Determining the Number of Batik Motif Object based on Hierarchical Symmetry Detection Approach

In certain conditions, symmetry can be used to describe objects in the batik motif efficiently. Symmetry can be defined based on three linear transformations of dimension n in Euclidian space in the form of translation and rotation. This concept is useful for detecting objects and recognising batik motifs. In this study, we conducted a study of the symmetry effect to determine the number of batik motif objects in an image using symmetry algorithm through a hierarchical approach. The process focuses on determining the intersection line of the batik motif object. Furthermore, by utilising intersection line information for bilateral and rotational symmetry, the number of objects carried out recursively is determined. The results obtained are numbers of batik motif objects through symmetry detection. This information will be used as a reference for batik motif detection. Based on the experimental results, there are some errors caused by the axis of the symmetry line that is not appropriate due to the characteristics of batik motifs. The problem is solved by adding several rules to detect symmetry line and to determine the number of objects. The additional rules increase the average accuracy of the number of object detection from 66.21% to 86.19% (19.99% increase)

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

Assessing the existence of visual clues of human ovulation

Is the concealed human ovulation a myth? The author of this work tries to answer the above question by using a medium-size database of facial images specially created and tagged. Analyzing possible facial modifications during the mensal period is a formal tool to assess the veracity about the concealed ovulation. In normal view, the human ovulation remains concealed. In other words, there is no visible external sign of the mensal period in humans. These external signs are very much visible in many animals such as baboons, dogs or elephants. Some are visual (baboons) and others are biochemical (dogs). Insects use pheromones and other animals can use sounds to inform the partners of their fertility period. The objective is not just to study the visual female ovulation signs but also to understand and explain automatic image processing methods which could be used to extract precise landmarks from the facial pictures. This could later be applied to the studies about the fluctuant asymmetry. The field of fluctuant asymmetry is a growing field in evolutionary biology but cannot be easily developed because of the necessary time to manually extract the landmarks. In this work we have tried to see if any perceptible sign is present in human face during the ovulation and how we can detect formal changes, if any, in face appearance during the mensal period. We have taken photography from 50 girls for 32 days. Each day we took many photos of each girl. At the end we chose a set of 30 photos per girl representing the whole mensal cycle. From these photos 600 were chosen to be manually tagged for verification issues. The photos were organized in a rating software to allow human raters to watch and choose the two best looking pictures for each girl. These results were then checked to highlight the relation between chosen photos and ovulation period in the cycle. Results were indicating that in fact there are some clues in the face of human which could eventually give a hint about their ovulation. Later, different automatic landmark detection methods were applied to the pictures to highlight possible modifications in the face during the period. Although the precision of the tested methods, are far from being perfect, the comparison of these measurements to the state of art indexes of beauty shows a slight modification of the face towards a prettier face during the ovulation. The automatic methods tested were Active Appearance Model (AAM), the neural deep learning and the regression trees. It was observed that for this kind of applications the best method was the regression trees. Future work has to be conducted to firmly confirm these data, number of human raters should be augmented, and a proper learning data base should be developed to allow a learning process specific to this problematic. We also think that low level image processing will be necessary to achieve the final precision which could reveal more details of possible changes in human faces.A ovulação no ser humano é, em geral, considerada “oculta”, ou seja, sem sinais exteriores. Mas a ovulação ou o período mensal é uma mudança hormonal extremamente importante que se repete em cada ciclo. Acreditar que esta mudança hormonal não tem nenhum sinal visível parece simplista. Estes sinais externos são muito visíveis em animais, como babuínos, cães ou elefantes. Alguns são visuais (babuínos) e outros são bioquímicos (cães). Insetos usam feromonas e outros animais podem usar sons para informar os parceiros do seu período de fertilidade. O ser humano tem vindo a esconder ou pelo menos camuflar sinais desses durante a evolução. As razoes para esconder ou camuflar a ovulação no ser humano não são claros e não serão discutidos nesta dissertação. Na primeira parte deste trabalho, a autora deste trabalho, depois de criar um base de dados de tamanho médio de imagens faciais e anotar as fotografias vai verificar se sinais de ovulação podem ser detetados por outros pessoas. Ou seja, se modificações que ‘as priori’ são invisíveis podem ser percebidas de maneira inconsciente pelo observador. Na segunda parte, a autora vai analisar as eventuais modificações faciais durante o período, de uma maneira formal, utilizando medidas faciais. Métodos automáticos de analise de imagem aplicados permitem obter os dados necessários. Uma base de dados de imagens para efetuar este trabalho foi criado de raiz, uma vez que nenhuma base de dados existia na literatura. 