Texture can be broadly stated as spatial variation of image intensities. Texture analysis and classification is a well researched area for its importance to many computer vision applications. Consequently, much research has focussed on deriving powerful and efficient texture descriptors. Local binary patterns (LBP) and its variants are simple yet powerful texture descriptors. LBP features describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture descriptions. A comprehensive evaluation of different LBP variants on a common benchmark dataset is missing in the literature. This thesis presents the performance for different LBP variants on texture classification and retrieval tasks. The results show that multi-scale local binary pattern variance (LBPV) gives the best performance over eight benchmarked datasets. Furthermore, improvements to the Dominant LBP (D-LBP) by ranking dominant patterns over complete training set and Compound LBP (CM-LBP) by considering 16 bits binary codes are suggested which are shown to outperform their original counterparts. The main contribution of the thesis is the introduction of multi-dimensional LBP features, which preserve the relationships between different scales by building a multi-dimensional histogram. The results on benchmarked classification and retrieval datasets clearly show that the multi-dimensional LBP (MD-LBP) improves the results compared to conventional multi-scale LBP. The same principle is applied to LBPV (MD-LBPV), again leading to improved performance. The proposed variants result in relatively large feature lengths which is addressed using three different feature length reduction techniques. Principle component analysis (PCA) is shown to give the best performance when the feature length is reduced to match that of conventional multi-scale LBP. The proposed multi-dimensional LBP variants are applied for medical image analysis application. The first application is nailfold capillary (NC) image classification. Performance of MD-LBPV on NC images is highest, whereas for second application, HEp-2 cell classification, performance of MD-LBP is highest. It is observed that the proposed texture descriptors gives improved texture classification accuracy.

Plastic filaments are used in a fastly growing industry of 3D printing using the Fused Filament Fabrication (FFF) method. A poor quality of spool winding can negatively impact the printing process. On the other hand, producing high-quality filament winding is surprisingly difficult to achieve consistently. The thesis proposes a holistic approach to inspect winding quality during the winding process. We suggest tracking reflections of bright visible light. This method seems robust enough to track filament color from black to white and even transparent materials. Furthermore, it is possible to run everything on cheap and widely available Raspberry Pi 4 B with Camera Module v2. The system uses classical computer vision approaches for filtering, segmentation, and inter-frame tracking of individual filament strands between video frames. It was confirmed to be fast enough to process 30 FPS footage directly on the Raspberry Pi in real-time. Additionally, the GUI tool for quick dataset annotation of spool winding images was created along with a small dataset. Both might be useful for the future development of a system, which would predict the quality issues earlier when corrective action can still be carried out to prevent them. 1

In many different insect species the highly conserved neuropil regions known as the central complex or central body complex have been shown to be important in behaviours such as locomotion, visual memory and courtship conditioning. The aim of this project is to generate accurate quantitative neuroanatomy of the central complex in the fruit fly Drosophila melanogaster. Much of the authoritative neuroanatomy of the fruit fly from past literature has been derived using Golgi stains, and in important cases these data are available only from 2D camera lucida drawings of the neurons and linguistic descriptions of connectivity. These cannot easily be mapped onto 3D template brains or compared directly to our own data. Using GAL4 driver and reporter constructs, some of the findings within these studies could be visualized using immunohistochemistry and confocal microscopy. A range of GAL4 driver lines were selected that particularly had prominent expression in the fan-shaped body. Images of brains from these lines were archived using a web-based 3D image stack archive developed for the sharing and backup of large confocal stacks. This is also the platform which we use to publish the data, so that other researchers can reuse this catalogue and compare their results directly. Each brain was annotated using desktop-based tools for labelling neuropil regions, locating landmarks in image stacks and tracing fine neuronal processes both manually and automatically. The development of the tracing and landmark annotation tools is described, and all of the tools used in this work are available as free software. In order to compare and aggregate these data, which are from many different brains, it is necessary to register each image stack onto some standard template brain. Although this is a well-studied problem in medical imaging, these high resolution scans of the central fly brain are unusual in a number of respects. The relative effectiveness of various methods currently available were tested on this data set. The best registrations were produced by a method that generates free-form deformations based on B-splines (the Computational Morphometry Toolkit), but for much faster registrations, the thin plate spline method based on manual landmarks may be sufficient. The annotated and registered data allows us to produce central complex template images and also files that accurately represent the possible central complex connectivity apparent in these images. One interesting result to arise from these efforts was evidence for a possible connection between the inferior region of the fan-shaped body and the beta lobe of the mushroom body which had previously been missed in these GAL4 lines. In addition, we can identify several connections which appear to be similar to those described in [Hanesch et al., 1989], the canonical paper on the architecture of the Drosophila melanogaster central complex, and describe for the first time their variation statistically. This registered data was also used to suggest a method for classifying layers of expression within the fan-shaped body.

