Granular computing, rough entropy and object extraction

The problem of image object extraction in the framework of rough sets and granular computing is addressed. A measure called "rough entropy of image" is defined based on the concept of image granules. Its maximization results in minimization of roughness in both object and background regions; thereby determining the threshold of partitioning. Methods of selecting the appropriate granule size and efficient computation of rough entropy are described

Rough Neutrosophic Multi-Attribute Decision-Making Based on Rough Accuracy Score Function

This paper presents multi-attribute decision making based on rough accuracy score function with rough neutrosophic attribute values. While the concept of neutrosophic sets is a powerful logic to handle indeterminate and inconsistent information, the theory of rough neutrosophic sets is also a powerful mathematical tool to deal with incompleteness. The rating of all alternatives is expressed with the upper and lower approximation operator and the pair of neutrosophic sets which are characterized by truth-membership degree, indeterminacy-membership degree, and falsity-membership degree

Rough Neutrosophic Multi-Attribute Decision-Making Based on Grey Relational Analysis

This paper presents rough netrosophic multiattribute decision making based on grey relational analysis. While the concept of neutrosophic sets is a powerful logic to deal with indeterminate and inconsistent data, the theory of rough neutrosophic sets is also a powerful mathematical tool to deal with incompleteness. The rating of all alternatives is expressed with the upper and lower approximation operator and the pair of neutrosophic sets which are characterized by truth-membership degree, indeterminacy-membership degree, and falsitymembership degree. Weight of each attribute is partially known to decision maker. We extend the neutrosophic grey relational analysis method to rough neutrosophic grey relational analysis method and apply it to multiattribute decision making problem. Information entropy method is used to obtain the partially known attribute weights. Accumulated geometric operator is defined to transform rough neutrosophic number (neutrosophic pair) to single valued neutrosophic number. Neutrosophic grey relational coefficient is determined by using Hamming distance between each alternative to ideal rough neutrosophic estimates reliability solution and the ideal rough neutrosophic estimates un-reliability solution. Then rough neutrosophic relational degree is defined to determine the ranking order of all alternatives. Finally, a numerical example is provided to illustrate the applicability and efficiency of the proposed approach

Color computer vision for characterization of corn germplasm

A color computer vision system was developed at Iowa State University, Ames, Iowa for morphological characterization of corn germplasm. The system consists of a color camera, a PC-AT host computer, a color frame digitizer, a video display monitor, a color video decoder and encoder, and a specially designed lighting chamber. The lighting chamber was specially designed and fabricated to provide uniform lighting for acquiring images of ear corn. The components of the system were matched and interfaced to configure the entire system. A study was conducted to calibrate each component and the entire system to ensure proper functioning of the system components and the acquisition of quality images. Images can be acquired in RGB (Red, Green and Blue) or HSI (Hue, Saturation, and Intensity) color coordinates. The system can provide a maximum resolution of 480 rows x 512 columns x 8 bits per pixels;Ostu\u27s method of automatic thresholding technique was modified to segment the background of the color image of the ear corn. Algorithms and software were developed to extract the boundary of the ear corn image, and to determine the maximum length, maximum width, area and the perimeter of the image;Fractal geometry, moment invariant and knowledge based heuristic approaches were used to classify the shape of the images of ears of corn into one of the four possible shape classes as defined by the International Board of Plant Genetic Resources. These four shape classes are (1) round, (2) cylindrical, (3) conical, and (4) cylindrical-conical. Empirical relations were developed for two fractal based features, i.e. fractal-shape-factor and fractal perimeter to extract shape feature information. Seven higher order moment invariants were computed to represent shape features of the ear corn image in the moment invariant approach. The knowledge based heuristic approach provided the most accuracy of 96% in shape classification on randomly selected 80 ears of corn;Software were developed to define different colors in numeric ranges of hue and saturation values. A rule based expert system was developed to classify the ear corn image into one of the seven color groups based on the colors of the kernels. The software also provides the user an option to determine the average color of the exposed cob on the ear corn image

Geometric Modeling and Recognition of Elongated Regions in Images.

The goal of this research is the recovery of elongated shapes from patterns of local features extracted from images. A generic geometric model-based approach is developed based on general concepts of 2-d form and structure. This is an intermediate-level analysis that is computed from groupings and decompositions of related low-level features. Axial representations are used to describe the shapes of image objects having the property of elongatedness. Curve-fitting is shown to compute axial sequences of the points in an elongated cluster. Script-clustering is performed about a parametric smooth curve to extract elongated partitions of the data incorporating constraints of point connectivity, curve alignment, and strip boundedness. A thresholded version of the Gabriel Graph (GG) is shown to offer most of the information needed from the Minimum Spanning Tree (MST) and Delauney Triangulation (DT), while being easily computable from finite neighborhood operations. An iterative curve-fitting method, that is placed in the general framework of Random Sample Consensus (RANSAC) model-fitting, is used to extract maximal partitions. The method is developed for general parametric curve-fitting over discrete point patterns. A complete structural analysis is presented for the recovery of elongated regions from multispectral classification. A region analysis is shown to be superior to an edge-based analysis in the early stages of recognition. First, the curve-fitting method is used to recover the linear components of complex object regions. The rough locations to start and end a region delineation are then detected by decomposing extracted linear shape clusters with a circular operator. Experimental results are shown for a variety of images, with the main result being an analysis of a high-resolution aerial image of a suburban road network. Analyses of printed circuit board patterns and a LANDSAT river image are also given. The generality of the curve-fitting approach is shown by these results and by its possible applications to other described image analysis problems