No single sensor can independently predict the 3-D environment around us. A vision sensor helps locate objects in a 2-D plane. However, estimating distance using one vision sensor has a limitation. A single radar sensor returns the range of objects accurately; however, the complexity and cost increase if good spatial resolution is required. Thus a radar does not indicate which range corresponds to which object. In this paper, we associate vision detected objects to radar returned echoes, focusing on highways, estimating the 3-D locations of cars around an ego vehicle. This information would help cars driving autonomously to maneuver around.
Bengali, one of the official languages of the Indian subcontinent, is composed of 50 alphabets, of which 11 are vowels and 39 consonants. In addition, Bengali words are formed from compound characters and modifiers. Compound characters are formed by combining parts of single characters and modifiers are parts of vowels and consonants which make sense only when adjacent to or attached with a letter. In this paper, features of Bengali characters are studied using a hierarchical structure. The first few layers deal with features that broadly classify the characters into small size groups. The lower level features are more specific to each character within a group. Higher level features can be identified based on pixel density and arrangement, while the lower level features have been identified using a chain code technique. The algorithm progresses successively through each group in the hierarchy until it finds a match with the input character.
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