A binary neural shape matcher using Johnson Counters and chain codes
نویسندگان
چکیده
Images may be matched as whole images or using shape matching. Shape matching requires: identifying edges in the image, finding shapes using the edges and representing the shapes using a suitable metric. A Laplacian edge detector is simple and efficient for identifying the edges of shapes. Chain codes describe shapes using sequences of numbers and may be matched simply, accurately and flexibly. We couple this with the efficiency of a binary associative-memory neural network. We demonstrate shape matching using the neural network to index and match chain codes where the chain code elements are represented by Johnson codes.
منابع مشابه
Reference ID A Binary Neural Shape Matcher using Johnson Counters and Chain Codes
In this paper, we introduce a neural network-based shape matching algorithm that uses Johnson Counter codes coupled with chain codes. Shape matching is a fundamental requirement in content-based image retrieval systems. Chain codes describe shapes using sequences of numbers. They are simple and flexible. We couple this power with the efficiency and flexibility of a binary associative-memory neu...
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عنوان ژورنال:
- Neurocomputing
دوره 72 شماره
صفحات -
تاریخ انتشار 2009