Identification of Rice Variety Using Geometric Features and Neural Network

 Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm

Image Processing and Pattern Recognition Applied to Soil Structure

This thesis represents a collaborative research between the Department of Electronics & Electrical Engineering and the Department of Civil Engineering, University of Glasgow. The project was initially aimed at development of some theories and techniques of image processing and pattern recognition for the study of soil microstructures. More specifically, the aim was to study the shapes, orientations, and arrangements of soil particles and voids (i.e. pores): these three are very important properties, which are used both for description, recognition and classification of soils, and also for studying the relationships between the soil structures and physical, chemical, geological, geographical, and environmental changes. The work presented here was based principally on a need for analysing the structure of soil as recorded in two-dimensional images which might be conventional photographs, optical micrographs, or electron-micrographs. In this thesis, first a brief review of image processing and pattern recognition and their previous application in the study of soil microstructures is given. Then a convex hull based shape description and classification for soil particles is presented. A new algorithm, SPCH, is proposed for finding the convex hull of either a binary object or a cluster of points in a plane. This algorithm is efficient and reliable. Features of pattern vectors for shape description and classification are obtained from the convex hull and the object. These features are invariant with respect to coordinate rotation, translation, and scaling. The objects can then be classified by any standard feature-space method: here minimum-distance classification was used. Next the orientation analysis of soil particles is described. A new method, Directed Vein, is proposed for the analysis. Another three methods: Convex Hull, Principal Components, and Moments, are also presented. Comparison of the four methods shows that the Directed Vein method appears the fastest; but it also has the special property of estimating an 'internal preferred orientation' whereas the other methods estimate an 'elongation direction'. Fourth, the roundness/sharpness analysis of soil particles is presented. Three new algorithms, referred to as the Centre, Gradient Centre, and Radius methods, all based on the Circular Hough Transform, are proposed. Two traditional Circular Hough Transform algorithms are presented as well. The three new methods were successfully applied to the measurement of the roundness (sharpness of comers) of two-dimensional particles. The five methods were compared from the points of view of memory requirement, speed, and accuracy; and the Radius method appears to be the best for the special topic of sharpness/roundness analysis. Finally the analysis and classification of aggregates of objects is introduced. A new method. Extended Linear Hough Transform, is proposed. In this method, the orientations and locations of the objects are mapped into extended Hough space. The arrangements of the objects within an aggregate are then determined by analysing the data distributions in this space. The aggregates can then be classified using a tree classifier. Taken together, the methods developed or tested here provide a useful toolkit for analysing the shapes, orientation, and aggregation of particles such as those seen in two-dimensional images of soil structure at various scales

