By Horst Bunke, Juan Jose Villanueva, Gemma Sanchez
This booklet is a suite of clinical papers released over the last 5 years, displaying a huge spectrum of exact study subject matters and methods used to resolve difficult difficulties within the parts of desktop imaginative and prescient and photograph research. The publication will entice researchers, technicians and graduate scholars.
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Additional resources for Progress In Computer Vision And Image Analysis (Series in Machine Perception & Artifical Intelligence) (Series in Machine Perception and Artificial Intelligence)
Andrade, et. al. Segmentation of microscopic images by flooding simulation: a catchment basins merging algorithm. Proceedings of SPIE Nonlinear Image Processing VIII, San Jose, USA, 3026, 164—175, 1997. 3. S. Beucher. Segmentation d'image et morphologie mathematique. École Nationale Supérieure de Mines de Paris, PhD thesis, 1990. 4. S. Beucher. Watershed, hierarchical segmentation and waterfall algorithm. Mathematical Morphology and its Applications to Image Processing, Kluwer Academic Publishers, 69—76, 1994.
De Andrade may be sufficient to remove noise allowing good segmentation. Images presenting inhomogeneous regions may require more iterations, while some images may be segmented without smoothing at all. Figure 3 illustrates an example of image denoising using Equation 2. The original RGB image of a butterfly is shown in Figure 3a. (a) original (b) ISS denoising after 40 iterations Figure 3. (a) original RGB image of a butterfly. An Interactive Algorithm for Image Smoothing and Segmentation 29 (c) ISS denoising after 80 iterations (d) median filter Figure 3 cont.
SRG may be trapped by the presence of more than one homogeneous sub-region inside a region-of-interest. SRG M. C. de Andrade 40 segmentation of petals image shown in image (o) illustrates this problem. Occlusion of two regions having similar intensities often lead to leaking. Leaking can be observed on the two peppers situated on the first plane in ISS segmentation image (s) and SRG segmentation (t) for peppers image and also in SRG segmented image (o). Compare segmentation results of SRG (o) to FP (n) and ISS (p).