A neuro-genetic hybrid approach to automatic identification of plant leaves

Plants are essential for the existence of most living things on this planet. Plants are used for providing food, shelter, and medicine. The ability to identify plants is very important for several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds. In recent times, many researchers have made attempts to develop automated plant species recognition systems. However, the current computer-based plants recognition systems have limitations as some plants are naturally complex, thus it is difficult to extract and represent their features. Further, natural differences of features within the same plant and similarities between plants of different species cause problems in classification. This thesis developed a novel hybrid intelligent system based on a neuro-genetic model for automatic recognition of plants using leaf image analysis based on novel approach of combining several image descriptors with Cellular Neural Networks (CNN), Genetic Algorithm (GA), and Probabilistic Neural Networks (PNN) to address classification challenges in plant computer-based plant species identification using the images of plant leaves. A GA-based feature selection module was developed to select the best of these leaf features. Particle Swam Optimization (PSO) and Principal Component Analysis (PCA) were also used sideways for comparison and to provide rigorous feature selection and analysis. Statistical analysis using ANOVA and correlation techniques confirmed the effectiveness of the GA-based and PSO-based techniques as there were no redundant features, since the subset of features selected by both techniques correlated well. The number of principal components (PC) from the past were selected by conventional method associated with PCA. However, in this study, GA was used to select a minimum number of PC from the original PC space. This reduced computational cost with respect to time and increased the accuracy of the classifier used. The algebraic nature of the GA’s fitness function ensures good performance of the GA. Furthermore, GA was also used to optimize the parameters of a CNN (CNN for image segmentation) and then uniquely combined with PNN to improve and stabilize the performance of the classification system. The CNN (being an ordinary differential equation (ODE)) was solved using Runge-Kutta 4th order algorithm in order to minimize descritisation errors associated with edge detection. This study involved the extraction of 112 features from the images of plant species found in the Flavia dataset (publically available) using MATLAB programming environment. These features include Zernike Moments (20 ZMs), Fourier Descriptors (21 FDs), Legendre Moments (20 LMs), Hu 7 Moments (7 Hu7Ms), Texture Properties (22 TP) , Geometrical Properties (10 GP), and Colour features (12 CF). With the use of GA, only 14 features were finally selected for optimal accuracy. The PNN was genetically optimized to ensure optimal accuracy since it is not the best practise to fix the tunning parameters for the PNN arbitrarily. Two separate GA algorithms were implemented to optimize the PNN, that is, the GA provided by MATLAB Optimization Toolbox (GA1) and a separately implemented GA (GA2). The best chromosome (PNN spread) for GA1 was 0.035 with associated classification accuracy of 91.3740% while a spread value of 0.06 was obtained from GA2 giving rise to improved classification accuracy of 92.62%. The PNN-based classifier used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. Some computational theoretic properties on PNN are also presented

Quantifying alignment in carbon nanotube yarns and similar two‐dimensional anisotropic systems

The uniaxial orientational order in a macromolecular system is usually specified using the Hermans factor which is equivalent to the second moment of the system\u27s orientation distribution function (ODF) expanded in terms of Legendre polynomials. In this work, we show that for aligned materials that are two‐dimensional (2D) or have a measurable 2D intensity distribution, such as carbon nanotube (CNT) textiles, the Hermans factor is not appropriate. The ODF must be expanded in terms of Chebyshev polynomials and therefore, its second moment is a better measure of orientation in 2D. We also demonstrate that both orientation parameters (Hermans in three dimensional (3D) and Chebyshev in 2D) depend not only on the respective full‐width‐at‐half‐maximum of the peaks in the ODF but also on the shape of the fitted functions. Most importantly, we demonstrate a method to rapidly estimate the Chebyshev orientation parameter from a sample\u27s 2D Fourier power spectrum, using an analysis program written in Python which is available for open access. As validation examples, we use digital photographs of dry spaghetti as well as scanning electron microscopy images of direct‐spun carbon nanotube fibers, proving the technique\u27s applicability to a wide variety of fibers and images

Image understanding and feature extraction for applications in industry and mapping

Bibliography: p. 212-220.The aim of digital photogrammetry is the automated extraction and classification of the three dimensional information of a scene from a number of images. Existing photogrammetric systems are semi-automatic requiring manual editing and control, and have very limited domains of application so that image understanding capabilities are left to the user. Among the most important steps in a fully integrated system are the extraction of features suitable for matching, the establishment of the correspondence between matching points and object classification. The following study attempts to explore the applicability of pattern recognition concepts in conjunction with existing area-based methods, feature-based techniques and other approaches used in computer vision in order to increase the level of automation and as a general alternative and addition to existing methods. As an illustration of the pattern recognition approach examples of industrial applications are given. The underlying method is then extended to the identification of objects in aerial images of urban scenes and to the location of targets in close-range photogrammetric applications. Various moment-based techniques are considered as pattern classifiers including geometric invariant moments, Legendre moments, Zernike moments and pseudo-Zernike moments. Two-dimensional Fourier transforms are also considered as pattern classifiers. The suitability of these techniques is assessed. These are then applied as object locators and as feature extractors or interest operators. Additionally the use of fractal dimension to segment natural scenes for regional classification in order to limit the search space for particular objects is considered. The pattern recognition techniques require considerable preprocessing of images. The various image processing techniques required are explained where needed. Extracted feature points are matched using relaxation based techniques in conjunction with area-based methods to 'obtain subpixel accuracy. A subpixel pattern recognition based method is also proposed and an investigation into improved area-based subpixel matching methods is undertaken. An algorithm for determining relative orientation parameters incorporating the epipolar line constraint is investigated and compared with a standard relative orientation algorithm. In conclusion a basic system that can be automated based on some novel techniques in conjunction with existing methods is described and implemented in a mapping application. This system could be largely automated with suitably powerful computers

