Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components

Sparse signal representation by adaptive non-uniform B-spline dictionaries on a compact interval

Non-uniform B-spline dictionaries on a compact interval are discussed in the context of sparse signal representation. For each given partition, dictionaries of B-spline functions for the corresponding spline space are built up by dividing the partition into subpartitions and joining together the bases for the concomitant subspaces. The resulting slightly redundant dictionaries are composed of B-spline functions of broader support than those corresponding to the B-spline basis for the identical space. Such dictionaries are meant to assist in the construction of adaptive sparse signal representation through a combination of stepwise optimal greedy techniques

Natural genetic variation in fluctuating asymmetry of wing shape in Drosophila melanogaster

Fluctuating asymmetry (FA), defined as random deviation from perfect symmetry, has been used to assay the inability of individuals to buffer their developmental processes from environmental perturbations (i.e., developmental instability). In this study, we aimed to characterize the natural genetic variation in FA of wing shape in Drosophila melanogaster, collected from across the Japanese archipelago. We quantified wing shapes at whole wing and partial wing component levels and evaluated their mean and FA. We also estimated the heritability of the mean and FA of these traits. We found significant natural genetic variation in all the mean wing traits and in FA of one of the partial wing components. Heritability estimates for mean wing shapes were significant in two and four out of five wing traits in males and females, respectively. On the contrary, heritability estimates for FA were low and not significant. This is a novel study of natural genetic variation in FA of wing shape. Our findings suggest that partial wing components behave as distinct units of selection for FA, and local adaptation of the mechanisms to stabilize developmental processes occur in nature

Automated measurement of Drosophila wings

BACKGROUND: Many studies in evolutionary biology and genetics are limited by the rate at which phenotypic information can be acquired. The wings of Drosophila species are a favorable target for automated analysis because of the many interesting questions in evolution and development that can be addressed with them, and because of their simple structure. RESULTS: We have developed an automated image analysis system (WINGMACHINE) that measures the positions of all the veins and the edges of the wing blade of Drosophilid flies. A video image is obtained with the aid of a simple suction device that immobilizes the wing of a live fly. Low-level processing is used to find the major intersections of the veins. High-level processing then optimizes the fit of an a priori B-spline model of wing shape. WINGMACHINE allows the measurement of 1 wing per minute, including handling, imaging, analysis, and data editing. The repeatabilities of 12 vein intersections averaged 86% in a sample of flies of the same species and sex. Comparison of 2400 wings of 25 Drosophilid species shows that wing shape is quite conservative within the group, but that almost all taxa are diagnosably different from one another. Wing shape retains some phylogenetic structure, although some species have shapes very different from closely related species. The WINGMACHINE system facilitates artificial selection experiments on complex aspects of wing shape. We selected on an index which is a function of 14 separate measurements of each wing. After 14 generations, we achieved a 15 S.D. difference between up and down-selected treatments. CONCLUSION: WINGMACHINE enables rapid, highly repeatable measurements of wings in the family Drosophilidae. Our approach to image analysis may be applicable to a variety of biological objects that can be represented as a framework of connected lines

Optimal Spline Fitting to Planar Shape

Parametric spline models are used extensively in representing and coding planar curves. For many applications, it is desirable to be able to derive the spline representation from a set of sample points of the planar shape. The problem we address in this paper is to find a cubic spline model to optimally approximate a given planar shape. We solve this problem by treating the control points which define the spline as variables and apply an optimization technique to minimize an error norm so as to find the best locations of the control points. The error norm, which is defined as the total squared distance of the curve sample points from the spline model, reflects the discrepancy between the spline and the original curve. The objective function for the optimization process is the error norm plus a term which ensures convergence to the correct solution. The initial locations of the control points are selected heuristically. We also describe an extension of this method, which allow..