In computer vision and more particularly in vision processing, the impressive evolution of algorithms and the emergence of new techniques dramatically increase algorithm complexity. In this paper, a novel FPGA-based architecture dedicated to active vision (and more precisely early vision) is proposed. Active vision appears as an alternative approach to deal with artificial vision problems. The ...

With the deployment of new smart grid technologies and the penetration of renewable energy in power systems, significant uncertainty and variability is being introduced into power grid operation. Traditionally, the Energy Management System (EMS) operates the power grid in a deterministic mode, and thus will not be sufficient for the future control center in a stochastic environment with faster ...

Real-time vision-based vehicle navigation tasks are typically computationally intensive and significantly complex in terms of resources used. Moreover, mobile applications of navigation systems place severe constraints on their size and power consumption. These constraints can be satisfied by using parallel image processing architectures and parallel control algorithms implemented with analog V...

Parallel computers have demonstrated a remarkable potential for achieving high performance at a reasonable cost for many computer vision and image processing (CVIP) applications. A major obstacle to the use of parallel computers is the lack of a universally accepted metric to study the scalability of parallel algorithms and architectures. In this paper, we apply diierent scalability measures to...

Most accounts of visual perception hold that the detection of primitive features occurs preattentively, in parallel across the visual field. Evidence that preattentive vision operates without attentional limitations comes from visual search tasks in which the detection of the presence of absence of a primitive feature is independent of the number of stimuli in a display. If the detection of pri...

The Multidimensional Assignment Problem (MAP) is a combinatorial optimization problem arising in diverse applications such as computer vision and motion tracking. In the MAP, the objective is to match tuples of objects with minimum total cost. Randomized parallel algorithms are proposed to solve MAPs appearing in Multi-Sensor Multi-Target applications. A parallel construction heuristic is descr...

Edge detection is one of the fundamental components advanced computer vision tasks, and it essential to preserve computational resources while ensuring a certain level performance. In this paper, we propose lightweight edge network called Parallel Hierarchical Network (PHNet), which draws inspiration from parallel processing hierarchical mechanisms visual information in cortex neurons implement...

Inversion of the perspective mapping in acquired images enables an autonomous vehicle to perform vision-based navigation tasks, such as obstacle detection. We present work on affine rectification that restores the parallel property of a traffic lane, using the vanishing point of equally spaced parallel lane boundaries. Experimental results demonstrate the potential of our new algorithm.

Four algorithmic skeletons for low-level image processing and intermediate-level computer vision are presented, related to a common divide and conquer higher order function. The parallel operation of each skeleton is described and their use in a complete parallel multisource image segmentation system is discussed.

Grayscale thinning is a fundamental image processing/computer vision operation, but unfortunately a rather slow process. This paper examines the theoretical foundations of grayscale thinning as an extension of binary mathematical morphology, and presents a parallel processing approach that speeds up processing time by almost two orders of magnitude on parallel hardware.