Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm

Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs

Intelligent laser scanning for computer aided manufacture.

Reverse engineering requires the acquisition of large amounts of data describing the surface of an object, sufficient to replicate that object accurately using appropriate fabrication techniques. This is important within a wide range of commercial and scientific fields where CAD models may be unavailable for parts that must be duplicated or modified, or where a physical model is used as a prototype. The three-dimensional digitisation of objects is an essential first step in reverse engineering. Optical triangulation laser sensors are one of the most popular and common non-contact methods used in the data acquisition process today. They provide the means for high resolution scanning of complex objects. Multiple scans of the object are usually required to capture the full 3D profile of the object. A number of factors, including scan resolution, system optics and the precision of the mechanical parts comprising the system may affect the accuracy of the process. A single perspective optical triangulation sensor provides an inexpensive method for the acquisition of 3D range image data

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

A Novel Probability-based Data Clustering Application for Detecting Elongated Clusters with Application to the Line Detection Problem

Ένα σημαντικό πρόβλημα που απαντάται σε διάφορα πεδία εφαρμογών, όπως η ανάλυση γεωχωρικών δεδομένων (geospatial data analysis), η συμπίεση εικόνας (image compression) και η εξαγωγή δρόμων (road extraction), μεταξύ άλλων παραδειγμάτων, είναι αυτό της ανίχνευσης ευθείων γρμμών ή ευθυγράμμων τμημάτων σε μια δεδομένη εικόνα. Στη διατριβή αυτή, προτείνεται μια νέα προσέγγιση στο παραπάνω πρόβλημα, η οποία βασίζεται στην πιθανοτική ομαδοποίηση (probabilistic clustering). Πιο συγκεκριμένα, ορίζεται μια νέα κατανομή πυκνότητας πιθανότητας η οποία αποτελεί παραλλαγή της Γκαουσσιανής κατανομής πιθανοτήτων (Gaussian probability distribution), στην οποία το κέντρο της δεν είναι πλέον σημείο, αλλά ένα ευθύγραμμο τμήμα, με στόχο τη μοντελοποίηση ευθυγράμμων τμημάτων. Στη συνέχεια, το σύνολο των δεδομένων σημείων θεωρείται ότι προέρχεται από μία κατανομή που εκφράζεται ως ένα σταθμισμένο άθροισμα (μίξη)επιμέρους κατανομών και ο στόχος είναι ο προσδιορισμός αυτών των κατανομών, κάθε μία από τις οποίες μοντελοποιεί και μια (γραμμική) συστάδα (linear cluster). Προτείνεται ένας αγόριθμος, ο οποίος ονομάζεται Αγλόριθμος Πιθανοτικής Συσταδοποίησης Ευθυγράμμων Τμημάτων (Probabilistic Line Segment Clustering algorithm – PLSC) και ακολουθεί τη λογική της Αποδόμησης Μίξης (Mixture Decomposition). Η διαδικασία εύρεσης βέλτιστων τοποθετήσεων των ευθυγράμμων τμημάτων (κέντρων των κατανομών πιθανοτήτων) φέρεται εις πέρας μέσω μιας επαναληπτικής διαδικασίας παρόμοιας του αλγορίθμου Αναμενόμενης Τιμής Βελτιστοποίησης (Expectation Maximization), κατά την οποία, τα τμήματα μετακινούνται σταδιακά με σκοπό να ταιριάξουν στις γραμμικές ομάδες που σχηματίζονται από τα δεδομένα, βάσει ενός ευρετικού κανόνα (heuristic rule). Ο αλγόριθμος δεν απαιτεί εκ των προτέρων γνώση του αριθμού των συστάδων. Αντί αυτού, ξεκινά κάνοντας μiα υπερεκτίμηση του πλήθους τους και σταδιακά τις μειώνει μέσω κατάλληλων μηχανισμών απαλοιφής και συνένωσης. Με σκοπό την τεκμηρίωση της αξίας της προτεινώμενης μεθόδου, διεξήχθησαν αρκετά πειράματα, τα αποτελέσματα των οποίων δείχνουν ότι η τρέχουσα μέθοδος είναι ικανή να αναγνωρίσει σε πολύ ικανοποιητικό βαθμό συστάδες τόσο σε απλούστερες όσο και σε πολυπλοκότερες περιπτώσεις. Ο αλγόριθμος μπορεί να αποδώσει παρόμοια και, σε μερικές περιπτώσεις, καλύτερα απολέσματα συγκρινόμενος με ένα επιλεγμένο πλήθος σχετικών δημοσιευμένων μεθόδων.Line detection is the process of identifying straight lines or line segments in a given image. Potential applications are commonly found in a variety of fields, such as analysis of geospatial data, image compression and road extraction, to name a few. In this dissertation an approach to the above problem based on probabilistic clustering is explored. A variation of the Gaussian probability distribution centered around a line segment is defined accordingly for the two dimensional space in order to model the line segments in the image under study and an algorithm, called Probabilistic Line Segment Clustering (PLSC) that follows the Mixture Decomposition approach is proposed. The process of finding the optimal positioning of the line segments is carried out by an iterative ExpectationMaximizationlike procedure in which the segments are gradually moved in order to fit the actual edges of the image using a heuristic rule. In order to find the appropriate number of segments/clusters, the algorithm starts with an overestimation of it and progressively reduces it via appropriate elimination and unification mechanisms. Toward supporting the value of the proposed method, experimental results have been carried out and discussed in which it is shown that the current method is able to appropriately identify clusters in multiple scenarios. The algorithm can perform mostly comparably and in some cases, even favorably with regard to a selection of relevant published methods

