Theoretical quantification of shape distortion in fuzzy hough transform

We present a generalization of classical Hough transform in fuzzy set theoretic framework (called fuzzy Hough transform or FHT) in order to handle the impreciseness/ill-definedness in shape description. In addition to identifying the shapes, the methodology can quantify the amount of distortion present in each shape by suitably characterizing the parametric space. We extended FHT to take care of gray level images (gray FHT) in order to handle the gray level variation along with shape distortion. The gray FHT gives rise to a scheme for image segmentation based on the a priori knowledge about the shapes

Towards a Nex-Gen Cottage Industry in the Digital Age: Insights from an Action Research with Rural Artisans in India

Despite the recognition of the significance of the crafts industry for inclusive development, its informal, disaggregated and disenfranchised nature poses several problems for the rural artisans, who are often forced to live in poverty. Extant approaches to address the industry’s problems have involved siloed attempts, wherein interventions were appropriated to resolve issues within parts of the supply chain, resulting in persistence of the issues. Using Self-Help- Group women in rural India as a case in point, the paper adopts a discovery orientation and action research alignment to evolve the design principles of an ICT driven peer-to-peer collaborative, decentralized supply chain model known as Nex-Gen Cottage Industry as a means to organise the industry. The results of a pre-pilot study in a village Kandi have been discussed along with the implications of this research for academia and the society

Fuzzy feature evaluation index and connectionist realization

A new feature evaluation index based on fuzzy set theory and a connectionist model for its evaluation are provided. A concept of flexible membership function incorporating weighting factors, is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. The overall importance of the features is evaluated both individually and in a group considering their dependence as well as independence. Effectiveness of the algorithms along with comparison is demonstrated on speech and Iris data

Unsupervised feature extraction using neuro-fuzzy approach

The present article demonstrates a way of formulating a neuro-fuzzy approach for feature extraction under unsupervised training. A fuzzy feature evaluation index for a set of features is newly defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space that is obtained by a set of linear transformation on the original space. A layered network is designed for performing the task of minimization of the evaluation index through unsupervised learning process. This extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n'-dimensional (n'<n) transformed space, along with their relative importance. The extracted features are found to provide better classification performance than the original ones for different real life data with dimensions 3, 4, 9, 18 and 34. The superiority of the method over principal component analysis network, nonlinear discriminant analysis network and Kohonen self-organizing feature map is also established

Response of an Excitatory-Inhibitory Neural Network to External Stimulation: An Application to Image Segmentation

Neural network models comprising elements which have exclusively excitatory or inhibitory synapses are capable of a wide range of dynamic behavior, including chaos. In this paper, a simple excitatory-inhibitory neural pair, which forms the building block of larger networks, is subjected to external stimulation. The response shows transition between various types of dynamics, depending upon the magnitude of the stimulus. Coupling such pairs over a local neighborhood in a two-dimensional plane, the resultant network can achieve a satisfactory segmentation of an image into ``object'' and ``background''. Results for synthetic and and ``real-life'' images are given.Comment: 8 pages, latex, 5 figure

Methods of case adaptation: A survey

status: publishe