Contribution to structural parameters computation: volume models and methods

Bio-CAD and in-silico experimentation are getting a growing interest in biomedical applications where scientific data coming from real samples are used to compute structural parameters that allow to evaluate physical properties. Non-invasive imaging acquisition technologies such as CT, mCT or MRI, plus the constant growth of computer capabilities, allow the acquisition, processing and visualization of scientific data with increasing degree of complexity. Structural parameters computation is based on the existence of two phases (or spaces) in the sample: the solid, which may correspond to the bone or material, and the empty or porous phase and, therefore, they are represented as binary volumes. The most common representation model for these datasets is the voxel model, which is the natural extension to 3D of 2D bitmaps. In this thesis, the Extreme Vertices Model (EVM) and a new proposed model, the Compact Union of Disjoint Boxes (CUDB), are used to represent binary volumes in a much more compact way. EVM stores only a sorted subset of vertices of the object¿s boundary whereas CUDB keeps a compact list of boxes. In this thesis, methods to compute the next structural parameters are proposed: pore-size distribution, connectivity, orientation, sphericity and roundness. The pore-size distribution helps to interpret the characteristics of porous samples by allowing users to observe most common pore diameter ranges as peaks in a graph. Connectivity is a topological property related to the genus of the solid space, measures the level of interconnectivity among elements, and is an indicator of the biomechanical characteristics of bone or other materials. The orientation of a shape can be defined by rotation angles around a set of orthogonal axes. Sphericity is a measure of how spherical is a particle, whereas roundness is the measure of the sharpness of a particle's edges and corners. The study of these parameters requires dealing with real samples scanned at high resolution, which usually generate huge datasets that require a lot of memory and large processing time to analyze them. For this reason, a new method to simplify binary volumes in a progressive and lossless way is presented. This method generates a level-of-detail sequence of objects, where each object is a bounding volume of the previous objects. Besides being used as support in the structural parameter computation, this method can be practical for task such as progressive transmission, collision detection and volume of interest computation. As part of multidisciplinary research, two practical applications have been developed to compute structural parameters of real samples. A software for automatic detection of characteristic viscosity points of basalt rocks and glasses samples, and another to compute sphericity and roundness of complex forms in a silica dataset.El Bio-Diseño Asistido por Computadora (Bio-CAD), y la experimentacion in-silico est an teniendo un creciente interes en aplicaciones biomedicas, en donde se utilizan datos cientificos provenientes de muestras reales para calcular par ametros estructurales que permiten evaluar propiedades físicas. Las tecnologías de adquisicion de imagen no invasivas como la TC, TC o IRM, y el crecimiento constante de las prestaciones de las computadoras, permiten la adquisicion, procesamiento y visualizacion de datos científicos con creciente grado de complejidad. El calculo de parametros estructurales esta basado en la existencia de dos fases (o espacios) en la muestra: la solida, que puede corresponder al hueso o material, y la fase porosa o vacía, por tanto, tales muestras son representadas como volumenes binarios. El modelo de representacion mas comun para estos conjuntos de datos es el modelo de voxeles, el cual es una extension natural a 3D de los mapas de bits 2D. En esta tesis se utilizan el modelo Extreme Verrtices Model (EVM) y un nuevo modelo propuesto, the Compact Union of Disjoint Boxes (CUDB), para representar los volumenes binarios en una forma mucho mas compacta. El modelo EVM almacena solo un subconjunto ordenado de vertices de la frontera del objeto mientras que el modelo CUDB mantiene una lista compacta de cajas. En esta tesis se proponen metodos para calcular los siguientes parametros estructurales: distribucion del tamaño de los poros, conectividad, orientacion, esfericidad y redondez. La distribucion del tamaño de los poros ayuda a interpretar las características de las muestras porosas permitiendo a los usuarios observar los rangos de diametro mas comunes de los poros mediante picos en un grafica. La conectividad es una propiedad topologica relacionada con el genero del espacio solido, mide el nivel de interconectividad entre los elementos, y es un indicador de las características biomecanicas del hueso o de otros materiales. La orientacion de un objeto puede ser definida por medio de angulos de rotacion alrededor de un conjunto de ejes ortogonales. La esfericidad es una medida de que tan esferica es una partícula, mientras que la redondez es la medida de la nitidez de sus aristas y esquinas. En el estudio de estos parametros se trabaja con muestras reales escaneadas a alta resolucion que suelen generar conjuntos de datos enormes, los cuales requieren una gran cantidad de memoria y mucho tiempo de procesamiento para ser analizados. Por esta razon, se presenta un nuevo metodo para simpli car vol umenes binarios de una manera progresiva y sin perdidas. Este metodo genera una secuencia de niveles de detalle de los objetos, en donde cada objeto es un volumen englobante de los objetos previos. Ademas de ser utilizado como apoyo en el calculo de parametros estructurales, este metodo puede ser de utilizado en otras tareas como transmision progresiva, deteccion de colisiones y calculo de volumen de interes. Como parte de una investigacion multidisciplinaria, se han desarrollado dos aplicaciones practicas para calcular parametros estructurales de muestras reales. Un software para la deteccion automatica de puntos de viscosidad característicos en muestras de rocas de basalto y vidrios, y una aplicacion para calcular la esfericidad y redondez de formas complejas en un conjunto de datos de sílice

Quantification of a continuous-cover forest in Sweden using remote sensing techniques

