A new thresholding approach for automatic generation of polygonal approximations

The present paper proposes a new algorithm for automatic generation of polygonal approximations of 2D closed contours based on a new thresholding method. The new proposal computes the signi cance level of the contour points using a new symmetric version of the well-known Ramer, Douglas - Peucker method, and then a new Adaptive method is applied to threshold the normalized signi cance level of the contour points to generate the polygonal approximation. The experiments have shown that the new algorithm has good performance for generating polygonal approximations of 2D closed contours. Futhermore, the new algorithm does not require any parameter to be tuned

Geometric uncertainty models for correspondence problems in digital image processing

Many recent advances in technology rely heavily on the correct interpretation of an enormous amount of visual information. All available sources of visual data (e.g. cameras in surveillance networks, smartphones, game consoles) must be adequately processed to retrieve the most interesting user information. Therefore, computer vision and image processing techniques gain significant interest at the moment, and will do so in the near future. Most commonly applied image processing algorithms require a reliable solution for correspondence problems. The solution involves, first, the localization of corresponding points -visualizing the same 3D point in the observed scene- in the different images of distinct sources, and second, the computation of consistent geometric transformations relating correspondences on scene objects. This PhD presents a theoretical framework for solving correspondence problems with geometric features (such as points and straight lines) representing rigid objects in image sequences of complex scenes with static and dynamic cameras. The research focuses on localization uncertainty due to errors in feature detection and measurement, and its effect on each step in the solution of a correspondence problem. Whereas most other recent methods apply statistical-based models for spatial localization uncertainty, this work considers a novel geometric approach. Localization uncertainty is modeled as a convex polygonal region in the image space. This model can be efficiently propagated throughout the correspondence finding procedure. It allows for an easy extension toward transformation uncertainty models, and to infer confidence measures to verify the reliability of the outcome in the correspondence framework. Our procedure aims at finding reliable consistent transformations in sets of few and ill-localized features, possibly containing a large fraction of false candidate correspondences. The evaluation of the proposed procedure in practical correspondence problems shows that correct consistent correspondence sets are returned in over 95% of the experiments for small sets of 10-40 features contaminated with up to 400% of false positives and 40% of false negatives. The presented techniques prove to be beneficial in typical image processing applications, such as image registration and rigid object tracking

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Human Metaphase Chromosome Analysis using Image Processing

Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation. Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable of calculating the segmentation outline, the centerline (which gives the chromosome length), partitioning of the telomere regions and the centromere location of a given chromosome. The algorithm also detects and corrects for the sister chromatid separation artifact in metaphase cell images. A metric termed the Candidate Based Centromere Confidence (CBCC) is proposed to accompany each centromere detection result of the proposed method, giving an indication of the confidence the algorithm has on a given localization. The proposed method was first tested for the ability of calculating an accurate width profile against a centerline based method [1] using 226 chromosomes. A statistical analysis of the centromere detection error values proved that the proposed method can accurately locate centromere locations with statistical significance. Furthermore, the proposed method performed more consistently across different staining methods in comparison to the centerline based approach. When tested with a larger data set of 1400 chromosomes collected from a set of DAPI (4\u27,6-diamidino-2-phenylindole) and Giemsa stained cell images, the proposed candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%

Model-Based Environmental Visual Perception for Humanoid Robots

The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling

Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis

[no abstract

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Using an anisotropic diffusion scale-space for the detection and delineation of shacks in informal settlement imagery

