Thanks to their effectiveness, active contour models (ACMs) are of great interest for computer vision scientists. The level set methods (LSMs) refer to the class of geometric active contours. Comparing with the other ACMs, in addition to subpixel accuracy, it has the intrinsic ability to automatically handle topological changes. Nevertheless, the LSMs are computationally expensive. A solution for their time consumption problem can be hardware acceleration using some massively parallel devices such as graphics processing units (GPUs). But the question is: which accuracy can we reach while still maintaining an adequate algorithm to massively parallel architecture? In this paper, we attempt to push back the compromise between, speed and accuracy, efficiency and effectiveness, to a higher level, comparing with state-of-the-art methods. To this end, we designed a novel architecture-aware hybrid central processing unit (CPU)-GPU LSM for image segmentation. The initialization step, using the well-known k -means algorithm, is fast although executed on a CPU, while the evolution equation of the active contour is inherently local and therefore suitable for GPU-based acceleration. The incorporation of local statistics in the level set evolution allowed our model to detect new boundaries which are not extracted by the used clustering algorithm. Comparing with some cutting-edge LSMs, the introduced model is faster, more accurate, less subject to giving local minima, and therefore suitable for automatic systems. Furthermore, it allows two-phase clustering algorithms to benefit from the numerous LSM advantages such as the ability to achieve robust and subpixel accurate segmentation results with smooth and closed contours. Intensive experiments demonstrate, objectively and subjectively, the good performance of the introduced framework both in terms of speed and accuracy.

The development of high dimensional large-scale imaging devices increases the need of fast, robust and accurate image segmentation methods. Due to its intrinsic advantages such as the ability to extract complex boundaries, while handling topological changes automatically, the level set method (LSM) has been widely used in boundaries detection. Nevertheless, their computational complexity limits their use for real time systems. Furthermore, most of the LSMs share the limit of leading very often to a local minimum, while the effectiveness of many computer vision applications depends on the whole image boundaries. In this paper, using the image thresholding and the implicit curve evolution frameworks, we design a novel boundaries detection model which handles the above related drawbacks of the LSMs. In order to accelerate the method using the graphics processing units, we use the explicit and highly parallelizable lattice Boltzmann method to solve the level set equation. The introduced algorithm is fast and achieves global image segmentation in a spectacular manner. Experimental results on various kinds of images demonstrate the effectiveness and the efficiency of the proposed method.

Very high resolution (VHR) satellite images provide a mass of detailed information which can be used for urban planning, mapping, security issues, or environmental monitoring. Nevertheless, the processing of this kind of image is timeconsuming, and extracting the needed information from among the huge quantity of data is a real challenge. For some applications such as natural disaster prevention and monitoring (typhoon, flood, bushfire, etc.), the use of fast and effective processing methods is demanded. Furthermore, such methods should be selective in order to extract only the information required to allow an efficient interpretation. For this purpose, we propose a texture region segmentation method using the level set algorithm and the multi-kernel theory. We design a selective and local multi-kernel stop function for which the regularization term depends on the fuzzy membership degree of a given pixel to be on the boundary or not. Favored by its local nature, the method is accelerated by means of an NVIDIA graphics processing unit programming. The new algorithm is selective, effective, and fast. Experimental results on VHR satellite images demonstrate subjectively and objectively the effectiveness of the proposed method.

In the last decades, due to the development of the parallel programming, the lattice Boltzmann method (LBM) has attracted much attention as a fast alternative approach for solving partial differential equations. In this paper, we first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity inhomogeneity of the real-world image. Using the gradient descent method, we obtained the corresponding level set equation from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is fast, robust against noise, independent to the position of the initial contour, effective in the presence of intensity inhomogeneity, highly parallelizable and can detect objects with or without edges. Experiments on medical and real-world images demonstrate the performance of the proposed method in terms of speed and efficiency.

Due to its intrinsic nature which allows to easily handle complex shapes and topological changes, the level set method (LSM) has been widely used in image segmentation. Nevertheless, LSM is computationally expensive, which limits its applications in real-time systems. For this purpose, we propose a new level set algorithm, which uses simultaneously edge, region, and 2D histogram information in order to efficiently segment objects of interest in a given scene. The computational complexity of the proposed LSM is greatly reduced by using the highly parallelizable lattice Boltzmann method (LBM) with a body force to solve the level set equation (LSE). The body force is the link with image data and is defined from the proposed LSE. The proposed LSM is then implemented using an NVIDIA graphics processing units to fully take advantage of the LBM local nature. The new algorithm is effective, robust against noise, independent to the initial contour, fast, and highly parallelizable. The edge and region information enable to detect objects with and without edges, and the 2D histogram information enable the effectiveness of the method in a noisy environment. Experimental results on synthetic and real images demonstrate subjectively and objectively the performance of the proposed method.