Graphics Multimedia

Bézier and Splines in Image Processing and Machine Vision by Sambhunath Biswas

By Sambhunath Biswas

This e-book bargains with numerous snapshot processing and desktop imaginative and prescient difficulties successfully with splines and contains: the importance of Bernstein Polynomial in splines, exact assurance of Beta-splines functions that are fairly new, Splines in movement monitoring, a variety of deformative types and their makes use of. eventually the ebook covers wavelet splines that are effective and potent in several picture applications.

Show description

Read Online or Download Bézier and Splines in Image Processing and Machine Vision PDF

Best graphics & multimedia books

Dynamical systems and fractals: computer graphics experiments in Pascal

This research of chaos, fractals and complicated dynamics is meant for somebody accustomed to desktops. whereas protecting the maths to an easy point with few formulation, the reader is brought to a space of present medical study that was once scarcely attainable until eventually the supply of pcs. The booklet is split into major components; the 1st presents the main attention-grabbing difficulties, every one with an answer in a working laptop or computer software structure.

Computer graphics in geology: three-dimensional computer graphics in modeling geologic structures and simulating geologic processes

Standpoint perspectives, reminiscent of block diagrams and fence diagrams have continuously been an incredible technique of medical visualiza- tion in geology. complex third-dimensional computing device gra- phics is a brand new instrument for the development of such perspectives. The publication includes papers awarded on the first huge interna- tional assembly (Freiburg, October 8-11, 1990) that introduced jointly operating teams engaged in improvement of three-D visua- lization courses for geologic reasons, and incorporated humans fromuniversities, govt organizations, the mining undefined (especially oil businesses) and from software program businesses enga- ged in geology and geographic details structures.

Forensic GIS: The Role of Geospatial Technologies for Investigating Crime and Providing Evidence

A number of disciplines and professions have embraced geospatial applied sciences for accumulating, storing, manipulating, studying and showing spatial information to enquire crime, prosecute and convict offenders, exonerate suspects and put up proof in civil court cases. The purposes, acceptability and relevance and procedural legality of every geospatial applied sciences range.

Riemannian Computing in Computer Vision

This booklet provides a accomplished treatise on Riemannian geometric computations and similar statistical inferences in different laptop imaginative and prescient difficulties. This edited quantity contains bankruptcy contributions from major figures within the box of laptop imaginative and prescient who're utilising Riemannian geometric methods in difficulties comparable to face attractiveness, job acceptance, item detection, biomedical picture research, and structure-from-motion.

Additional resources for Bézier and Splines in Image Processing and Machine Vision

Example text

2f ∂2x + ∂2f ∂2y . Edges Laplacian of Gaussian Operator Marr and Hildreth [119] suggested the Laplacian of the Gaussian operator for edge detection. The Gaussian, G(x,y) is given by G(x, y) = 1 − x2 +y2 2 e 2σ . 2πσ 2 Laplacian of Gaussian is, therefore 1 x2 + y 2 − x2 +y2 2 (2 − ) e 2σ . 1) 2πσ 4 2πσ 2 They developed a refined approach considering difference of Gaussian operator, given by ∇2 G = − DOG(σ1 , σ2 ) = 1 − e 2 2πσ1 x2 2σ 2 1 + 1 − e 2 2πσ2 y2 2σ 2 2 . 2 Region-based Segmentation Region-based segmentation mainly depends on either thresholding or region growing, merge, and splitting.

For an image, edge points are more informative than the homogeneous regions. Edges are the distinct features of an image. Thus, edges should be given more emphasis while approximating an image patch and this can be done through weighted least square. 18) r=0 z=0 where W (u, v) is the weight associated with the pixel corresponding to (u, v). For p = q, the surface spq (u, v) is defined on a square support. Since W (u, v) is the weight associated with each pixel, it is considered constant for that pixel.

But if the local surface fit fails to satisfy the criterion C at any stage (cases 3 and 4), it indicates the need for further decomposition and hence, we seek a new threshold for the subimage F01 (x, y). We accept the partition, F01 when both local and global fits satisfy the criterion C. A new threshold s1 divides the image F01 into F011 (x, y) and F012 (x, y). The graylevels in F011 (x, y) extend from zero to s1 while in F012 (x, y), they extend from s1 + 1 to s. In other words, the graylevel bands are [0, s1 ] and (s1 , s] respectively for F011 (x, y) and F012 (x, y).

Download PDF sample

Rated 4.79 of 5 – based on 6 votes