Mapping and quantifying forest information about e.g. land cover, tree height and biomass has traditionally been a both time-consuming and labour-intensive part of forestry and forest research as field measurements typically are collected manually using handheld equipment. Remote sensing has proved to be a valuable complement to field based measurements as it enables for fast and relatively cheap collection of data from areas that would be hard to access from the ground. The aim of this thesis was to map and quantify the Romperöd forest outside Glimåkra in southern Sweden where selective thinning forestry has been practised since the 1960’s. The study was carried out using high resolution multispectral aerial images and small-footprint discrete-return LiDAR data included in the Swedish national elevation model in conjunction with field measurements. The results revealed a mixed forest where Norway spruce was the most dominating tree species, accounting for 40.2 % of the total coverage of the study area, followed by Scots pine (13.8 %), broadleaved trees (8.7 %), succession (6.7 %) and bare-ground (4.1 %). The elevation of the terrain varies between 76.2 and 107.3 meters above sea level, with a ridge extending from south to north. The canopy height of the forest varies greatly throughout the study area and ranged between 1.0 and 34.6 m with an average height of 15.1 m and a standard deviation of 8 m. Above-ground biomass (AGB) was estimated by fitting a multiple regression model to LiDAR-derived vegetation metrics (independent variables) and AGB estimates based on field measurements (dependent variable). The model managed to explain 70 % of the variability in the field measured AGB estimates and was applied to the entire study area yielding an average AGB of 122 900 kg/ha and a standard deviation of 50 497 kg/ha. The inclusion of remote sensing data improved the AGB estimates compared to those based solely on field measurements. The results were compared to the AGB data included in the SLU Forest Map which showed low correlation with AGB estimates based on field measurements (adjusted R2: 0.14), proving it unsuitable for the part of the Romperöd forest characterized by selective thinning.Att karlägga och kvantifiera skogsinformation angående exempelvis marktäcke, terräng, trädhöjder och volym är en traditionellt både tidskrävande och dyr del av skogsbruk och forskning eftersom mätningar vanligtvis samlas in i fält med handhållna instrument. Fjärranalys har visat sig vara ett värdefullt komplement till fältbaserade mätningar eftersom det möjliggör för snabb och relativt billig insamling av data från områden som skulle vara svåra att besöka i fält. Syftet med denna uppsats var att kartlägga och kvantifiera Romperödskogen utanför Glimåkra i nordöstra Skåne där blädningsskogsbruk har praktiserats sedan 1960-talet. Studien genomfördes med hjälp av fjärranalysdata i kombination med mätdata som samlats in i fält och blottlade en blandskog där gran utgör det dominerande trädslaget (40,2 % av studieområdet), följt av tall (13,8 %), lövträd (8,7 %), föryngringar (6,7 %) och bar mark (4,1 %). Terrängen varierar från 76,2 till 107,3 meter över havet med en ås som sträcker sig från söder till norr. Höjden på krontaket är heterogent i hela studieområdet och varierar mellan 1,0 och 34,6 m med en medelhöjd på 15,1 m och en standardavikelse på 8 m. Biomassa ovan jord uppskattades för hela studieområdet och visade ett genomsnitt på 122 900 kg/ha med en standardavikelse på 50 497 kg. Resultaten jämfördes med biomassa ovan jord enligt SLUs Skogskarta som visade låg överensstämmelse med skattningar baserade på fältmätningar vilket visar att SLU:s Skogskarta ej är applicerbar för den del av Romperödskogen som kännetecknas av blädning. Den föreslagna metodiken kan användas för att planera skogsbruk eller för att studera framtida förändringar eller störningar i skogen. Resultaten kan även vara till hjälp vid framtida forskning angående Romperödskogen och dess kolutbyte med atmosfären då biomassa ovan jord direkt kan konverteras till kolförråd, vilket är ett viktigt steg för att kunna studera effekten blädningsskogsbruk har på kolcykeln i skogen

Machining accuracy enhancement using machine tool error compensation and metrology

