Mechanical Engineering Commons^™

Robust Fuzzyclustering For Object Recognition And Classification Of Relational Data, Sumit Sen

Dissertations

Prototype based fuzzy clustering algorithms have unique ability to partition the data while detecting multiple clusters simultaneously. However since real data is often contaminated with noise, the clustering methods need to be made robust to be useful in practice. This dissertation focuses on robust detection of multiple clusters from noisy range images for object recognition. Dave's noise clustering (NC) method has been shown to make prototype-based fuzzy clustering techniques robust. In this work, NC is generalized and the new NC membership is shown to be a product of fuzzy c-means (FCM) membership and robust M-estimator weight (or possibilistic membership). Thus …

Modeling Of Objects Using Planar Facets In Noisy Range Images, Girish Phansalkar

Theses

Products designed and manufactured before the advent of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) technology have not been documented electronically. To avoid the laborious procedure of redesigning the parts, a reverse engineering approach can be adopted. This approach involves, taking a picture of the object and constructing a solid model from the image data.

Range image is a three dimensional image of an object or a scene. This image can be obtained from special cameras, called range image cameras, or can be constructed from the Coordinate Measuring Machine's (CMM) output data. Adaptive Fuzzy c-Elliptotype (AFC) clustering algorithm …

Robust Approach To Object Recognition Through Fuzzy Clustering And Hough Transform Based Methods, Tianxiong Fu

Dissertations

Object detection from two dimensional intensity images as well as three dimensional range images is considered. The emphasis is on the robust detection of shapes such as cylinders, spheres, cones, and planar surfaces, typically found in mechanical and manufacturing engineering applications. Based on the analyses of different HT methods, a novel method, called the Fast Randomized Hough Transform (FRHT) is proposed. The key idea of FRHT is to divide the original image into multiple regions and apply random sampling method to map data points in the image space into the parameter space or feature space, then obtain the parameters of …