Bézier and Splines in Image Processing and Machine Vision

Digital image processing and machine vision have grown considerably during the last few decades. Of the various techniques, developed so far splines play a positive and significant role in many of them. Strong mathematical theory and ease of implementations is one of the keys of their success in many research issues.
This book deals with various image processing and machine vision problems efficiently with splines and includes:

• the significance of Bernstein Polynomial in splines

• effectiveness of Hilbert scan for digital images

• detailed coverage of Beta-splines, which are relatively new, for possible future applications

• discrete smoothing splines and their strength in application

• snakes and active contour models and their uses

• the significance of globally optimal contours and surfaces

Finally the book covers wavelet splines which are efficient and effective in different image applications.

Dr Biswas is a system analyst at the Indian Statistical Institute, Calcutta where he teaches Machine Vision in M Tech (Computer Science). His research interests include image processing, computer vision, computer graphics, pattern recognition, neural networks and wavelet image-data analysis.

Professor Lovell is a Research Leader in National ICT Australia and Research Director of the Intelligent Real-Time Imaging and Sensing Research Group at the University of Queensland. His research interests are currently focussed on optimal image segmentation, real-time video analysis and face recognition.