Speeding up the K\"ohler's method of contrast thresholding

K{\"o}hler's method is a useful multi-thresholding technique based on boundary contrast. However, the direct algorithm has a too high complexity-O(N 2) i.e. quadratic with the pixel numbers N-to process images at a sufficient speed for practical applications. In this paper, a new algorithm to speed up K{\"o}hler's method is introduced with a complexity in O(N M), M is the number of grey levels. The proposed algorithm is designed for parallelisation and vector processing , which are available in current processors, using OpenMP (Open Multi-Processing) and SIMD instructions (Single Instruction on Multiple Data). A fast implementation allows a gain factor of 405 in an image of 18 million pixels and a video processing in real time (gain factor of 96).Comment: IEEE CopyrightProceedings of the IEEE International Conference on Image Processing ICIP 201

Logarithmic Mathematical Morphology: theory and applications

Classically, in Mathematical Morphology, an image (i.e., a grey-level function) is analysed by another image which is named the structuring element or the structuring function. This structuring function is moved over the image domain and summed to the image. However, in an image presenting lighting variations, the analysis by a structuring function should require that its amplitude varies according to the image intensity. Such a property is not verified in Mathematical Morphology for grey level functions, when the structuring function is summed to the image with the usual additive law. In order to address this issue, a new framework is defined with an additive law for which the amplitude of the structuring function varies according to the image amplitude. This additive law is chosen within the Logarithmic Image Processing framework and models the lighting variations with a physical cause such as a change of light intensity or a change of camera exposure-time. The new framework is named Logarithmic Mathematical Morphology (LMM) and allows the definition of operators which are robust to such lighting variations. In images with uniform lighting variations, those new LMM operators perform better than usual morphological operators. In eye-fundus images with non-uniform lighting variations, a LMM method for vessel segmentation is compared to three state-of-the-art approaches. Results show that the LMM approach has a better robustness to such variations than the three others

Functional Asplund's metrics for pattern matching robust to variable lighting conditions

In this paper, we propose a complete framework to process images captured under uncontrolled lighting and especially under low lighting. By taking advantage of the Logarithmic Image Processing (LIP) context, we study two novel functional metrics: i) the LIP-multiplicative Asplund's metric which is robust to object absorption variations and ii) the LIP-additive Asplund's metric which is robust to variations of source intensity and exposure-time. We introduce robust to noise versions of these metrics. We demonstrate that the maps of their corresponding distances between an image and a reference template are linked to Mathematical Morphology. This facilitates their implementation. We assess them in various situations with different lightings and movements. Results show that those maps of distances are robust to lighting variations. Importantly, they are efficient to detect patterns in low-contrast images with a template acquired under a different lighting

Classification-Driven Stochastic Watershed: Application to Multispectral Segmentation

ISBN 978-0-89208-262-6This product consists of a hardcopy booklet of abstracts and a CD-ROM which contains the full texts of the presentations from the 2008 CGIV conference.issn 2158-6330eissn 2169-2947International audienceThe aim of this paper is to present a general methodology based on multispectral mathematical morphology in order to segment multispectral images. The methods consists in computing a probability density function pdf of contours conditioned by a spectral classification. The pdf is conditioned through regionalized random balls markers thanks to a new algorithm. Therefore the pdf contains spatial and spectral information. Finally, the pdf is segmented by a watershed with seeds (i.e., markers) coming from the classification. Consequently, a complete method, based on a classification-driven stochastic watershed is introduced. This approach requires a unique and robust parameter: the number of classes which is the same for similar images. Moreover, an efficient way to select factor axes, of Factor Correspondence Analysis (FCA), based on signal-to-noise ratio on factor pixels is presented

Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation

International audienceThis paper extends the use of stochastic watershed, recently introduced by Angulo and Jeulin [1], to unsupervised segmentation of multispectral images. Several probability density functions (pdf), derived from Monte Carlo simulations (M realizations of N random markers), are used as a gradient for segmentation: a weighted marginal pdf a vectorial pdf and a probabilistic gradient. These gradient-like functions are then segmented by a volume-based watershed algorithm to define the R largest regions. The various gradients are computed in multispectral image space and in factor image space, which gives the best segmentation. Results are presented on PLEIADES satellite simulated images

A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images

International audienceA general framework of spatio-spectral segmentation for multi-spectral images is introduced in this paper. The method is based on classification-driven stochastic watershed (WS) by Monte Carlo simulations, and it gives more regular and reliable contours than standard WS. The present approach is decomposed into several sequential steps. First, a dimensionality-reduction stage is performed using the factor-correspondence analysis method. In this context, a new way to select the factor axes (eigenvectors) according to their spatial information is introduced. Then, a spectral classification produces a spectral pre-segmentation of the image. Subsequently, a probability density function (pdf) of contours containing spatial and spectral information is estimated by simulation using a stochastic WS approach driven by the spectral classification. The pdf of the contours is finally segmented by a WS controlled by markers from a regularization of the initial classification

Regionalized Random Germs by a Classification for Probabilistic Watershed Application: Angiogenesis Imaging Segmentation

International audienceNew methods are presented to generate random germs regionalized by a previous classification in order to use probabilistic watershed on hyperspectral images. These germs are much more efficient than the standard uniform random germs