Istraživanje i modeliranje nepoznatog poligonalnog prostora zasnovano na nesigurnim podacima udaljenosti

We consider problem of exploration and mapping of unknown indoor environments using laser range finder. We assume a setup with a resolved localization problem and known uncertainty sensor models. Most exploration algorithms are based on detection of a boundary between explored and unexplored regions. They are, however, not efficient in practice due to uncertainties in measurement, localization and map building. The exploration and mapping algorithm is proposed that extends Ekman’s exploration algorithm by removing rigid constraints on the range sensor and robot localization. The proposed algorithm includes line extraction algorithm developed by Pfister, which incorporates noise models of the range sensor and robot’s pose uncertainty. A line representation of the range data is used for creating polygon that represents explored region from each measurement pose. The polygon edges that do not correspond to real environmental features are candidates for a new measurement pose. A general polygon clipping algorithm is used to obtain the total explored region as the union of polygons from different measurement poses. The proposed algorithm is tested and compared to the Ekman’s algorithm by simulations and experimentally on a Pioneer 3DX mobile robot equipped with SICK LMS-200 laser range finder.Razmatramo problem istraživanja i izgradnje karte nepoznatog unutarnjeg prostora koristeći laserski sensor udaljenosti. Pretpostavljamo riješenu lokalizaciju robota i poznati model nesigurnosti senzora. Većna se algoritama istraživanja zasniva na otkrivanju granica istraženog i neistraženog područja. Međutim, u praksi nisu učinkoviti zbog nesigurnosti mjerenja, lokalizacije i izgradnje karte. Razvijen je algoritam istraživanja i izgradnje karte koji proširuje Ekmanov algoritam uklanjanjem strogih ograničenja na senzor udaljenosti i lokalizaciju robota. Razvijeni algoritam uključuje algoritam izdvajanja linijskih segmenata prema Pfisteru, koji uzima u obzir utjecaje zašumljenosti senzora i nesigurnosti položaja mobilnog robota. Linijska reprezentacija podataka udaljenosti koristi se za stvaranje poligona koji predstavlja istraženo područje iz svakog mjernog položaja. Bridovi poligona koji se ne podudaraju sa stvarnim značajkama prostora su kandidati za novi mjerni položaj. Algoritam općenitog isijecanja poligona korišten je za dobivanje ukupnog istraženog područja kao unija poligona iz različitih mjernih položaja. Razvijeni algoritam testiran je i uspoređen s izvornim Ekmanovim algoritmom simulacijski i eksperimentalno na mobilnom robotu Pioneer 3DX opremljenim laserskim senzorom udaljenosti SICK LMS-200

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

Occluded iris classification and segmentation using self-customized artificial intelligence models and iterative randomized Hough transform

A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is crosscorrelated with the same subject’s iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iriscode Hamming distance of the two most correlated segments to identify the subject’s unique iris pattern with high accuracy, security, and reliability

A microcomputer-based vision system to recognize and locate partially occluded parts in binary and gray level images

This paper presents a microcomputer-based machine vision system to recognize and locate partially occluded parts in binary or gray level images. The recognition process is restricted to untilted, two-dimensional objects;A new edge-tracking technique in conjunction with a straight-line approximation algorithm is used to identify the local features in an image. Corners and holes serve as local features. The local features identified in an image are matched against all the compatible features stored for the model parts. The algorithm computes, for all image and model features matches, a coordinate transformation that maps a model feature onto an image feature. A new clustering algorithm has been developed to identify consistent coordinate transformation clusters that serve as initial match hypotheses. A hypothesis verification process eliminates the match hypotheses that are not compatible with the image information;The system performance was compared to a vision system restricted to recognize nonoverlapping parts. Both systems require the same hardware configuration and share the basic image processing routines