High-speed pattern cutting using real-time computer vision techniques

This thesis presents a study of computer vision for guiding cutting tools to perform high-speed pattern cutting on deformable materials. Several new concepts on establishing a computer vision system to guide a C02 laser beam to separate lace are presented. The aim of this study is to determine a cutting path on lace in real-time by using computer vision techniques, which is part of an automatic lace separation project. The purpose of this project is to replace the current lace separation process which uses a mechanical knife or scissors. The research on computer vision has concentrated on the following aspects: 1. A weighted incremental tracking algorithm based on a reference map is proposed, examined and implemented. This is essential for tracking an arbitrarily defined path across the surface of a patterned deformable material such as lace. Two methods, a weighting function and infinite impulse response filter, are used to cope with lateral distortions of the input image. Three consecutive map lines matching with one image line is introduced to cope with longitudinal distortion. A software and hardware hybrid approach boosts the tracking speed to hnls that is 2-4 times faster than the current mechanical method. 2. A modified Hough transform and the weighted incremental tracking algorithm to find the start point for tracking are proposed and investigated to enable the tracking to start from the correct position on the map. 3. In order to maintain consistent working conditions for the vision system, the light source, camera threshold and camera scan rate synchronisation with lace movement are studied. Two test rigs combining the vision and cutting system have been built and used to cut lace successfully

Map-image registration using automatic extraction of features from high resolution satellite images

In every part of the world the rate of map revision is alarmingly low, when compared to the rate of change of many human influenced surface features. Map making is very time-consuming and often information used for updates has become history before the updated map is made available. There is therefore a requirement to regularly gather up-to-date information about surface features and to incorporate changes in maps both quickly and efficiently. Automation of two systems, i.e. the automation of map-image registration and then of change detection can fulfill the requirements of map revision. This thesis works on the first system. The piece of work in this study has looked into a fast and an accurate solution to register high resolution satellite images to maps. This will allow changes in ground features to be used to update maps. Photogrammetric techniques used to update maps have previously shown good results, but they are tedious, time-consuming, and not beneficial for updating small changes at all. Feature extraction methods were used in the present study. The system developed was designed for automatic extraction of suitable areal features in images. The emphasis was on areal features rather than point or linear features because they have a distinctive shape, and they are extracted easily from vector as well as raster data. The extraction of suitable polygons, as control information, from images was obtained by using two matching techniques. Patch matching to extract the conjugate map and image polygons, and dynamic programming to find the corresponding matched boundary pixels of the map and image polygons. Some matched points were incorrect because of perspective, shadows and occlusions. A statistical model was developed to remove perspective distortion and large errors. The model demonstrated the removal of erroneous match points, and selected the good match points and registered the images to maps with a sub-pixel accuracy. A novel aspect of the study is that the automation is achieved with high accuracy in flat and moderate terrain areas without using height information, as it is essentially used in photogrammetric techniques

Analyse / synthèse de champs de tenseurs de structure : application à la synthèse d’images et de volumes texturés