Thesis (M.S.)--Rochester Institute of Technology, 2005. / Typescript. Includes bibliographical references (p. 91-93).

An AdaBoost algorithm for construction of strong classifier from several weak hypotesis will be presented in this work. Theoretical background of the algorithm and the method of construction of strong classifiers will be explained. WaldBoost extension to the algorithm will be described. The thesis deals with image features that are often used as element of weak classifiers. Brief introduction to pattern recognition in context of computer vision will be outlined in the begining of the work. Also some widely used methods of classifier training will be presented. An object detection library based on AdaBoost classifiers was developed as part of the work. The library was used in implementation of software that in praktice demonstrates object detection in videosquences. Last part of the work describes tool for training of AdaBoost classifiers.

The focus of this research is the extension of topological operators with the addition of a inclusion measure. This extension is carried out in both crisp and fuzzy topological spaces. The mathematical properties of the new operators are discussed and compared with traditional operators. Ignoring small errors due to imperfections and noise in digital images is the main motivation in introducing the proposed operators. To show the effectiveness of the new operators, we demonstrate their utility in image database classification and shape classification. Each image (shape) category is modeled with a topological space and the interior of the query image is obtained with respect to different topologies. This novel way of looking at the image categories and classifying a query image shows some promising results. Moreover, the proposed interior and closure operators with inclusion degree is utilized in mathematical morphology area. The morphological operators with inclusion degree outperform traditional morphology in noise removal and edge detection in a noisy environment

The focus of this research is the extension of topological operators with the addition of a inclusion measure. This extension is carried out in both crisp and fuzzy topological spaces. The mathematical properties of the new operators are discussed and compared with traditional operators. Ignoring small errors due to imperfections and noise in digital images is the main motivation in introducing the proposed operators. To show the effectiveness of the new operators, we demonstrate their utility in image database classification and shape classification. Each image (shape) category is modeled with a topological space and the interior of the query image is obtained with respect to different topologies. This novel way of looking at the image categories and classifying a query image shows some promising results. Moreover, the proposed interior and closure operators with inclusion degree is utilized in mathematical morphology area. The morphological operators with inclusion degree outperform traditional morphology in noise removal and edge detection in a noisy environment

With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. There is no clear definition how to divide the digital image processing, but normally, digital image processing includes three main steps: low-level, mid-level and highlevel processing. Low-level processing involves primitive operations, such as: image preprocessing to reduce the noise, contrast enhancement, and image sharpening. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. Finally, higher-level processing involves "making sense" of an ensemble of recognised objects, as in image analysis. Based on the theory just described in the last paragraph, this thesis is organised in three parts: Colour Edge and Face Detection; Hand motion detection; Hand Gesture Detection and Medical Image Processing. II In Colour Edge Detection, two new images G-image and R-image are built through colour space transform, after that, the two edges extracted from G-image and R-image respectively are combined to obtain the final new edge. In Face Detection, a skin model is built first, then the boundary condition of this skin model can be extracted to cover almost all of the skin pixels. After skin detection, the knowledge about size, size ratio, locations of ears and mouth is used to recognise the face in the skin regions. In Hand Motion Detection, frame differe is compared with an automatically chosen threshold in order to identify the moving object. For some special situations, with slow or smooth object motion, the background modelling and frame differencing are combined in order to improve the performance. In Hand Gesture Recognition, 3 features of every testing image are input to Gaussian Mixture Model (GMM), and then the Expectation Maximization algorithm (EM)is used to compare the GMM from testing images and GMM from training images in order to classify the results. In Medical Image Processing (mammograms), the Artificial Neural Network (ANN) and clustering rule are applied to choose the feature. Two classifier, ANN and Support Vector Machine (SVM), have been applied to classify the results, in this processing, the balance learning theory and optimized decision has been developed are applied to improve the performance.

