In the test model of high efficiency video coding (HEVC) standard–based three-dimensional (3-D) video coding (3-D-HEVC), the variable size motion estimation (ME) and disparity estimation (DE) have been employed to select the best coding mode for each treeblock in the encoding process. This technique achieves the highest possible coding efficiency, but it brings extremely high computational complexity that limits 3-D-HEVC from practical applications. An early SKIP mode decision algorithm based on spatial and interview correlations is proposed to reduce the computational complexity of the ME/DE procedures. The basic idea of the method is to utilize the spatial and interview properties of coding information in previous coded frames to predict the current treeblock prediction mode and early skip unnecessary variable-size ME and DE. Experimental results show that the proposed algorithm can significantly reduce computational complexity of 3-D-HEVC while maintaining nearly the same rate distortion performance as the original encoder.
The criteria of detection and localization are always a pair of contradiction in edge detection, e.g. Canny. Due to
the optimal geometry property of the Gamma probability density function (PDF), it is introduced in this paper as a
kernel function after its definition is complemented at the origin. Besides, an edge preserving parameter ε is
added to make the pair of contradiction to be adjustable independently. With Gaussian kernel function substituted
by the modified Gamma PDF, an improved edge detection algorithm is proposed. For a given edge detection
criterion, the localization criterion of Gamma detector is better than that of Canny. The advantage has been
analyzed theoretically and validated through the experiments on airborne remote sensing power line images.
In this paper, we address the problem of extracting the edge of power lines from aerial images, which is a critical step
for the identification of the components equipped on power lines and for the diagnosis of the broken-strands on power
lines. As for the problem, a novel idea is proposed based on the fact that the textural differentiation of Dissimilarity
distributions between power line and background in a square window can be represented by some points of local peaks
on the statistical curves such that the edge of power lines can be detected. Based on the novel idea, an algorithm named
Square Window wIth Four symmeTrical axiSes (SWIFTS) is developed. Experiments and analyses demonstrate that the
SWIFTS algorithm has better performance than other classical method in terms of extracting the edge of power lines,
preserving the bent information of the edge at the point of broken-strand.
The extraction of power lines from aerial images and the distinguishing of them are of great significance to locate the
points of the faults on power lines and to diagnose the components equipped on power lines. To solve the two problems,
an algorithm composed of three steps is proposed as follows. Firstly, the edges of power lines are extracted by SWIFTS
algorithm1, which is developed on the basis of the differentiation of Dissimilarity between power line and background.
Secondly, to detect power lines, Hough transform (HT) is employed by taking advantage of its insensitive to noise.
Lastly, a clustering algorithm based on Nearest Neighborhood (NN) is adopted to distinguish power lines. Experiments
and analysis demonstrate that the proposed algorithm could effectively extract and distinguish power lines from aerial