This work is a part of the texture synthesis context. Aiming to ensure a faithful reproduction of the patterns and variations of orientations of the input texture, a two-stage structure/texture synthesis algorithm is proposed. It consists of synthesizing the structure layer showing the geometry of the exemplar and represented by the structure tensor field in the first stage, and using the resulting tensor field to constrain the synthesis of the texture layer holding more local variations, in the second stage. An acceleration method based on the use of Gaussian pyramids and parallel computing is then developed.In order to demonstrate the ability of the proposed algorithm to faithfully reproduce the visual aspect of the considered textures, the method is tested on various texture samples and evaluated objectively using statistics of 1st and 2nd order of the intensity and orientation field. The obtained results are of better or equivalent quality than those obtained using the algorithms of the literature. A major advantage of the proposed approach is its capacity in successfully synthesizing textures in many situations where traditional algorithms fail to reproduce the large-scale patterns.The structure/texture synthesis approach is extended to color texture synthesis. 3D texture synthesis is then addressed and finally, an extension to the synthesis of specified form textures using an imposed texture is carried out, showing the capacity of the approach in generating textures of arbitrary forms while preserving the input texture characteristics.Cette thèse s’inscrit dans le contexte de la synthèse d’images texturées. Dans l’objectif d’assurer une reproduction fidèle des motifs et des variations d’orientations d’une texture initiale, un algorithme de synthèse de texture à deux étapes « structure/texture » est proposé. Il s’agit, dans une première étape, de réaliser la synthèse d’une couche de structure caractérisant la géométrie de l’exemplaire et représentée par un champ de tenseurs de structure et, dans une deuxième étape, d’utiliser le champ de structure résultant pour contraindre la synthèse d’une couche de texture portant des variations plus locales. Une réduction du temps d’exécution est ensuite développée, fondée notamment sur l’utilisation de pyramides Gaussiennes et la parallélisation des calculs mis en oeuvre.Afin de démontrer la capacité de l’algorithme proposé à reproduire fidèlement l’aspect visuel des images texturées considérées, la méthode est testée sur une variété d’échantillons de texture et évaluée objectivement à l’aide de statistiques du 1er et du 2nd ordre du champ d’intensité et d’orientation. Les résultats obtenus sont de qualité supérieure ou équivalente à ceux obtenus par des algorithmes de la littérature. Un atout majeur de l’approche proposée est son aptitude à synthétiser des textures avec succès dans de nombreuses situations où les algorithmes existants ne parviennent pas à reproduire les motifs à grande échelle.L’approche de synthèse structure/texture proposée est étendue à la synthèse de texture couleur. La synthèse de texture 3D est ensuite abordée et, finalement, une extension à la synthèse de texture de forme spécifiée par une texture imposée est mise en oeuvre, montrant la capacité de l’approche à générer des textures de formes arbitraires en préservant les caractéristiques de la texture initiale

Analyse statistique de textures directionnelles - Application à la caractérisation de matériaux composites

Ce mémoire a pour objet l'analyse d'images de textures composées d'éléments structuraux orientés, ou textures directionnelles. Plus précisément, nous montrons qu'elles peuvent être décrites par l'analyse statistique du champ des orientations locales. En premier lieu, nous proposons une approche pour la conception d'opérateurs de mesure d'une orientation locale. Deux types d'opérateurs sont introduits, gradient et vallonnement, adaptés respectivement aux régions pentées et aux lignes de crête et de vallée. Guidés par le souci de robustesse et de précision, nous présentons une procédure d'optimisation des opérateurs et étudions leurs performances sur des textures synthétiques et naturelles. Nous définissons ensuite une méthode de description des textures directionnelles, fondée sur les statistiques d'ordre 2 du champ des orientations locales. Plus précisément, nous utilisons les différences spatiales d'orientations pour la construction de cartes d'interaction. Ces cartes sont appliquées à la caractérisation de textures de l'album de Brodatz et de textures de matériaux composites. Le dernier point concerne plus particulièrement la caractérisation des images de matériaux composites. Ces images, composées de primitives longiformes, sont décrites à l'aide d'une approche structurale. L'ondulation des primitives est mise en évidence par l'analyse du spectre de l'orientation calculée le long des primitives. Pour finir, nous proposons, pour ces textures, un modèle stochastique qui corrobore les résultats expérimentaux.This thesis aims at the characterization of textures composed of oriented patterns (i.e. directional textures). More precisely, we show that these textures are efficiently described through their local orientation field. Firstly, we propose a new framework for the conception of operators dedicated to the estimation of an orientation. Two kinds of operators are introduced, the gradient and the valleyness, which apply respectively on sloped regions and on crest or valley lines. We present an optimization procedure in order to ensure the precision and the robustness of the operators. Their performances are studied on synthetic and natural textures. We then define a new method for the description of directional textures, which is based on the second order statistics of the local orientation field. More precisely, the orientation spatial differences is used so as to construct orientation-based interaction maps. These maps are applied to the characterization of both Brodatz and composite material textures. Finally, we deal more specifically with the analysis of composite material images, which consist of wavelike directional primitives called fringes. A structural approach is used. It leads to the description of the fringes in terms of length and ripple. The ripple phenomenon is studied through the undulation spectrum, i.e. the spectrum of the orientation computed along the fringes. A stochastic model is finally provided in order to explain the experimental results