With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. There is no clear definition how to divide the digital image processing, but normally, digital image processing includes three main steps: low-level, mid-level and highlevel processing. Low-level processing involves primitive operations, such as: image preprocessing to reduce the noise, contrast enhancement, and image sharpening. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. Finally, higher-level processing involves "making sense" of an ensemble of recognised objects, as in image analysis. Based on the theory just described in the last paragraph, this thesis is organised in three parts: Colour Edge and Face Detection; Hand motion detection; Hand Gesture Detection and Medical Image Processing. II In Colour Edge Detection, two new images G-image and R-image are built through colour space transform, after that, the two edges extracted from G-image and R-image respectively are combined to obtain the final new edge. In Face Detection, a skin model is built first, then the boundary condition of this skin model can be extracted to cover almost all of the skin pixels. After skin detection, the knowledge about size, size ratio, locations of ears and mouth is used to recognise the face in the skin regions. In Hand Motion Detection, frame differe is compared with an automatically chosen threshold in order to identify the moving object. For some special situations, with slow or smooth object motion, the background modelling and frame differencing are combined in order to improve the performance. In Hand Gesture Recognition, 3 features of every testing image are input to Gaussian Mixture Model (GMM), and then the Expectation Maximization algorithm (EM)is used to compare the GMM from testing images and GMM from training images in order to classify the results. In Medical Image Processing (mammograms), the Artificial Neural Network (ANN) and clustering rule are applied to choose the feature. Two classifier, ANN and Support Vector Machine (SVM), have been applied to classify the results, in this processing, the balance learning theory and optimized decision has been developed are applied to improve the performance.

L’objectif de cette recherche est la création d’une plateforme en ligne qui permettrait d’examiner les différences individuelles de stratégies de traitement de l’information visuelle dans différentes tâches de catégorisation des visages. Le but d’une telle plateforme est de récolter des données de participants géographiquement dispersés et dont les habiletés en reconnaissance des visages sont variables. En effet, de nombreuses études ont montré qu’il existe de grande variabilité dans le spectre des habiletés à reconnaître les visages, allant de la prosopagnosie développementale (Susilo & Duchaine, 2013), un trouble de reconnaissance des visages en l’absence de lésion cérébrale, aux super-recognizers, des individus dont les habiletés en reconnaissance des visages sont au-dessus de la moyenne (Russell, Duchaine & Nakayama, 2009). Entre ces deux extrêmes, les habiletés en reconnaissance des visages dans la population normale varient. Afin de démontrer la faisabilité de la création d’une telle plateforme pour des individus d’habiletés très variables, nous avons adapté une tâche de reconnaissance de l’identité des visages de célébrités utilisant la méthode Bubbles (Gosselin & Schyns, 2001) et avons recruté 14 sujets contrôles et un sujet présentant une prosopagnosie développementale. Nous avons pu mettre en évidence l’importance des yeux et de la bouche dans l’identification des visages chez les sujets « normaux ». Les meilleurs participants semblent, au contraire, utiliser majoritairement le côté gauche du visage (l’œil gauche et le côté gauche de la bouche). / The present study aims to create a web-based platform that would examine individual differences in face processing strategies in different categorization tasks. The purpose of this platform is to collect data from geographically dispersed participants with variable face recognition abilities. Indeed, many studies have shown that there is high variability in the spectrum of face recognition ability, ranging from developmental prosopagnosia (Duchaine & Susilo, 2013), a disorder of face recognition in the absence of brain damage, to super-recognizers, individuals with extraordinary face recognition ability (Russell, Duchaine & Nakayama, 2009). Between these extremes, people vary substantially in their ability to recognize faces. To demonstrate the reliability of creating such a platform for individuals of widely varying abilities, we adapted a recognition task of the identity of famous faces using the Bubbles method (Gosselin & Schyns, 2001) and recruited 12 control subjects and a subject with developmental prosopagnosia. We were able to highlight the importance of the eyes and the mouth in face identification. The best observers seem mostly to use the left side of the face (left eye and the left side of the mouth).