This dissertation aims to enhance machining accuracy by machine tool error reduction and workpiece metrology. The error characteristics are studied by building a quasi-static error model. Perturbed forward kinematic model is used for modeling a 5-axis Computer Numerical Control (CNC) machine with one redundant linear axis. It is found that the 1st order volumetric error model of the 5-axis machine is attributed to 32 error parameter groups. To identify the model by estimating these parameter groups using the least-squares fitting, errors at 290 quasi-randomly generated measurement points over the machine’s workspace are measured using a laser tracker. The identified error model explains 90% of the mean error of the training data sets. However, the measurements using the laser tracker take about 90 minutes, which may cause the identified error parameters to be inaccurate due to the slow varying and transient natures of thermal errors. To shorten the measurement time, an experimental design approach, which suggests the optimal observation locations such that the corresponding robustness of identification is maximized, is applied to design the optimal error observers. Since the observers must be uniformly distributed over the workspace for gaining redundancy, the constrained K-optimal designs are used to select 80 K-optimal observers for the 5-axis machine. Six measurement cycles using 80 observers are done at machine’s different thermal states within a 400-minute experiment. Six error models are trained with consistent performances and are found to be comparable to the one trained by 290 quasi-random observations. This shows the feasibility of using smaller but more strategical-chosen point-set in data-driven error models. More importantly, the growth on mean nominal (119.1 to 181.9 microns) and modeled error (26.3 to 33.9 microns) suggest the necessity of thermal error tracking for enhancing the machining accuracy. A point-set based metrology is also developed to compensate the inaccuracies introduced by workpiece and fixtures and enhance machining accuracy. The machinability of all planar features is examined by virtually comparing the scanned data with the nominal machining planes, which are also known as virtual gages. The virtual gaging problem is modeled as a constrained linear program. The optimal solution to the problem can compensate the displacement introduced by workpiece and fixtures and hence guarantee a conforming finished part. To transfer point-set data into mathematical constraints, algorithms that align, segment, downsize and filter the point-set data are exploited. The concept of virtual gage analysis is demonstrated using experimental data for a simple raw casting. However, for the case where the casting is defective, and some virtual gages are not feasible, the corresponding linear program was found to have no solution. By introducing slack variables to the original linear programming problem, the extended problem has been solved. The extended model is validated for the data obtained for another casting. Further, the analysis predicts the machining allowances on all functional features. Cylindrical surface and its tolerance verification play important role in machining process. Although there exist many approaches that can fit the maximum, minimum and minimum zone cylinders, the cylinder fitting problems can be even simplified. The proposed methodology seeks to reduce the number of parameters used in cylinder fitting model by using the projection model that considers the degenerated tolerance specifications of the projected 2-D point-set. Also, to avoid the problem of local optimum by introducing the optimal direction of projection such that the 2-D point projected onto this direction has optimal tolerance specifications (maximum, minimum and minimum zone circles), global optimum solver such as Particle Swarm Optimization (PSO) is used. The proposed simplified method shows consistent results compared with the results from literature

Stabilization and Imaging of Cohesionless Soil Specimens

abstract: This dissertation describes development of a procedure for obtaining high quality, optical grade sand coupons from frozen sand specimens of Ottawa 20/30 sand for image processing and analysis to quantify soil structure along with a methodology for quantifying the microstructure from the images. A technique for thawing and stabilizing frozen core samples was developed using optical grade Buehler® Epo-Tek® epoxy resin, a modified triaxial cell, a vacuum/reservoir chamber, a desiccator, and a moisture gauge. The uniform epoxy resin impregnation required proper drying of the soil specimen, application of appropriate confining pressure and vacuum levels, and epoxy mixing, de-airing and curing. The resulting stabilized sand specimen was sectioned into 10 mm thick coupons that were planed, ground, and polished with progressively finer diamond abrasive grit levels using the modified Allied HTP Inc. polishing method so that the soil structure could be accurately quantified using images obtained with the use of an optical microscopy technique. Illumination via Bright Field Microscopy was used to capture the images for subsequent image processing and sand microstructure analysis. The quality of resulting images and the validity of the subsequent image morphology analysis hinged largely on employment of a polishing and grinding technique that resulted in a flat, scratch free, reflective coupon surface characterized by minimal microstructure relief and good contrast between the sand particles and the surrounding epoxy resin. Subsequent image processing involved conversion of the color images first to gray scale images and then to binary images with the use of contrast and image adjustments, removal of noise and image artifacts, image filtering, and image segmentation. Mathematical morphology algorithms were used on the resulting binary images to further enhance image quality. The binary images were then used to calculate soil structure parameters that included particle roundness and sphericity, particle orientation variability represented by rose diagrams, statistics on the local void ratio variability as a function of the sample size, and the local void ratio distribution histograms using Oda's method and Voronoi tessellation method, including the skewness, kurtosis, and entropy of a gamma cumulative probability distribution fit to the local void ratio distribution.Dissertation/ThesisM.S. Civil Engineering 201

