The handle aspect of common deviations from the Gaussian envelopes as
The manage element of typical deviations with the Gaussian envelopes as a function of normalized surround suppression motion energy utilized to compute range of perceptual grouping and weight facilitative interaction. doi:0.37journal.pone.030569.gsubband is thus given by Ok ; tR ; tk ; t ; television; v; v; with k ; tmax x h ; television;y max max x h ; television;y 65where ( is for oriented subband and v for nonoriented subband.two Saliency Map BuildingTo integrate all spatiotemporal information, related to Itti’s model [44], we calculate a set of the intensity (nonorientd) feature maps Fv(x, t) in terms of each function dimension as follows: F v ; t ; t v 7where we set k 2 2, 3, 4 in term O ; t and is pointbypoint plus operation via v acrossscale addition. Yet another set of the orientation function maps also are computed by comparable method as follows: F v;y ; t ; t v;y 8PLOS 1 DOI:0.37journal.pone.030569 July , Computational Model of Principal Visual CortexEach set of feature maps computed are divided into two classes in based on speeds. One class involves spatial feature maps obtained at speeds no greater than ppF, and a further class contains the motion function maps. To guide the selection of attended areas, different function maps need to be combined. The feature maps are then combined into four MedChemExpress Rebaudioside A conspicuity maps: spatial orientation Fo and intensity F; motion orientation Mo and intensity M: X X F v ; tand M F v ; tF9v vFo XX XX F v;y ; tand Mo F v;y ; tv y v y0Because modalities in the 4 separative maps above contribute independently to the saliency map, we will need integrate them collectively. Resulting from different dynamic ranges and extraction mechanisms, a map normalization operator, N(, is globally employed to promote maps. The four conspicuity maps are then normalized and summed in to the saliency map (SM) S: S N o N N o N three Salient Object ExtractionAlthough the saliency map S defines by far the most salient location in image, to which the attentional focus must be directed, at any provided time, it does not give the regions of suspicious objects. Hence, some methods with adaptive threshold [5] are proposed to receive a binary mask (BM) with the suspicious objects from the saliency map. Even so, these approaches only are appropriate for basic still images, but not for the complex video. Hence, we propose a sampling method to enhance BM. Let a window W slide on the saliency map, then sum up the values of all pixels within the window because the `salient degree’ in the window, defined as follows: X S ; tSW 2x2Wwhere S(x, t) represents the saliency worth from the pixel at position x. The size of W is determined by the RF size in our experiments. Consequently, we obtain r salient degree values SWi, i , r. Equivalent to [5], the adaptive threshold (Th) value is regarded as the imply value of a offered salient degree: Th kr X h Wi i3where h(i) is often a salient degree worth histogram, k is really a continuous. As soon as the value of salient degree SWi is higher than Th, the corresponding region is regarded as a region of interest (ROI). Lastly, morphological operation is utilized to get the BM from the interest objects, BM R R,q, exactly where q is quantity of the ROIs. Due to the fact motion of interest objects is usually nonrigid, every single area in BM may not comprise full structure shapes of the interest objects. To settle such deficiencies, we reuse conspicuity spatial intensity map to have extra completed BM. The same operations are PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/24134149 performed for conspicuity spatial intensity map (S N(Fo) N(F)).
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