Browsing by Subject "Mathematical morphology"
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Item Open Access An Automated rule based visual printed circuit board inspection system which uses mathematical morphological image processing algorithms(1990) Oğuz, Seyfullah HalitIn this thesis, the design and implementation of an automated rule based visual printed circuit board (PCB) inspection system are presented. The developed system makes use of mathematical morphology based image processing algorithms. This system is designed for the detection of the PCB defects related to the conducting structures on the PCBs. For this purpose, four new algorithms, three of which are defect detection algorithms, are designed, and an already existing algorithm is modified for its implementation in our system. The designed defect detection algorithms respectively verify the minimum conductor trace width, minimum land width, and the minimum conductor trace spacing requirements on digital binary PCB images. The implementation of a prototype system is made in our image processing laboratory and the necessary computer programs are developed. These programs control the image processor and apply the defect detection algorithms to discrete binary PCB test images.Item Open Access An automated system for design-rule-based visual inspection of printed circuit boards(IEEE, 1991) Oğuz, Seyfullah Halit; Onural, LeventThe design and the implementation of an automated, design-rule-based, visual printed circuit board (PCB) inspection system are presented. The system employs mathematical-morphology-based image processing algorithms. This system detects PCB defects related to the conducting structures on PCBs by checking a set of geometric design rules. For this purpose, an image segmentation algorithm and a defect detection algorithm are designed. The defect detection algorithm is capable of verifying the minimum conductor spacing, minimum conductor trace width, and the minimum land width requirements on digital binary PCB images. Also, an existing defect detection algorithm is modified for its implementation in the system.Item Open Access Automatic detection of geospatial objects using multiple hierarchical segmentations(Institute of Electrical and Electronics Engineers, 2008-07) Akçay, H. G.; Aksoy, S.The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classification. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback-Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes. © 2008 IEEE.Item Open Access Canlı hücre bölütlemesi için gözeticili öğrenme modeli(IEEE Computer Society, 2014-04) Koyuncu, Can Fahrettin; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, ÇiğdemAutomated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the other system steps. Thus, it is critical to implement robust and efficient segmentation algorithms for the design of successful systems. In the literature, the most commonly used methods for cell segmentation are marker controlled watersheds. These watershed algorithms assume that markers one-to-one correspond to cells and identify their boundaries by growing these markers. Thus, it is very important to correctly define the markers for these algorithms. The markers are usually defined by finding local minima/maxima on intensity or gradient values or by applying morphological operations on the corresponding binary image. In this work, we propose a new marker controlled watershed algorithm for live cell segmentation. The main contributions of this algorithm are twofold. First, different than the approaches in the literature, it implements a new supervised learning model for marker detection. In this model, it has been proposed to extract features for each pixel considering its neighbors' intensities and gradients and to decide whether this pixel is a marker pixel or not by a classifier using these extracted features. Second, it has been proposed to group the neighboring pixels based on the direction information and to extract features according to these groups. The experiments on 1954 cells show that the proposed algorithm leads to higher segmentation results compared to other watersheds. © 2014 IEEE.Item Open Access Image mining using directional spatial constraints(Institute of Electrical and Electronics Engineers, 2010-01) Aksoy, S.; Cinbiş, R. G.Spatial information plays a fundamental role in building high-level content models for supporting analysts' interpretations and automating geospatial intelligence. We describe a framework for modeling directional spatial relationships among objects and using this information for contextual classification and retrieval. The proposed model first identifies image areas that have a high degree of satisfaction of a spatial relation with respect to several reference objects. Then, this information is incorporated into the Bayesian decision rule as spatial priors for contextual classification. The model also supports dynamic queries by using directional relationships as spatial constraints to enable object detection based on the properties of individual objects as well as their spatial relationships to other objects. Comparative experiments using high-resolution satellite imagery illustrate the flexibility and effectiveness of the proposed framework in image mining with significant improvements in both classification and retrieval performance.Item Open Access Konuma bağlı uzamsal ilişkilerin biçimbilimsel modellenmesi(IEEE, 2007-06) Cinbiş, R. Gökberk; Aksoy, SelimUzamsal bilgi, görüntü analizi modellerinde çok önemli bir yer tutmaktadır. Bu bildiride, ikili ve üçlü uzamsal ilişkileri bulmak için, matemaktiksel biçimbilim kullanarak, özelleştirilebilir, gerçekçi ve hızlı yöntemler öneriyoruz. Bu ilişkiler,resmin her noktasında, referans nesneye veya nesnelere göre, istenilen ilişkinin değerini veren bir matris hesaplanarak gösterilmektedir. Modelimiz, bir nesnenin istenilen yönlerden gözükmeyecek kısımlarını da dikkate almayı mümkün kılmakta, ayrıca, nesnelerin uzamsal olarak çok farklı olduğu durumlara da başarılı olmaktadır. Yapay ve gerçek görüntülerde yaptığımız deneyler ise modelimizin diğer yöntemlere olan üstünlüğünü ortaya koymaktadır. Spatial information plays a