PhD, Faculty of Engineering and the Built Environment, University of the Witwatersrand, 2010Informal settlements are a growing world-wide phenomenon. Up-to-date spatial information mapping settlements is essential for a variety of end-user applications from planning settlement upgrading to monitoring expansion and infill. One method of gathering this information is through the analysis of nadir-view aerial imagery and the automated or semi-automated extraction of individual shacks. The problem of shack detection and delineation in, particularly South African, informal settlements is a unique and difficult one. This is primarily due to the inhomogeneous appearance of shack roofs, which are constructed from a variety of disparate materials, and the density of shacks. Previous research has focused mostly on the use of height data in conjunction with optical images to perform automated or semi-automated shack extraction. In this thesis, a novel approach to automating shack extraction is presented and prototyped, in which the appearance of shack roofs is homogenised, facilitating their detection. The main features of this strategy are: construction of an anisotropic scale-space from a single source image and detection of hypotheses at multiple scales; simplification of hypotheses' boundaries through discrete curve evolution and regularisation of boundaries in accordance with an assumed shack model - a 4-6 sided, compact, rectilinear shape; selection of hypotheses competing across scales using fuzzy rules; grouping of hypotheses based on their support for one another, and localisation and re-regularisation of boundaries through the incorporation of image edges. The prototype's performance is evaluated in terms of standard metrics and is analysed for four different images, having three different sets of imaging conditions, and containing well over a hundred shacks. Detection rates in terms of building counts vary from 83% to 100% and, in terms of roof area coverage, from 55% to 84%. These results, each derived from a single source image, compare favourably with those of existing shack detection systems, especially automated ones which make use of richer source data. Integrating this scale-space approach with height data offers the promise of even better results

Development of a holoscopic imaging system and applied high-resolution fluorescence microscopy

Biomedical imaging helps extending our comprehension of ourselves and our environment. Advances in camera, laser and computation technologies have enabled an ever-increasing number of imaging technologies. Imaging with visible and infrared light has the advantage that it is less harmful than other radiation and its wavelength is in the order of magnitude of cells and subcellular components. Fluorescence microscopy provides good chemical contrast and multi-colour imaging can help elucidate cellular architecture. Incoherent superresolution methods permit us to bypass Abbe's diffraction limit of lateral resolution and visualize previously unnoticed details. Coherent imaging methods such as optical coherence tomography or holoscopy do not require any previous labelling and have the advantage that they record both the amplitude and phase of the light emitted from a scattering sample by interferometric superposition with a reference wave. Both incoherent an coherent imaging methods are used in this thesis. The results of two interdisciplinary research collaborations using different fluorescence microscopy methods, including superresolution methods, are presented. Podosomes in macrophages were studied with stimulated emission depletion microscopy, structured illumination microscopy and localisation microscopy and a distinctly polygonal shape in their vinculin rings was found. Image processing routines allowed for a quantitative analysis of the acquired images [1]. In the second study, chlorophyll, the most prominent natural pigments, and digested chlorophyll metabolites were detected in gut section of the herbivorous Spodoptera littoralis larva. Widefield and high-resolution autofluorescence microscopy revealed that the brush border membranes of their gut are covered with the chlorophyllide binding protein tightly bound to the gut membrane. A function in defense against gut microbes is discussed [2]. Optical coherence tomography (OCT) offers a slightly lower spatial resolution than light microscopy but generally better penetration depths. In order to use a higher numerical aperture for detection in OCT, the dilemma of the resulting reduced depth of field has to be overcome. Different extended focus possibilities are explored in this thesis. Bessel illumination is an established method to achieve an extended depth of field without compromising the lateral resolution. When broadband or multicolour imaging is required, wavelength-dependent changes in the radial profile of the Bessel illumination can however complicate further image processing and analysis. A solution for engineering a multicolour Bessel beam was implemented with a phase-only spatial light modulator in the image plane and an iterative Fourier Transformation algorithm [3]. For higher acquisition speed, full-field recording is favourable to scanning the scattering sample with a Bessel beam. OCT can be combined with reconstruction methods from digital holography to achieve an extended focus numerically. A suitable experimental imaging setup and a custom-written reconstruction algorithm are presented. [1] M. Walde, J. Monypenny, R. Heintzmann, G. E. Jones, and S. Cox, “Vinculin binding angle in podosomes revealed by high resolution microscopy”, PLoS ONE, vol. 9, no. 2, 2014. [2] A. Badgaa, R. Büchler, N. Wielsch, M. Walde, R. Heintzmann, Y. Pauchet, A. Svatos, K. Ploss, and W. Boland, “The Green Gut: Chlorophyll Degradation in the Gut of Spodoptera littoralis”, Journal of Chemical Ecology, vol. 41, no. 11, pp. 965-974, 2015. [3] M. Walde, A. Jost, K. Wicker, and R. Heintzmann, “Engineering an achromatic Bessel beam using a phase-only spatial light modulator and an iterative Fourier transformation algorithm”, Optics Communications, vol. 383, pp. 64-68, 2017