Neutro-Connectedness Theory, Algorithms and Applications

Connectedness is an important topological property and has been widely studied in digital topology. However, three main challenges exist in applying connectedness to solve real world problems: (1) the definitions of connectedness based on the classic and fuzzy logic cannot model the “hidden factors” that could influence our decision-making; (2) these definitions are too general to be applied to solve complex problem; and (4) many measurements of connectedness are heavily dependent on the shape (spatial distribution of vertices) of the graph and violate the intuitive idea of connectedness. This research focused on solving these challenges by redesigning the connectedness theory, developing fast algorithms for connectedness computation, and applying the newly proposed theory and algorithms to solve challenges in real problems. The newly proposed Neutro-Connectedness (NC) generalizes the conventional definitions of connectedness and can model uncertainty and describe the part and the whole relationship. By applying the dynamic programming strategy, a fast algorithm was proposed to calculate NC for general dataset. It is not just calculating NC map, and the output NC forest can discover a dataset’s topological structure regarding connectedness. In the first application, interactive image segmentation, two approaches were proposed to solve the two most difficult challenges: user interaction-dependence and intense interaction. The first approach, named NC-Cut, models global topologic property among image regions and reduces the dependence of segmentation performance on the appearance models generated by user interactions. It is less sensitive to the initial region of interest (ROI) than four state-of-the-art ROI-based methods. The second approach, named EISeg, provides user with visual clues to guide the interacting process based on NC. It reduces user interaction greatly by guiding user to where interacting can produce the best segmentation results. In the second application, NC was utilized to solve the challenge of weak boundary problem in breast ultrasound image segmentation. The approach can model the indeterminacy resulted from weak boundaries better than fuzzy connectedness, and achieved more accurate and robust result on our dataset with 131 breast tumor cases

A discrete graph Laplacian for signal processing

In this thesis we exploit diffusion processes on graphs to effect two fundamental problems of image processing: denoising and segmentation. We treat these two low-level vision problems on the pixel-wise level under a unified framework: a graph embedding. Using this framework opens us up to the possibilities of exploiting recently introduced algorithms from the semi-supervised machine learning literature. We contribute two novel edge-preserving smoothing algorithms to the literature. Furthermore we apply these edge-preserving smoothing algorithms to some computational photography tasks. Many recent computational photography tasks require the decomposition of an image into a smooth base layer containing large scale intensity variations and a residual layer capturing fine details. Edge-preserving smoothing is the main computational mechanism in producing these multi-scale image representations. We, in effect, introduce a new approach to edge-preserving multi-scale image decompositions. Where as prior approaches such as the Bilateral filter and weighted-least squares methods require multiple parameters to tune the response of the filters our method only requires one. This parameter can be interpreted as a scale parameter. We demonstrate the utility of our approach by applying the method to computational photography tasks that utilise multi-scale image decompositions. With minimal modification to these edge-preserving smoothing algorithms we show that we can extend them to produce interactive image segmentation. As a result the operations of segmentation and denoising are conducted under a unified framework. Moreover we discuss how our method is related to region based active contours. We benchmark our proposed interactive segmentation algorithms against those based upon energy-minimisation, specifically graph-cut methods. We demonstrate that we achieve competitive performance

Herramienta de segmentación semiautomática de imágenes mediante visión por computador

El proyecto propone una herramienta de segmentación semiautomàtica de imágenes usando visión por computador. El objectivo es poder segmentar imágenes que contienen gestos realizados con las manos para su posterior interpretación como parte de una aplicación posterior. Para llevar a cabo esta herramienta primeramente se realiza un estudio del estado del arte para poder analitzar las capacidades de las herramientas actuales que ya realizan esta función, tratando de aprovechar lo que éstas nos oferecen. Posteriormente al estudio se explica el desarrollo de la nueva herramienta, que consta de tres fases: preprocesamiento de la imagen, generación semiautomática de marcadores mediante el metodo connected labeled components, y la utilización del algoritmo Watershed que finalmente se encarga de hacer la segmentación final. Los resultados preliminares mostrados en este documento indican un gran potencial de la técnica desarrollada, ofreciendo resultados prometedores de segmentación sin requerir del usuario final más que marcar un punto en la imagen.This project proposes a semi-automatic computer vision tool for segmenting images. The final objective is to segment images which contains persons performing a series of gestures; our methodology aims at segmenting both hands and the head as part of a bigger pipeline which would include the recognition of the actions performed. To develop our tool we have first carried out a research on the State-of-the-art on semi-automatic segmentation in order to explore the capabilities of existint approaches in order to obtain knowledge to create our tool. After this, we propose our segmentation methodology which consists of three stages: image preprocessing, semi-automatic foreground marker generation using connected labeled components and finally, the use of Watersheds to obtain the final segmentation. Preliminary results show the potential of our tool to be used for segmenting the proposed structures; our method is able to obtain promising segmentation results with minimal user interaction.El projecte proposa una eina de segmentació semiautomàtica d'imatges utilitzant visió per computador. L'objectiu es pode segmentar imatges que contenen gestos realitzats amb les mans per a la seva posterior interpretació com a parte d'una aplicació posterior. Per portar a terme aquesta eina primerament es realitza un estudi del estat de l'art per a poder analitzar les capacitats de les eines actuals que ja realitzen aquesta funció, tactant d'aprofitar el que aquestes ofereixen. Posteriorment a l'estudi s'explica el desenvolupament de la nova eina, que consta de tres fase: preprocesament de l'imatge, generació semiautomàtica de marcadors mitjançant el mètode connected labeled components, i l'utilització de l'algoritme Watershed que finalment s'encarrega de fer la segmentació final. Els resultats preliminars mostrats en aquest document indiquen un gran potencial de la tècnica desenvolupada, oferint resultats prometedors de segmentació sense requerir de l'usuari final més que per a marcar un punt en la imatge

