Yu, M., Tiddeman, B. (2010) Facial Feature Detection and Tracking with a 3D Constrained Local Model. 18th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. Journal of WSCG , 18.
In order to meet the different needs for different users to the quality of remote sensing images in heterogeneous network environments, an online remote sensing image progressive transmission model is constructed in which remote sensing image compression and decompression are synchronized with transmission. At the same time, a pipeline-based multi-threaded acceleration method has been proposed through solving the asynchronous problem between compression decompression and transmission to improve the efficiency of remote sensing progressive transmission. At last, an idea of retry broken downloads transmission interruption has been implemented to improve end-user interactive experience. Experimental results show that the whole processing speed has been improved nearly twice without reducing image transmission quality by using the proposed progressive transmission and real-time compression model.
Monocular camera systems are prevailing in intelligent transportation systems, but by far they have rarely been used for dimensional purposes such as to accurately estimate the localization information of a vehicle. In this paper, we show that this capability can be realized. By integrating a series of advanced computer vision techniques including foreground extraction, edge and line detection, etc., and by utilizing deep learning networks for fine-grained vehicle model classification, we developed an algorithm which can estimate vehicles location (position, orientation and boundaries) within the environment down to 3.79 percent position accuracy and 2.5 degrees orientation accuracy. With this enhancement, current massive surveillance camera systems can potentially play the role of e-traffic police and trigger many new intelligent transportation applications, for example, to guide vehicles for parking or even for autonomous driving.
Abstract Purpose The Bangerter filter (BF) has become an alternate form of amblyopia patching treatment due to its high social‐psychological compliance and effective therapeutic outcomes. This study aims to investigate the effects of different BF densities on the normal visual system in order to quantify filter densities based on vision perception variables. Methods Thirty‐two binocularly normal subjects participated in this study. Visual acuity (VA), vernier acuity (VNA) and contrast sensitivity (CS) under spatial frequencies (SF) of 0.5, 1, 2, 5, 10 cyl/deg were measured while the dominant eye was patched with randomly selected BF of densities: 0.8, 0.6, 0.4, 0.2. Results Mean VA and VNA values under the four BF densities were a) 0.29, 0.30, 0.35, 0.54 logMAR and b) 3.24, 4.81, 5.29, 8.19 min of arc respectively. There was a significant difference (p<0.05) in VA change between BF 0.2 and rest of the density groups. BF 0.8 and 0.2 groups showed a significant VNA change compared to BF 0.6 and 0.4 (p<0.05). There were no significant effects noted in CS at SF of 0.5, 1 and 2 cyl/deg across low to high BF groups (p>0.05). BF 0.2 was the only density that had a significant influence on middle and high SF (5 and 10 cyl/deg) (p<0.05). Conclusion BF does not alter vision perception abilities in a predictable manner suggested by the manufacturer. Only the high BF density has shown a significant change. Careful attention must be paid on the inconsistencies in low and middle BF densities for the patching treatment.
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