A generalization of Otsu method for linear separation of two unbalanced classes in document image binarization

The classical Otsu method is a common tool in document image binarization. Often, two classes, text and background, are imbalanced, which means that the assumption of the classical Otsu method is not met. In this work, we considered the imbalanced pixel classes of background and text: weights of two classes are different, but variances are the same. We experimentally demonstrated that the employment of a criterion that takes into account the imbalance of the classes' weights, allows attaining higher binarization accuracy. We described the generalization of the criteria for a two-parametric model, for which an algorithm for the optimal linear separation search via fast linear clustering was proposed. We also demonstrated that the two-parametric model with the proposed separation allows increasing the image binarization accuracy for the documents with a complex background or spots.We are grateful for the insightful comments offered by D.P. Nikolaev. This research was partially supported by the Russian Foundation for Basic Research No. 19-29-09066 and 18-07-01387

OCR-directed evaluation of binarization techniques

The objective of this work is to study different binarization methods and to investigate their effect on the performance of OCR systems. Two sets of document images and four OCR systems were used to study several binarization algorithms. The simplest method that chooses the median value of the gray levels, i.e., 127 from 256 levels, as the global threshold value did not work well unless the scanner characteristic matched with the nature of a collection of documents by chance. The best-fixed method uses the global threshold value that minimizes the number of overall errors for a combination of an OCR system and a collection of documents. Both Otsu\u27s global algorithm and Niblack\u27s local algorithm performed, on the average, as well as the best-fixed method for the test data sets. The ideal global threshold method selects the best global threshold value for each combination of a page and an OCR system. Although the ideal method outperformed, on the average, Niblack\u27s method, Niblack\u27s method processed some images better than the ideal method

Evaluation of yarn characteristics using computer vision and image processing

Irregularity, hairiness and twist are among the most important characteristics that define yarn quality. This thesis describes computer vision and image processing techniques developed to evaluate these characteristics. The optical and electronic aspects such as the illumination, lens parameters and aberrations play crucial role on the quality of yam images and on the overall performance of image processing. The depth of field limitation being the most important restraint in yam imaging as well as image distortion in line scan cameras arising from digitisation and yam movement are modelled mathematically and verified through experiments both for front-lit and back-lit illuminations. Various light sources and arrangements are tested and relative advantages and disadvantages are discussed based on the image quality. Known problems in defining the hair-core boundaries and determining the total hairiness from yam images are addressed and image enhancement and processing algorithms developed to overcome these problems are explained. A method to simulate various yam scanning resolution conditions is described. Using this method, the minimum scanning resolution limits to measure the hairiness and irregularity are investigated. [Continues.

Image segmentation for defect detection on veneer surfaces

Machine vision is widely used in scientific areas and non-wood using industries, but the extreme variability of wood has limited its adoption by forest products industries. However, it is now becoming a key factor in further automation of the forest products industry. As a very important part of machine vision, developing image segmentation algorithms that can be used for wood products is an ambitious undertaking. The focus of this research was to adapt existing and develop some new segmentation algorithms which could be used to detect defects on veneer surfaces. Nine algorithms covering three segmentation technique categories were explored. Three existing edge detection algorithms were modified for use on veneer images, and four existing thresholding algorithms were adapted in both global and local versions. Two new region extraction algorithms were developed specifically for defect detection on veneer surfaces. The performances of these nine algorithms were tested and compared under the combinations of two camera resolutions (5-bit and 8-bit), three color spaces (RGB, Lab, and gray-scale), and seven surface features (clear wood, blue stain, loose knot, pitch pocket, pitch streak, tight knot, and wane). Ten sample images for each of seven surface features on Douglas-fir veneer [Pseudotsuga menziesii] were used. Ten measures were proposed for performance evaluation. A multi-factor factorial ANOVA was used in the performance tests and comparisons. The best combinations of camera resolution and color space for each of the algorithms were determined. The 5-bit and 8-bit camera resolutions were not significantly different for the three edge detection and two region extraction algorithms, but the 8-bit camera resolution was better for all but one of the thresholding algorithms. That exception was the global Otsu thresholding algorithm, for which the 5-bit camera resolution was better. The RGB color space was the best for all algorithms. Overall, the two region extraction algorithms were the best. Under the best combination of factors, those two algorithms provided the highest defect detection accuracies of 91% for pitch streak samples and over 95% for loose knot, tight knot, and pitch pocket samples. These results were accomplished while still providing clear wood accuracies of over 95%. The one performance exception was blue stain, for which no satisfactory algorithm was found