Quantifying alignment in carbon nanotube yarns and similar two-dimensional anisotropic systems

Abstract: The uniaxial orientational order in a macromolecular system is usually specified using the Hermans factor which is equivalent to the second moment of the system's orientation distribution function (ODF) expanded in terms of Legendre polynomials. In this work, we show that for aligned materials that are two‐dimensional (2D) or have a measurable 2D intensity distribution, such as carbon nanotube (CNT) textiles, the Hermans factor is not appropriate. The ODF must be expanded in terms of Chebyshev polynomials and therefore, its second moment is a better measure of orientation in 2D. We also demonstrate that both orientation parameters (Hermans in three dimensional (3D) and Chebyshev in 2D) depend not only on the respective full‐width‐at‐half‐maximum of the peaks in the ODF but also on the shape of the fitted functions. Most importantly, we demonstrate a method to rapidly estimate the Chebyshev orientation parameter from a sample's 2D Fourier power spectrum, using an analysis program written in Python which is available for open access. As validation examples, we use digital photographs of dry spaghetti as well as scanning electron microscopy images of direct‐spun carbon nanotube fibers, proving the technique's applicability to a wide variety of fibers and images

Study of object recognition and identification based on shape and texture analysis

The objective of object recognition is to enable computers to recognize image patterns without human intervention. According to its applications, it is mainly divided into two parts: recognition of object categories and detection/identification of objects. My thesis studied the techniques of object feature analysis and identification strategies, which solve the object recognition problem by employing effective and perceptually important object features. The shape information is of particular interest and a review of the shape representation and description is presented, as well as the latest research work on object recognition. In the second chapter of the thesis, a novel content-based approach is proposed for efficient shape classification and retrieval of 2D objects. Two object detection approaches, which are designed according to the characteristics of the shape context and SIFT descriptors, respectively, are analyzed and compared. It is found that the identification strategy constructed on a single type of object feature is only able to recognize the target object under specific conditions which the identifier is adapted to. These identifiers are usually designed to detect the target objects which are rich in the feature type captured by the identifier. In addition, this type of feature often distinguishes the target object from the complex scene. To overcome this constraint, a novel prototyped-based object identification method is presented to detect the target object in the complex scene by employing different types of descriptors to capture the heterogeneous features. All types of descriptors are modified to meet the requirement of the detection strategy’s framework. Thus this new method is able to describe and identify various kinds of objects whose dominant features are quite different. The identification system employs the cosine similarity to evaluate the resemblance between the prototype image and image windows on the complex scene. Then a ‘resemblance map’ is established with values on each patch representing the likelihood of the target object’s presence. The simulation approved that this novel object detection strategy is efficient, robust and of scale and rotation invariance

Positional disorder in Huygens’ metasurfaces

The primary goal of this thesis was the experimental exploration of certain linear optical properties of positionally disordered Huygens-metasurfaces, which consist of two-dimensional arrangements of specific scattering particles. This goal has been motivated by two recent trends: On the one hand, dielectric Huygens-metasurfaces have proved to be a suitable material platform for holographic and wavefront-shaping applications, and on the other hand, structurally disordered metamaterials have already exhibited features that cannot be attained with ordered structures. However, the influence of positional disorder on Huygens-metasurfaces has not yet been systematically studied, and it thus remained the question if and how Huygens-metasurfaces could benefit from positional disorder. This thesis characterizes the directional transmission and reflection of positionally disordered Huygens' metasurfaces both experimentally and numerically, provides a simple theoretical model that explains the observation of a disorder-induced phase transition on the basis of effective multipole moments, and confirms that positional disorder is indeed a valuable degree of freedom in the design of holographic and wavefront-shaping devices. Based on these findings, future research may investigate how the directional transmission and reflection of positionally disordered Huygens-metasurfaces can be tailored via their particle arrangements and/or higher multipole moments.Das Hauptziel dieser Arbeit war die experimentelle Untersuchung bestimmter linearer optischer Eigenschaften positionell ungeordneter Huygens-Metaoberflächen – das sind zweidimensionale Anordnungen spezieller Streupartikel. Dieses Ziel wurde durch zwei aktuelle Forschungstrends motiviert: Einerseits haben sich dielektrische Huygens-Metaoberflächen als eine geeignete Materialplattform für holografische und wellenfrontformende Anwendungen erwiesen, und andererseits wurde bereits gezeigt, dass strukturell ungeordnete Metamaterialien Eigenschaften besitzen, die mit geordneten Strukturen nicht zu erreichen sind. Der Einfluss positioneller Unordnung auf Huygens-Metaoberflächen wurde jedoch noch nicht systematisch untersucht und es verblieb daher die Frage, ob und wie Huygens-Metaoberflächen von positioneller Unordnung profitieren könnten. In dieser Arbeit wird die gerichtete Transmission und Reflexion positionell ungeordneter Huygens-Metaoberflächen sowohl experimentell als auch numerisch charakterisiert, es wird ein einfaches theoretisches Modell vorgestellt, welches die Beobachtung eines durch Unordnung induzierten Phasenübergangs auf der Grundlage effektiver Multipolmomente erklärt, und es wird bestätigt, dass positionelle Unordnung tatsächlich ein wertvoller Freiheitsgrad in der Gestaltung holografischer und wellenfrontformender Bauelemente ist. Auf diesen Erkenntnissen aufbauend könnte in Zukunft erforscht werden, wie die gerichtete Transmission und Reflexion positionell ungeordneter Huygens-Metaoberflächen über deren Partikelanordnungen und/oder höheren Multipolmomente maßgeschneidert werden kann