very important role in image understanding. Fuzzy mathematical morphology provides an effective basis for extracting binary and ternary spatial relationships by creating a fuzzy landscape where the value at each point corresponds to the relationship degree according to its position with respect to the reference object(s). We improve existing morphological approaches in terms of flexibility and efficiency while also obtaining more intuitive results. Our morphological definitions are sensitive to relative visibility of areas based on partial occlusions, and can also cope with the cases where some objects extend significantly differently relative to others. We show the effectiveness of the proposed definitions using synthetic and real images.Item Open Access Morphological subband decomposition structure using GF(N) arithmetic(IEEE, 1996-09) Gürcan, Metin Nafi; Çetin, A. Enis; Gerek, Ömer, N.Linear filter banks with critical subsampling and perfect reconstruction (PR) property have received much interest and found numerous applications in signal and image processing. Recently, nonlinear filter bank structures with PR and critical subsampling have been proposed and used in image coding. In this paper, it is shown that PR nonlinear subband decomposition can be performed using the Gallois Field (GF) arithmetic. The result of the decomposition of an n-ary (e.g. 256-ary) input signal is still n-ary at different resolutions. This decomposition structure can be utilized for binary and 2k (k is an integer) level signal decompositions. Simulation studies are presented.Item Open Access Pap smear test görüntülerinde hücre çekirdeklerinin bölütlenmesi(IEEE, 2009-04) Kale, Aslı; Aksoy, Selim; Önder, S.Cervical cancer is a preventable disease and the dysplasia it causes can be scanned by using a pap smear test. It can be beneficial to develop a computer-assisted diagnosis system to make the pap smear test robust and widespread. The most fundamental part of such a system is the segmentation of nuclei and cytoplasm in cervical cell images. The aim of this study is to segment the nuclei in such images. First, markers on the nuclei are found by using mathematical morphology operations. Based on the obtained markers, marker-based watershed segmentation and balloon snake model are applied to find the nuclei contours in a data set consisting of cervical cell images. The data set is composed of six classes ranging according to the dysplasia degree of the cells. The results are evaluated according to the relative distance error measure, and the strengths and weakness of the methods are discussed. ©2009 IEEE.Item Open Access Relative position-based spatial relationships using mathematical morphology(IEEE, 2007-09-10) Cinbiş, R. Gökberk; Aksoy, SelimSpatial information is a crucial aspect of image understanding for modeling context as well as resolving the uncertainties caused by the ambiguities in low-level features. We describe intuitive, flexible and efficient methods for modeling pairwise directional spatial relationships and the ternary between relation using fuzzy mathematical morphology. First, a fuzzy landscape is constructed where each point is assigned a value that quantifies its relative position according to the reference object(s) and the type of the relationship. Then, the degree of satisfaction of this relation by a target object is computed by integrating the corresponding landscape over the support of the target region. Our models support sensitivity to visibility to handle areas that are partially enclosed by objects and are not visible from image points along the direction of interest. They can also cope with the cases where one object is significantly spatially extended relative to others. Experiments using synthetic and real images show that our models produce more intuitive results than other techniques. ©2007 IEEE.Item Open Access Surface profile determination from multiple sonar data using morphological processing(1998) Başkent, DenizIn this thesis, a novel method for surface profile determination using multiple sensors is presented. Our approach is based on morphological processing techniques to fuse the range data from multiple sensor returns in a manner that directly reveals the target surface profile. The method has the intrinsic ability of suppressing spurious readings due to noise, crosstalk, and higher-order reflections, as well as processing multiple reflections informatively. The approach taken is extremely flexible and robust, in addition to being simple and straightforward. It can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays. The profil of any continuous surface with varying curvature can be extracted as long as the curvature of the surface is not too high. The average processing time of the method is of the order of several seconds indicating that the method is viable for real-time applications. The algorithm is verified both by simulations and experiments in the laboratory by processing real sonar data obtained from the Nomad 200 mobile robot. The results are compared to those obtained from a more accurate structured-light system, which is however more complex and expensive.Item Open Access Surface profile determination from multiple sonar data using morphological processing(Sage Publications Ltd., 1999-08) Başkent, D.; Barshan, B.This paper presents a novel method for surface profile determination using multiple sensors. Our approach is based on morphological processing techniques to fuse the range data from multiple sensor returns in a manner that directly reveals the target surface profile. The method has the intrinsic ability of suppressing spurious readings due to noise, crosstalk, and higher-order reflections, as well as processing multiple reflections informatively. The approach taken is extremely flexible and robust, in addition to being simple and straightforward. It can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays. The algorithm is verified both by simulating and experiments in the laboratory by processing real sonar data obtained from a mobile robot. The results are compared to those obtained from a more accurate structured-light system, which is, however, more complex and expensive.