Radyal taban fonksiyonlu yapay sinir ağı kullanarak zeki bir imza tanıma sistemi tasarımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Biyometrik sistemler bir bireyin kişisel bir nitelik ya da davranışını analiz ederek kimliğini açıklayan biyolojik verileri doğrulama bilimidir. İnsanları birbirinden ayırt edebilme şansını bize sunduğundan dolayı biyometri bir kimlik doğrulama sistemi olarak da kullanılmaktadır. En popüler biyometrik sistemlerden biri de imza tanıma ve doğrulamadır. Bu çalışmada çevirim dışı imza tanıma sistemi için bir uygulama gerçekleştirilmiştir. Çevrimdışı olarak 24 ayrı kişiden 36 tane, yani toplamda 864 tane imza toplanmıştır. 36 tane imzanın 26 tanesi yani toplamda 624 tane imza eğitim için,10 tanesi yani 240 tane imza test için ayrılmıştır. İmza tanıma uygulaması boyunca, ilkönce imza görüntüleri bir tarayıcı yardımıyla 450X250 boyutlarında alınmıştır. Bu görüntüler gri seviyeli görüntülere çevrilmiştir. Ondan sonra Otsu otomatik eşik seçme metoduyla ikili görüntülere çevrilmiştir. Bundan sonra, kenar inceltme metoduyla, ikili imza görüntüleri inceltilmiştir. İmza görüntülerinin özellikleri bundan sonra imzanın çevresindeki gereksiz boşluklar çıkarılarak, sahip oldukları boyutlarda bulunmuştur. İmzaların yoğunluk, genişlik, yükseklik, genişlik yükseklik oranı, x eksenindeki ağırlık merkezi, y eksenindeki ağırlık merkezi, genişliğinin x eksenindeki orta noktası, yüksekliğinin y eksenindeki orta noktası, x eksenindeki ağırlık merkezi ile genişliğinin orta noktası arasındaki fark, y eksenindeki ağırlık merkezi ile yüksekliğinin orta noktası arasındaki fark özellik çıkarma metotları kullanılmıştır. Ve bundan başka imzalar ağırlık merkezlerinden 4 eşit parçaya bölünmüştür. Ve bu her bir parça tekrar ağırlık merkezlerinden 4 eşit parçaya bölünmüştür. İmzaların sınıflandırılması radyal taban fonksiyonlu sinir ağında tasarlanmış ve kullanılmıştır. Tasarlanmış RBF sinir ağında, imza sınıflarına ait özelliklere dayalı 30 öz giriş ve 24 çıkış kullanılmıştır Çalışmada 91.6667 % sınıflandırma başarımı gözlenmiştir. Doğrulama işlemi gerçekleştirilmemiştir. Sinir sayılarının maksimum sayısı ve yayılım değeri analiz edilmiştir. Yayılım değerleri 1, 5, 10, 15, 20, 25 ve maksimum sinir sayısı 50,150, 225, 230, 235, 250, 300, 400, 550 olarak değiştirilmiştir. Yayılım değeri 1 ve maksimum sinir sayısı 225 veya 235 olduğunda en iyi performansa ulaşılmıştır. Bu çalışmaların hepsi tablo, grafik ve şekillerle gösterilmiştir.  Anahtar kelimeler: Matlab, yapay sinir ağları, radyal taban fonksiyonu, çevirimdışı imza tanımaBiometric systems are being verificated that analyzing personal character or behavior so describing identify. Biometrics is used as an authentication system because of providing to distinguish between people. One of the most popular biometric systems is signature recognition and verification systems. During the study, 864 signatures were collected offline. These signatures were taken from 24 different people. 36 signatures were collected from each person. 26 signatures have been used for the training process and other remaining signatures have been used for testing. During the implementation of the signature recognition, firstly the signature images have been taken to computer by using a scanner as 450x250 images. These images have been firstly converted to gray level image. Then, these images have been converted to binary images by using Otsu automatic threshold selection method. After that edge thinning operation has been applied to the binary signature images. Signature features of images have been found in the size that they have by removing unnecessary spaces around after the signature. Signature's density, width, height, ratio of width and height, center of gravity at x axis, center of gravity at y axis, midpoint of width, midpoint of height, difference between center of gravity at x and midpoint of width, difference between center of gravity at y and midpoint of height feature extraction methods have been used. And also, the signatures have been divided into mainly 4 pieces based on the geometric centroid of the signature image. Then, each part has been again divided into 4 pieces based on their centroid. For the classification of the signatures a radial bases neural network (RBFNN) has been designed and used. Designed RBF neural network has 30 inputs based on used features and 24 outputs belonging to signature classes. 91.6667 % classification performance have been observed during the study. Verification process has not been implemented. Efffect of the maximum number of neurons and spread values has analyzed. Spread values have changed 1, 5, 10, 15, 20, 25 and maximum number of neurons has been changed 50, 150, 225, 230, 235, 250, 300, 400, 550. When spread is 1 and maximum number of neurons has been 225 or 235 that the best performance has obtained. All of these have showed with tables, graphics and shapes. Keywords: Matlab, artificial neural networks, radial basis function, offline signature recognitio