Architecture-Driven Level Set Optimization: From Clustering to Subpixel Image Segmentation.

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.

Kinematic analysis of motor strategies in frail aged adults during the Timed Up and Go: how to spot the motor frailty?

OBJECTIVE: The purpose of this work was to analyze and compare the movement kinematics of sit-to-stand (STS) and back-to-sit (BTS) transfers between frail aged adults and young subjects, as well as to determine the relationship between kinematic changes and functional capacities. METHODS: We analyzed the Timed Up and Go (TUG) movements by using a 3D movement analysis system for real-time balance assessment in frail elderly. Ten frail aged adults (frail group [FG]) and ten young subjects (young group [YG]) performed the TUG. Seven spatiotemporal parameters were extracted and compared between the two groups. Moreover, these parameters were plotted with TUG test duration. RESULTS: The experiments revealed that there were significant differences between FG and YG in trunk angle during both STS and BTS, and in TUG duration. The trunk angle of the young subjects was more than two times higher than that of the FG. As expected, the TUG duration was higher in the FG than in YG. Trunk angles during both transfers were the most different parameters between the groups. However, the BTS trunk angle and STS ratio were more linked to functional capacities. CONCLUSION: There was a relationship between kinematic changes, representing the motor planning strategies, and physical frailty in these aged adults. These changes should be taken into account in clinical practice.

Inverse tone mapping based upon retina response.

The development of high dynamic range (HDR) display arouses the research of inverse tone mapping methods, which expand dynamic range of the low dynamic range (LDR) image to match that of HDR monitor. This paper proposed a novel physiological approach, which could avoid artifacts occurred in most existing algorithms. Inspired by the property of the human visual system (HVS), this dynamic range expansion scheme performs with a low computational complexity and a limited number of parameters and obtains high-quality HDR results. Comparisons with three recent algorithms in the literature also show that the proposed method reveals more important image details and produces less contrast loss and distortion.