50 raparigas aceitaram de participar na criação do base de dados. Durante 32 dias e diariamente, cada rapariga foi fotografada. Em cada sessão foi tirada várias fotos. As fotos foram depois apuradas para deixar só 30 fotos ao máximo, para cada rapariga. 600 fotos foram depois escolhidas para serem manualmente anotadas. Essas 600 fotos anotadas, definam a base de dados de verificação. Assim as medidas obtidas automaticamente podem ser verificadas comparando com a base de 600 fotos anotadas. O objetivo deste trabalho não é apenas estudar os sinais visuais da ovulação feminina, mas também testar e explicar métodos de processamento automático de imagens que poderiam ser usados para extrair pontos de interesse, das imagens faciais. A automatização de extração dos pontos de interesse poderia mais tarde ser aplicado aos estudos sobre a assimetria flutuante. O campo da assimetria flutuante é um campo crescente na biologia evolucionária, mas não pode ser desenvolvido facilmente. O tempo necessário para extrair referencias e pontos de interesse é proibitivo. Por além disso, estudos de assimetria flutuante, muitas vezes, baseado numa só fotografia pode vier a ser invalido, se modificações faciais temporárias existirem. Modificações temporárias, tipo durante o período mensal, revela que estudos fenotípicos baseados numa só fotografia não pode constituir uma base viável para estabelecer ligas genótipo-fenótipo. Para tentar ver se algum sinal percetível está presente no rosto humano durante a ovulação, as fotos foram organizadas num software de presentação para permitir o observador humano escolher duas fotos (as mais atraentes) de cada rapariga. Estes resultados foram então analisados para destacar a relação entre as fotos escolhidas e o período de ovulação no ciclo mensal. Os resultados sugeriam que, de facto, existem algumas indicações no rosto que poderiam eventualmente dar informações sobre o período de ovulação. Os observadores escolheram como mais atraente de cada rapariga, aquelas que tinham sido tiradas nos dias imediatos antes ou depois da ovulação. Ou seja, foi claramente estabelecido que a mesma rapariga parecia mais atraente durante os dias próximos da data da ovulação. O software também permite recolher dados sobre o observador para analise posterior de comportamento dos observadores perante as fotografias. Os dados dos observadores podem dar indicações sobre as razoes da ovulação escondida que foi desenvolvida durante a evolução. A seguir, diferentes métodos automáticos de deteção de pontos de interesse foram aplicados às imagens para detetar o tipo de modificações no rosto durante o período. A precisão dos métodos testados, apesar de não ser perfeita, permite observar algumas relações entre as modificações e os índices de atratividade. Os métodos automáticos testados foram Active Appearance Model (AAM), Convolutional Neural Networks (CNN) e árvores de regressão (Dlib-Rt). AAM e CNN foram implementados em Python utilizando o modulo Keras library. Dlib-Rt foi implementado em C++ utilizando OpenCv. Os métodos utilizados, estão todos baseados em aprendizagem e sacrificam a precisão. Comparando os resultados dos métodos automáticos com os resultados manualmente obtidos, indicaram que os métodos baseados em aprendizagem podem não ter a precisão necessária para estudos em simetria flutuante ou para estudos de modificação faciais finas. Apesar de falta de precisão, observou-se que, para este tipo de aplicação, o melhor método (entre os testados) foi as árvores de regressão. Os dados e medidas obtidas, constituíram uma base de dados com a data de período, medidas faciais, dados sociais e dados de atratividade que poderem ser utilizados para trabalhos posteriores. O trabalho futuro tem de ser conduzido para confirmar firmemente estes dados, o número de avaliadores humanos deve ser aumentado, e uma base de dados de aprendizagem adequada deve ser desenvolvida para permitir a definição de um processo de aprendizagem específico para esta problemática. Também foi observado que o processamento de imagens de baixo nível será necessário para alcançar a precisão final que poderia revelar detalhes finos de mudanças em rostos humanos. Transcrever os dados e medidas para o índice de atratividade e aplicar métodos de data-mining pode revelar exatamente quais são as modificações implicadas durante o período mensal. A autora também prevê a utilização de uma câmara fotográfica tipo true-depth permite obter os dados de profundidade e volumo que podem afinar os estudos. Os dados de pigmentação da pele e textura da mesma também devem ser considerados para obter e observar todos tipos de modificação facial durante o período mensal. Os dados também devem separar raparigas com métodos químicos de contraceção, uma vez que estes métodos podem interferir com os níveis hormonais e introduzir erros de apreciação. Por fim o mesmo estudo poderia ser efetuado nos homens, uma vez que homens não sofrem de mudanças hormonais, a aparição de qualquer modificação facial repetível pode indicar existência de fatos camuflados

