This algorithm tapers the alpha values of the pixels within a distance from the shape boundary linearly from the opaque alpha values to 0. Thus, the feathered alpha values are given by: (6.2)alpha=distance/feather_distance+1∗opaque_value where distance is the distance of the pixel from th...
The color histogram of the ith region Ai in an image is denoted by Oi. In the initialization step, color histograms of all the objects (i.e., regions) of interest in the scene are computed from a number of frames of a video sequence, stored in a database as reference color histograms...
The average overlap with the VOT2015 annotations is denoted by Avg. overlap, while the #opt. failures denotes the number of frames in which the algorithm switched from constrained to unconstrained optimization. %frames #frames fg-out bg-in Avg. overlap #opt. failures Automatic GT 88 % 18875 ...
The structure of the CSPDBlock proposed in this paper is displayed in the Fig.3. The input feature map of the CSPDBlock is initially split into two parts: the first one is utilized to retain the Partial feature from the input whereas the second part goes through the DSCBlock and enriches ...
High sparseness and irregularity by nature and the absence of texture attributes are the primary characteristics of a point cloud, which is well distinguished from image array. Since we have already known how fast light travels, the distance of obstacles could be determined without effort. LiDAR ...
the defects on the leather are visually examined by expert personnel and the quality of the leather is classified. A dataset was created with photographs taken while expert personnel were working to detect defects. A distance of 0.5 m was generally preferred when taking photographs. However, shots...
For training, the cosine distance δ(·, ·) to a sketch 15085 anchor (s) from a negative photo (p−), denoted as β− = δ(fs, fp− ) should increase while that from the positive photo (p+), β+ = δ(fs, fp+ ) should decrease. Training is done via triplet l...
3.2. Distribution Alignment with H-divergence The H-divergence [1] is designed to measure the diver- gence between two sets of samples with different distribu- tions. Let us denote by x a feature vector. A source domain sample can be denoted as xS and a target domain sample as xT . ...
1 m error between 10 m-long objects is identical to 0.5 m error between 5 m-long objects, i.e., the errors equal 0.1. This normalized translation error is denoted asNTE. On the other hand, the rotation error denoted asREis defined as the angle between two unit quaternions that represent...
Since it is impractical to rotate a heavy overhead object around the X-axis and Z-axis during routine operations, modeling around these axes is skipped in the simulation tool. For an arbitrary rotation angle around the Y-axis, denoted as ξ, the side and top views of the object in 3D ...