Rapid wide-field imaging of soft-tissue microstructure

Tissue microstructure is pivotal in determining the function, behavior, and disease state of biological tissues. Histology and advanced optical techniques are commonly used to examine the cellular, extracellular, and subcellular constituents that define tissue microstructure. However, these techniques frequently require tedious and destructive tissue preparations and lengthy imaging times, or have limited fields of view. Therefore, it is challenging to study soft-tissue microstructure within the macroscopic spatial and temporal context of tissue- and organ-level function. Wide-field imaging techniques provide a non-destructive alternative to rapidly assess tissue microstructure across macroscopic fields of view. Rather than resolving microstructure directly, these techniques are sensitive to light-scattering characteristics of tissue that indicate the underlying microstructure. This dissertation develops light-scattering models to interpret tissue microstructure from light-scattering across macroscopic fields of view rapidly and non-destructively. The first half of the dissertation uses spatial frequency domain imaging (SFDI) to quantify the sub-diffuse light-scattering characteristics of tissues that are intrinsically linked to microstructure. It then introduces a novel empirical model which allows rapid fitting of SFDI data and is sensitive to changes in microparticle size. This technique is then demonstrated as a potential surgical guidance tool for Mohs Micrographic Surgery by rapidly and non-destructively demarcating tumor boundaries in skin biopsies. The imaging and processing speeds achieved with this technique can improve clinical workflows, particularly tissue-conserving surgical procedures, which are currently hindered by the time necessary to determine tumor boundaries using histopathology. Improvements to this technique by use of higher spatial frequencies are also considered. The second section investigates polarization-dependent scattering in tissues that is a result of collagen fiber microstructure. An experimentally-validated computational model is developed to allow direct conversion of polarized-light measurements into absolute measures of collagen fiber alignment in tissues. Furthermore, a combined polarized light SFDI system (pSFDI) is demonstrated to measure distinct fiber alignments in multi-layered tissue samples. The increased speed and versatility of this system is employed to map wide-field microfiber kinematics during mechanical tissue deformation. This technique enables direct examination of the contributions of local fiber kinematics to tissue- and organ-level scales of growth and remodeling.Biomedical Engineerin

Inhomogeneity Correction in High Field Magnetic Resonance Images

Projecte realitzat en col.laboració amb el centre Swiss Federal Institute of Technology (EPFL)Magnetic Resonance Imaging, MRI, is one of the most powerful and harmless ways to study human inner tissues. It gives the chance of having an accurate insight into the physiological condition of the human body, and specially, the brain. Following this aim, in the last decade MRI has moved to ever higher magnetic field strength that allow us to get advantage of a better signal-to-noise ratio. This improvement of the SNR, which increases almost linearly with the field strength, has several advantages: higher spatial resolution and/or faster imaging, greater spectral dispersion, as well as an enhanced sensitivity to magnetic susceptibility. However, at high magnetic resonance imaging, the interactions between the RF pulse and the high permittivity samples, which causes the so called Intensity Inhomogeneity or B1 inhomogeneity, can no longer be negligible. This inhomogeneity causes undesired efects that afects quantitatively image analysis and avoid the application classical intensity-based segmentation and other medical functions. In this Master thesis, a new method for Intensity Inhomogeneity correction at high ¯eld is presented. At high ¯eld is not possible to achieve the estimation and the correction directly from the corrupted data. Thus, this method attempt the correction by acquiring extra information during the image process, the RF map. The method estimates the inhomogeneity by the comparison of both acquisitions. The results are compared to other methods, the PABIC and the Low-Pass Filter which try to correct the inhomogeneity directly from the corrupted data