Of assembling small sculptures and disassembling large geometry

This thesis describes the research results and contributions that have been achieved during the author’s doctoral work. It is divided into two independent parts, each of which is devoted to a particular research aspect. The first part covers the true-to-detail creation of digital pieces of art, so-called relief sculptures, from given 3D models. The main goal is to limit the depth of the contained objects with respect to a certain perspective without compromising the initial three-dimensional impression. Here, the preservation of significant features and especially their sharpness is crucial. Therefore, it is necessary to overemphasize fine surface details to ensure their perceptibility in the more complanate relief. Our developments are aimed at amending the flexibility and user-friendliness during the generation process. The main focus is on providing real-time solutions with intuitive usability that make it possible to create precise, lifelike and aesthetic results. These goals are reached by a GPU implementation, the use of efficient filtering techniques, and the replacement of user defined parameters by adaptive values. Our methods are capable of processing dynamic scenes and allow the generation of seamless artistic reliefs which can be composed of multiple elements. The second part addresses the analysis of repetitive structures, so-called symmetries, within very large data sets. The automatic recognition of components and their patterns is a complex correspondence problem which has numerous applications ranging from information visualization over compression to automatic scene understanding. Recent algorithms reach their limits with a growing amount of data, since their runtimes rise quadratically. Our aim is to make even massive data sets manageable. Therefore, it is necessary to abstract features and to develop a suitable, low-dimensional descriptor which ensures an efficient, robust, and purposive search. A simple inspection of the proximity within the descriptor space helps to significantly reduce the number of necessary pairwise comparisons. Our method scales quasi-linearly and allows a rapid analysis of data sets which could not be handled by prior approaches because of their size.Die vorgelegte Arbeit beschreibt die wissenschaftlichen Ergebnisse und Beiträge, die während der vergangenen Promotionsphase entstanden sind. Sie gliedert sich in zwei voneinander unabhängige Teile, von denen jeder einem eigenen Forschungsschwerpunkt gewidmet ist. Der erste Teil beschäftigt sich mit der detailgetreuen Erzeugung digitaler Kunstwerke, sogenannter Reliefplastiken, aus gegebenen 3D-Modellen. Das Ziel ist es, die Objekte, abhängig von der Perspektive, stark in ihrer Tiefe zu limitieren, ohne dass der Eindruck der räumlichen Ausdehnung verloren geht. Hierbei kommt dem Aufrechterhalten der Schärfe signifikanter Merkmale besondere Bedeutung zu. Dafür ist es notwendig, die feinen Details der Objektoberfläche überzubetonen, um ihre Sichtbarkeit im flacheren Relief zu gewährleisten. Unsere Weiterentwicklungen zielen auf die Verbesserung der Flexibilität und Benutzerfreundlichkeit während des Enstehungsprozesses ab. Der Fokus liegt dabei auf dem Bereitstellen intuitiv bedienbarer Echtzeitlösungen, die die Erzeugung präziser, naturgetreuer und visuell ansprechender Resultate ermöglichen. Diese Ziele werden durch eine GPU-Implementierung, den Einsatz effizienter Filtertechniken sowie das Ersetzen benutzergesteuerter Parameter durch adaptive Werte erreicht. Unsere Methoden erlauben das Verarbeiten dynamischer Szenen und die Erstellung nahtloser, kunstvoller Reliefs, die aus mehreren Elementen und Perspektiven zusammengesetzt sein können. Der zweite Teil behandelt die Analyse wiederkehrender Stukturen, sogenannter Symmetrien, innerhalb sehr großer Datensätze. Das automatische Erkennen von Komponenten und deren Muster ist ein komplexes Korrespondenzproblem mit zahlreichen Anwendungen, von der Informationsvisualisierung über Kompression bis hin zum automatischen Verstehen. Mit zunehmender Datenmenge geraten die etablierten Algorithmen an ihre Grenzen, da ihre Laufzeiten quadratisch ansteigen. Unser Ziel ist es, auch massive Datensätze handhabbar zu machen. Dazu ist es notwendig, Merkmale zu abstrahieren und einen passenden niedrigdimensionalen Deskriptor zu entwickeln, der eine effiziente, robuste und zielführende Suche erlaubt. Eine simple Betrachtung der Nachbarschaft innerhalb der Deskriptoren hilft dabei, die Anzahl notwendiger paarweiser Vergleiche signifikant zu reduzieren. Unser Verfahren skaliert quasi-linear und ermöglicht somit eine rasche Auswertung auch auf Daten, die für bisherige Methoden zu groß waren