Numerical Simulation and Characterisation of the Packing of Granular Materials

The scientific problems related to granular matter are ubiquitous. It is currently an active area of research for physicists and earth scientists, with a wide range of applications within the industrial community. Simple analogue experiments exhibit behaviour that is neither predicted nor described by any current theory. The work presented here consists of modelling granular media using a two-dimensional combined Finite-Discrete Element Method (FEM-DEM). While computationally expensive, as well as modelling accurately the dynamic interactions between independent and arbitrarily shaped grains, this method allows for a complete description of the stress state within individual grains during their transient motion. After a detailed description of FEM-DEM principles, this computational approach is used to investigate the packing of elliptical particles. The work is aimed at understanding the influence of the particle shape (the ellipse aspect ratio) on the emergent properties of the granular matrix such as the particle coordination number and the packing density. The diff erences in microstructure of the resultant packing are analysed using pair correlation functions, particle orientations and pore size distributions. A comparison between frictional and frictionless systems is carried out. It shows great diff erences not only in the calculated porosity and coordination number, but also in terms of structural arrangement and stress distribution. The results suggest that the particle's shape a ffects the structural order of the particle assemblage, which itself controls the stress distribution between the pseudo-static grains. The study then focuses on describing the stress patterns or \force chains" naturally generated in a frictional system. An algorithm based on the analysis of the contact force network is proposed and applied to various packs in order to identify the force chains. A statistical analysis of the force chains looking at their orientation, length and proportion of the particles that support the loads is then performed. It is observed that force chains propagate less efficiently and more heterogeneously through granular systems made of elliptical particles than through systems of discs and it is proposed that structural diff erences due to the particle shape lead to a signifi cant reduction in the length of the stress path that propagates across connected particles. Finally, the e ffect of compression on the granular packing, the emergent properties and the contact force distribution is examined. Results show that the force network evolves towards a more randomly distributed system (from an exponential to a Gaussian distribution), and it confi rms the observations made from simulations using discs. To conclude, the combined finite-discrete element method applied to the study of granular systems provides an attractive modelling strategy to improve the knowledge of granular matter. This is due to the wide range of static and dynamic problems that can be treated with a rigorous physical basis. The applicability of the method was demonstrated through to a variety of problems that involve di fferent physical processes modelled with the FEM-DEM (internal deformations, fracture, and complex geometry). With the rapid extension of the practical limits of computational models, this work emphasizes the opportunity to move towards a modern generation of computer software to understand the complexity of the phenomena associated with discontinua

An effective dimensional inspection method based on zone fitting

Coordinate measuring machines are widely used to generate data points from an actual surface. The generated measurement data must be analyzed to yield critical geometric deviations of the measured part according to the requirements specified by the designer. However, ANSI standards do not specify the methods that should be used to evaluate the tolerances. The coordinate measuring machines employ different verification algorithms which may yield different results. Functional requirements or assembly conditions on a manufactured part are normally translated into geometric constraints to which the part must conform. Minimum zone evaluation technique is used when the measured data is regarded as an exact copy of the actual surface and the tolerance zone is represented as geometric constraints on the data. In the present study, a new zone-fitting algorithm is proposed. The algorithm evaluates the minimum zone that encompasses the set of measured points from the actual surface. The search for the rigid body transformation that places the set of points in the zone is modeled as a nonlinear optimization problem. The algorithm is employed to find the form tolerance of 2-D (line, circle) as well as 3-D geometries (cylinder). It is also used to propose an inspection methodology for turbine blades. By constraining the transformation parameters, the proposed methodology determines whether the points measured at the 2-D cross-sections fit in the corresponding tolerance zones simultaneously