نمونه ما یک عکس با سطح خاکستری Gray level داریم در این حالت مقدار هر پیکسل بین 0 تا 255 خواهد بود ( عمق 8 ) و حالا توسط الگوریتمی مقدار Threshold را پیدا کرده ایم مثلا 130 در این حالت
هر پیکسل که مقدارش کمتر از 130 باشد به 0 و
هر پیکسل که مقدارش بیشتر از 130 باشد به 1 تغییر می کنید
بعد از این عملیات ما یک عکس سیاه و سفید داریم.

از روشهای جدیدی که برای این الگوریتم وجود دارد می توان به
multilevel thresholding اشاره نمود که شامل چندین بازه است که توسط آنها مقدار پیکسل ها تغییر می کند