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Fabric appearance is usually the highest priority consideration for consumers. Pilling and wrinkling are two major factors which cause the fabric to have a worse appearance after a certain service period. In order to prevent more piling and wrinkling, a fabric pilling and wrinkling severity evaluation is very important. Traditional visual examination needs at least three trained experts to judge each sample, which is both subjective and time-consuming. Objective, high efficiency, and automatic pilling and wrinkling evaluation based on computer processing techniques are now being developed quickly. In this study, an integrated fabric pilling and wrinkling measurement system based on stereovision was developed. The hardware part of the system consists of a pair of consumer high resolution cameras and a mounting stage, which is affordable and portable in comparison with other 3D imaging systems. A novel pilling detection algorithm focusing on 3D image local information was proposed to extract three pilling features including pilling density, pilling average height, and pilling average size. The logistic regression classifier was applied for pilling severity classification because it showed a good accuracy with 80% on the 120 3D pilling images. A fast wrinkle detection algorithm with leveled 3D fabric surface was developed to measure wrinkle density, hardness, tip-angle, and roughness. According to these four wrinkling features, 180 3D wrinkling images were tested by the logistic regression classifier with an overall 74.4% accuracy in comparison with visual judging results. Both pilling and wrinkling results obtained from the proposed automatic 3D fabric pilling and wrinkling severity evaluation system were consistent with the subjective visual evaluation results. The system is ready for practical use. / text

Optical flow, caused by relative motion of the object and the viewer, is the distribution of apparent velocities of brightness pattern in an image. The advantage of the optical-flow-based visual servo method is that feature of the object does not need to be defined or known in advance. This research plans to build an image servo technique to deal with the problem of 3D relative motion of the viewer and the environment. The images are treated as input and output signals of the control system and are fed back to extract the relative velocity information between contiguous image patterns. Then the video camera will automatically follow the motion to maintain the target image unchanged.

This thesis discusses certain aspects of the noninvasive imaging technique known as optical coherence tomography (OCT). Topics include three-dimensional image rendering as well as application of the Fast Fourier Transform to reconstruct the axial scan as a function of depth. Implementations use LabVIEW system design software and a Xilinx Spartan-6 field-programmable gate array (FPGA). The inherent parallel-processing capability of an FPGA opens the possibility of designing a "super-sensor" which entails simultaneous capturing of image and sensor data, giving medical practitioners more data for potentially improved diagnosis. FPGA-based processing would benefit many methods of characterizing biological samples; OCT and photonic crystal microarray biosensors are discussed. / text

