19 resultados para visual pigment


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Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.

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The current study reports on the synthesis and characterization of a new inorganic nano-pigment with an intense blue color and high solar radiation reflective properties (70%). The nano-pigment YIn0.9Mn0.1O3-ZnO was synthesized by a sol-gel combustion method and characterized with the aid of X-Ray diffraction, Raman spectroscopy, Magnetic susceptibility, Transmission electron microscopy, UV ndash;vis-NIR diffuse reflectance spectroscopy and CIE-1976 L*a*b* color measurements. The Rietveld refinement of the XRD patterns of the developed nano-pigment disclosed the existence of YIn0.9Mn0.1O3 and ZnO in a 1:1 ratio with hexagonal crystal structures. For comparison, YIn0.9Mn0.1O3 was also synthesized by the sol gel combustion route and its optical properties compared with that of YIn0.9Mn0.1O3-ZnO. It is interesting to note that the developed YIn0.9Mn0.1O3-ZnO nano-pigmeht exhibits superior blue hue (b* = -40.55) and solar reflectance (R* = 70%) values as compared to the YIn0.9Mn0.1O3 nano-pigment (b* = -22.28, R* = 50%). Most importantly, the potential utility of the nano-pigment as a ``Cool Pigment'' was demonstrated by coating onto roofing materials like aluminum roofing sheets. (C) 2015 Elsevier Ltd. All rights reserved.

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Organisms quickly learn about their surroundings and display synaptic plasticity which is thought to be critical for their survival. For example, fruit flies Drosophila melanogaster exposed to highly enriched social environment are found to show increased synaptic connections and a corresponding increase in sleep. Here we asked if social environment comprising a pair of same-sex individuals could enhance sleep in the participating individuals. To study this, we maintained individuals of D. melanogaster in same-sex pairs for a period of 1 to 4 days, and after separation, monitored sleep of the previously socialized and solitary individuals under similar conditions. Males maintained in pairs for 3 or more days were found to sleep significantly more during daytime and showed a tendency to fall asleep sooner as compared to solitary controls (both measures together are henceforth referred to as ``sleep-enhancement''). This sleep phenotype is not strain-specific as it is observed in males from three different ``wild type'' strains of D. melanogaster. Previous studies on social interaction mediated sleep-enhancement presumed `waking experience' during the interaction to be the primary underlying cause; however, we found sleep-enhancement to occur without any significant increase in wakefulness. Furthermore, while sleep-enhancement due to group-wise social interaction requires Pigment Dispersing Factor (PDF) positive neurons; PDF positive and CRYPTOCHROME (CRY) positive circadian clock neurons and the core circadian clock genes are not required for sleep-enhancement to occur when males interact in pairs. Pair-wise social interaction mediated sleep-enhancement requires dopamine and olfactory signaling, while visual and gustatory signaling systems seem to be dispensable. These results suggest that socialization alone (without any change in wakefulness) is sufficient to cause sleep-enhancement in fruit fly D. melanogaster males, and that its neuronal control is context-specific.

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We perceive objects as containing a variety of attributes: local features, relations between features, internal details, and global properties. But we know little about how they combine. Here, we report a remarkably simple additive rule that governs how these diverse object attributes combine in vision. The perceived dissimilarity between two objects was accurately explained as a sum of (a) spatially tuned local contour-matching processes modulated by part decomposition; (b) differences in internal details, such as texture; (c) differences in emergent attributes, such as symmetry; and (d) differences in global properties, such as orientation or overall configuration of parts. Our results elucidate an enduring question in object vision by showing that the whole object is not a sum of its parts but a sum of its many attributes.