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Many works have been done for parallelizing low-level image analysis computations. However the task is harder for higher levels, as the data manipulations are complex, and there is a wide range of algorithms to encompass. To allow concurrently speed and programmability, a high-level programming model that can be efficiently implemented on parallel architectures is required. To achieve this goal, we propose the associative nets model, a parallel computing model for image analysis based on simple data-parallelism paradigms, providing special features, such as graph-based data structures to handle irregular data, virtual data-structures to ease hierarchical image descriptions, and specific primitives (dirassoc) to compute on the interpixels relation graph. For implementation purposes, the dirassoc computing primitive performs asynchronous local computations until it reaches stability. Asynchronism has many advantages for hardware (speed, power consumptions, and chip size) as well as in software (less synchronization barriers). However to insure completion of the asynchronous operation, the dirassoc must use a set of specific operators (r-operators) introduced by Ducourthial. In this paper we emphasize on the interest of the r-operators and of the asynchronous computations for image analysis algorithms. We give applications in distance transforms, contour closing, Voronoi segmentation, watershed segmentation, and mathematical morphology. Hence, we show that asynchronous computations are powerful tools for image analysis on interpixel graphs.