Today’s 3D imaging techniques offer significant benefits over conventional 2D imaging techniques. The presence of natural depth information in the scene affords the observer an overall improved sense of reality and naturalness. A variety of systems attempting to reach this goal have been designed by many independent research groups, such as stereoscopic and auto-stereoscopic systems. Though the images displayed by such systems tend to cause eye strain, fatigue and headaches after prolonged viewing as users are required to focus on the screen plane/accommodation to converge their eyes to a point in space in a different plane/convergence. Holoscopy is a 3D technology that targets overcoming the above limitations of current 3D technology and was recently developed at Brunel University. This work is part W4.1 of the 3D VIVANT project that is funded by the EU under the ICT program and coordinated by Dr. Aman Aggoun at Brunel University, West London, UK. The objective of the work described in this thesis is to develop estimation and segmentation techniques that are capable of estimating precise 3D depth, and are applicable for holoscopic 3D imaging system. Particular emphasis is given to the task of automatic techniques i.e. favours algorithms with broad generalisation abilities, as no constraints are placed on the setting. Algorithms that provide invariance to most appearance based variation of objects in the scene (e.g. viewpoint changes, deformable objects, presence of noise and changes in lighting). Moreover, have the ability to estimate depth information from both types of holoscopic 3D images i.e. Unidirectional and Omni-directional which gives horizontal parallax and full parallax (vertical and horizontal), respectively. The main aim of this research is to develop 3D depth estimation and 3D image segmentation techniques with great precision. In particular, emphasis on automation of thresholding techniques and cues identifications for development of robust algorithms. A method for depth-through-disparity feature analysis has been built based on the existing correlation between the pixels at a one micro-lens pitch which has been exploited to extract the viewpoint images (VPIs). The corresponding displacement among the VPIs has been exploited to estimate the depth information map via setting and extracting reliable sets of local features. ii Feature-based-point and feature-based-edge are two novel automatic thresholding techniques for detecting and extracting features that have been used in this approach. These techniques offer a solution to the problem of setting and extracting reliable features automatically to improve the performance of the depth estimation related to the generalizations, speed and quality. Due to the resolution limitation of the extracted VPIs, obtaining an accurate 3D depth map is challenging. Therefore, sub-pixel shift and integration is a novel interpolation technique that has been used in this approach to generate super-resolution VPIs. By shift and integration of a set of up-sampled low resolution VPIs, the new information contained in each viewpoint is exploited to obtain a super resolution VPI. This produces a high resolution perspective VPI with wide Field Of View (FOV). This means that the holoscopic 3D image system can be converted into a multi-view 3D image pixel format. Both depth accuracy and a fast execution time have been achieved that improved the 3D depth map. For a 3D object to be recognized the related foreground regions and depth information map needs to be identified. Two novel unsupervised segmentation methods that generate interactive depth maps from single viewpoint segmentation were developed. Both techniques offer new improvements over the existing methods due to their simple use and being fully automatic; therefore, producing the 3D depth interactive map without human interaction. The final contribution is a performance evaluation, to provide an equitable measurement for the extent of the success of the proposed techniques for foreground object segmentation, 3D depth interactive map creation and the generation of 2D super-resolution viewpoint techniques. The no-reference image quality assessment metrics and their correlation with the human perception of quality are used with the help of human participants in a subjective manner.

Holoscopic 3D imaging also known as “Integral imaging” was first proposed by Lippmann in 1908. It facilitates a promising technique for creating full colour spatial image that exists in space. It promotes a single lens aperture for recording spatial images of a real scene, thus it offers omnidirectional motion parallax and true 3D depth, which is the fundamental feature for digital refocusing. While stereoscopic and multiview 3D imaging systems simulate human eye technique, holoscopic 3D imaging system mimics fly’s eye technique, in which viewpoints are orthographic projection. This system enables true 3D representation of a real scene in space, thus it offers richer spatial cues compared to stereoscopic 3D and multiview 3D systems. Focus has been the greatest challenge since the beginning of photography. It is becoming even more critical in film production where focus pullers are finding it difficult to get the right focus with camera resolution becoming increasingly higher. Holoscopic 3D imaging enables the user to carry out re/focusing in post-production. There have been three main types of digital refocusing methods namely Shift and Integration, full resolution, and full resolution with blind. However, these methods suffer from artifacts and unsatisfactory resolution in the final resulting image. For instance the artifacts are in the form of blocky and blurry pictures, due to unmatched boundaries. An upsampling method is proposed that improves the resolution of the resulting image of shift and integration approach. Sub-pixel adjustment of elemental images including “upsampling technique” with smart filters are proposed to reduce the artifacts, introduced by full resolution with blind method as well as to improve both image quality and resolution of the final rendered image. A novel 3D object extraction method is proposed that takes advantage of disparity, which is also applied to generate stereoscopic 3D images from holoscopic 3D image. Cross correlation matching algorithm is used to obtain the disparity map from the disparity information and the desirable object is then extracted. In addition, 3D image conversion algorithm is proposed for the generation of stereoscopic and multiview 3D images from both unidirectional and omnidirectional holoscopic 3D images, which facilitates 3D content reformation.

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.