Fine-Scaled 3D Geometry Recovery from Single RGB Images

3D geometry recovery from single RGB images is a highly ill-posed and inherently ambiguous problem, which has been a challenging research topic in computer vision for several decades. When fine-scaled 3D geometry is required, the problem become even more difficult. 3D geometry recovery from single images has the objective of recovering geometric information from a single photograph of an object or a scene with multiple objects. The geometric information that is to be retrieved can be of different representations such as surface meshes, voxels, depth maps or 3D primitives, etc. In this thesis, we investigate fine-scaled 3D geometry recovery from single RGB images for three categories: facial wrinkles, indoor scenes and man-made objects. Since each category has its own particular features, styles and also variations in representation, we propose different strategies to handle different 3D geometry estimates respectively. We present a lightweight non-parametric method to generate wrinkles from monocular Kinect RGB images. The key lightweight feature of the method is that it can generate plausible wrinkles using exemplars from one high quality 3D face model with textures. The local geometric patches from the source could be copied to synthesize different wrinkles on the blendshapes of specific users in an offline stage. During online tracking, facial animations with high quality wrinkle details can be recovered in real-time as a linear combination of these personalized wrinkled blendshapes. We propose a fast-to-train two-streamed CNN with multi-scales, which predicts both dense depth map and depth gradient for single indoor scene images.The depth and depth gradient are then fused together into a more accurate and detailed depth map. We introduce a novel set loss over multiple related images. By regularizing the estimation between a common set of images, the network is less prone to overfitting and achieves better accuracy than competing methods. Fine-scaled 3D point cloud could be produced by re-projection to 3D using the known camera parameters. To handle highly structured man-made objects, we introduce a novel neural network architecture for 3D shape recovering from a single image. We develop a convolutional encoder to map a given image to a compact code. Then an associated recursive decoder maps this code back to a full hierarchy, resulting a set of bounding boxes to represent the estimated shape. Finally, we train a second network to predict the fine-scaled geometry in each bounding box at voxel level. The per-box volumes are then embedded into a global one, and from which we reconstruct the final meshed model. Experiments on a variety of datasets show that our approaches can estimate fine-scaled geometry from single RGB images for each category successfully, and surpass state-of-the-art performance in recovering faithful 3D local details with high resolution mesh surface or point cloud