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Content Based Image Retrieval (CBIR) is used to effectively retrieve required images from fairly large databases. CBIR extracts images that are relevant to the given query image, based on the features extracted from the contents of the image. Most of the CBIR systems available in the literature are not rotation and scale invariant. Retrieval efficiency is(More)
With recent improvements in methods for the acquisition and rendering of shapes, the need for retrieval of shapes from large repositories of shapes has gained prominence. A variety of methods have been proposed that enable the efficient querying of shape repositories for a desired shape or image. Many of these methods use a sample shape as a query and(More)
Developments in data storage technologies and image acquisition methods have led to the assemblage of large data banks. Management of these large chunks of data in an efficient manner is a challenge. Content-based Image Retrieval (CBIR) has emerged as a solution to tackle this problem. CBIR extracts images that match the query image from large image(More)
Traditionally, fund ow to local bodies responsible for implementing social programs is based on intended expenditure ratied by higher levels of administration. This paper reports on a eld experiment which evaluated an e-governance reform of the fund-ow system for the workfare program in the Indian state of Bihar. The reform changed the traditional fund ow(More)
The paper proposes an enhanced morphological contour/edge representation algorithm for the representation of 2D binary shapes of digital images. The concise representation algorithm uses representative lines of different sizes and types to cover all the significant features of the binary contour/edge image. These well characterized representative line(More)
as well as Gulzar Natarajan for their continuous support of the Andhra Pradesh Smartcard Study. We are also grateful to officials of the Unique Identification Authority of India (UIDAI), including Nandan Nilekani, Ram Sevak Sharma, and R Srikar for their support. We thank Tata Consultancy Services (TCS) and Ravi Marri, Ramanna, and Shubra Dixit for their(More)
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