A formação de arcos – estruturas que promovem a anisotropia de forças dentro de um sistema – acontece corriqueiramente dentro de silos ou maquinaria agrícola. A presente tese propõe um modelo baseado em redes complexas para modelar tal fenômeno, definindo cada grão como vértice e a força que dois grãos trocam como o peso de uma ligação entre eles. A partir de ensaios tomográficos de 11 diferentes tipos de grãos foi desenvolvido um método para transformar cada uma das imagens tridimensionais resultantes numa rede complexa. Cada imagem foi pré-processada e submetida a uma transformada watershed utilizando como marcadores internos a erosão da própria imagem. Este processo tridimensional resultou na segmentação de cada um dos grãos da imagem original, tornando possível a extração de propriedades físicas de cada grão, como massa, centro de massa, momento de inércia, e as forças às quais este está submetido. A partir destes dados, a rede complexa de cada uma das 11 amostras foi construída. A amostra da soja foi comparada com um padrão-ouro pré-estabelecido possibilitando eventuais refinos no método. As reconstruções tridimensionais segmentadas de cada amostra apresentaram um resultado visual aceitável, embora algumas segmentações tenham sofrido com o efeito do elemento estruturante da erosão, uma vez que este tem de ser grande o suficiente para segmentar grãos adjacentes, porém não o suficiente para super-segmentar um único grão. A rede complexa formada a partir da imagem de soja foi submetida a uma análise mais profunda, estudando e normalizando sua propriedade strength, uma natural candidata para detectar anisotropia de forças. Os vértices com alto valor normalizado de strength foram definidos como o arco da estrutura, e sua análise visual permitiu concluir que estes de fato são os elementos responsáveis pela estrutura do arranjo, assim como substanciar o sucesso do método aqui proposto em detectar automaticamente o arco utilizando uma imagem tridimensional. / The formation of arches – structures that promotes force anisotropy within a system – appears routinely inside silos or agricultural machinery. This current thesis proposes a method for modeling this phenomenon as a complex network, defining each grain as vertex and a force that two grains exchanges as the weight of the link between them. By using computed tomography, 3D images were taken from 11 grain samples, and a method developed to transform each of this resulting images in a complex network. Each image had to be pre-processed and subjected to a watershed transform using as inner markers the erosion of the image itself. This process resulted in three-dimensional segmentation of each grain of the original image, allowing the estimation of the physical properties of each grain, such as mass, center of mass, moment of inertia and the forces to which the grain is subjected. From these measures, the complex network of each of the 11 samples was constructed. Sample soybeans were compared with a gold-standard, allowing improvements to the methodology. The segmented three-dimensional reconstructions of each sample provided acceptable visual output, although some samples suffered from erosion due to the structural element size, since it must be large enough to segment adjacent grains, but not enough to super-segment a single grain. The complex network obtained from the soybeans image was subjected to further analysis, studying and normalizing its strength property, a natural candidate to detect force anisotropy. Vertices with high normalized values of strength were understood as defining the arch of the structure, and its visual analysis showed that these indeed are the elements responsible for the arrangement structure. These results support the ability of the proposed method in automatically detecting the arches using as input a three-dimensional image.

<p>Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated.</p><p>The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques.</p><p>The results of this work are;</p><p>1. a parameter setting tool that makes the initial parameter setting much easier and</p><p>2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.</p>

This thesis presents a novel three dimensional (3D) image-analysis method for characterizing the physical characteristics of coarse particles in the field, and introduces new methodology for the total analysis of glacial till samples. The novel image analysis method, called the GID method, is capable of determining the size, shape and surface texture of each individual clast analysed. Images of particles are taken in the field and analysis is done in the laboratory. Therefore the GID method makes it feasible to analyse statistically representative large sample in short period; for poorly sorted sediments, such as till, one-tonne is required if the analysis includes cobble size. The capability of the GID method was demonstrated by studying coarse clasts (20-200 mm) from till. There is excellent agreement in the results of the size distribution obtained from the GID method and sieve analysis. The GID method results for size and shape parameters show high and very high repeatability. The particle angularity in the GID method has not been measured to acceptable level; the repeatability test shows some variability. The new methodology for total analysis of till applied the GID method at four different locations in Sweden. The total analysis included 3D size and shape distribution of coarse particles coupled to electrical resistivity, lithological distribution and magnetic susceptibility of the clasts. The results show clear difference in the till samples from the different sites. / <p>QC 20120828</p>