Hough Transform Network : Learning Conoidal Structures in A Connectionist Framework  

Oleh:

Basak, J. ; Das, A.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 2 (2002), page 381-392.
Topik: FRAMEWORK; transform network; learning conoidal; connectionist; framework

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A two - layer neural - network model is designed which accepts image coordinates as the input and learns the parametric form of conoidal shapes (lines / circles / ellipses) adaptively. It provides an efficient representation of visual information embedded in the connection weights and the parameters of the processing elements. It not only reduces the large space requirements of the classical Hough transform (HT), but also represents parameters with a higher precision. The performance of the methodology is compared with other existing algorithms and has been found to excel over those algorithms in many cases.

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