Interactive Learning for the Analysis of Biomedical and Industrial Imagery

In der vorliegenden Dissertation werden Methoden des überwachten Lernens untersucht und auf die Analyse und die Segmentierung digitaler Bilddaten angewendet, die aus diversen Forschungsgebieten stammen. Die Segmentierung und die Klassifikation spielen eine wichtige Rolle in der biomedizinischen und industriellen Bildverarbeitung, häufig basiert darauf weitere Erkennung und Quantifikation. Viele problemspezifische Ansätze existieren für die unterschiedlichsten Fragestellungen und nutzen meist spezifisches Vorwissen aus den jeweiligen Bilddaten aus. In dieser Arbeit wird ein überwachtes Lernverfahren vorgestellt, das mehrere Objekte und deren Klassen gleichzeitig segmentieren und unterscheiden kann. Die Methode ist generell genug um einen wichtigen Bereich von Anwendungen abzudecken, für deren Lösung lokale Merkmale eine Rolle spielen. Segmentierungsergebnisse dieses Ansatzes werden auf verschiedenen Datensätzen mit unterschiedlichen Problemstellungen gezeigt. Die Resultate unterstreichen die Anwendbarkeit der Lernmethode für viele biomedizinische und industrielle Anwendungen, ohne dass explizite Kenntnisse der Bildverarbeitung und Programmierung vorausgesetzt werden müssen. Der Ansatz basiert auf generellen Merkmalsklassen, die es erlauben lokal Strukturen wie Farbe, Textur und Kanten zu beschreiben. Zu diesem Zweck wurde eine interaktive Software implementiert, welche, für gewöhnliche Bildgrößen, in Echtzeit arbeitet und es somit einem Domänenexperten erlaubt Segmentierungs- und Klassifikationsaufgaben interaktiv zu bearbeiten. Dafür sind keine Kenntnisse in der Bildverarbeitung nötig, da sich die Benutzerinteraktion auf intuitives Markieren mit einem Pinselwerkzeug beschränkt. Das interaktiv trainierte System kann dann ohne weitere Benutzerinteraktion auf viele neue Bilder angewendet werden. Der Ansatz ist auf Segmentierungsprobleme beschränkt, für deren Lösung lokale diskriminative Merkmale ausreichen. Innerhalb dieser Einschränkung zeigt der Algorithmus jedoch erstaunlich gute Resultate, die in einer applikationsspezifischen Prozedur weiter verbessert werden können. Das Verfahren unterstützt bis zu vierdimensionale, multispektrale Bilddaten in vereinheitlichter Weise. Um die Anwendbar- und Übertragbarkeit der Methode weiter zu illustrieren wurden mehrere echte Anwendungsfälle, kommend aus verschiedenen bildgebenden Bereichen, untersucht. Darunter sind u. A. die Segmentierung von Tumorgewebe, aufgenommen mittelsWeitfeldmikroskopie, die Quantifikation von Zellwanderungen in konfokalmikroskopischen Aufnahmen für die Untersuchung der adulten Neurogenese, die Segmentierung von Blutgefäßen in der Retina des Auges, das Verfolgen von Kupferdrähten in einer Anwendung zur Produktauthentifikation und die Qualitätskontrolle von Mikroskopiebildern im Kontext von Hochdurchsatz-Experimenten. Desweiteren wurde eine neue Klassifikationsmethode basierend auf globalen Frequenzschätzungen für die Prozesskontrolle des Papieranlegers an Druckmaschinen entwickelt

Model based 3D vision synthesis and analysis for production audit of installations.

PhDOne of the challenging problems in the aerospace industry is to design an automated 3D vision system that can sense the installation components in an assembly environment and check certain safety constraints are duly respected. This thesis describes a concept application to aid a safety engineer to perform an audit of a production aircraft against safety driven installation requirements such as segregation, proximity, orientation and trajectory. The capability is achieved using the following steps. The initial step is to perform image capture of a product and measurement of distance between datum points within the product with/without reference to a planar surface. This provides the safety engineer a means to perform measurements on a set of captured images of the equipment they are interested in. The next step is to reconstruct the digital model of fabricated product by using multiple captured images to reposition parts according to the actual model. Then, the projection onto the 3D digital reconstruction of the safety related installation constraints, respecting the original intent of the constraints that are defined in the digital mock up is done. The differences between the 3D reconstruction of the actual product and the design time digital mockup of the product are identified. Finally, the differences/non conformances that have a relevance to safety driven installation requirements with reference to the original safety requirement intent are identified. The above steps together give the safety engineer the ability to overlay a digital reconstruction that should be as true to the fabricated product as possible so that they can see how the product conforms or doesn't conform to the safety driven installation requirements. The work has produced a concept demonstrator that will be further developed in future work to address accuracy, work flow and process efficiency. A new depth based segmentation technique GrabcutD which is an improvement to existing Grabcut, a graph cut based segmentation method is proposed. Conventional Grabcut relies only on color information to achieve segmentation. However, in stereo or multiview analysis, there is additional information that could be also used to improve segmentation. Clearly, depth based approaches bear the potential discriminative power of ascertaining whether the object is nearer of farer. We show the usefulness of the approach when stereo information is available and evaluate it using standard datasets against state of the art result

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes