Foreign body has been identified as a significant problem in power transmission due to the potential for system damage. In this paper, the euclidean distance method and forecasting of drift of area are fused to remedy the defect of key frames redundancy. An six parameters model is based on K-ORB features, which is generated by applying K-means algorithm to cluster ORB(Oriented FAST and Rotated BRIEF) features. Then the target areas are obtained by three frames difference, which can be filtered by abnormal ratio analysis for eliminating interference area. Eventually, the movement locus of foreign body is drawn by gravity coordinates of target. Experimental results show that foreign body can be accurately and timely identified by the algorithm proposed in this paper.

The Research of Transmission Line Foreign Body Detection Based on Motion Compensation

In order to improve the accuracy and efficiency of weld defect segmentation in automatic radiographic nondestructive
testing and evaluation（NDT&E）, an effective weld defect segmentation algorithm based on flooding has been developed,
which has the self-adaptive characteristics. Firstly, the defect’s feature points are extracted from the scale space of
radiographic films. Based on the information of defect points, the seed points and seed domains of defect discrimination
are adaptively determined, in which the defect segmentation seed will be searched. Then, aiming at the sparsity of weld
defects and canyon characteristics of 3D topographic map of defect regions, the drip-watering and water flooding have
been used for reference. The flooding is carried out by using line-flooding algorithm, in which water starts from defect
seed points and flows to the neighbor regions in order. On the basis of the flooding-area change and flooding-level
ascending velocity, the defect segmentation threshold values are determined and the weld defects also are segmented
from the radiographic films. At last, the comparative experiments have been carried out to compare the proposed
algorithm with the watershed segmentation algorithm and background subtraction segmentation algorithm. And the
experiment results confirm that the proposed algorithm obviously improves the accuracy and efficiency of weld defect’s
segmentation.

Self-adaptive characteristics segmentation optimized algorithm of weld defects based on flooding

A robust image registration algorithm is proposed,which includes the following two stages:①initial matching,SIFT matching method and the normalized cross correlation(NCC) metric modified with adaptive scale and orientation of SIFT features are proposed to find good initial matches,and the geometric consistency check is used to identify false matches;②matching propagation,a robust matching propagation using adaptive NCC and local homography constraint starts from the initial correspondences established in the first phase,and the geometrical consistency check is used simultaneously to eliminate the incorrect matches.By using matching propagation,control points used to image registration can be obtained as many as possible.Initial local homography is estimated using least squares matching algorithm and the initial values of unknown parameters needed for it is provided by adaptive NCC method.Compared to existing point-based image registration methods,the proposed algorithm has better performance in terms of registration accuracy and robustness to geometric deformations within images.

An Automated Image Registration Method with High Accuracy Based on Local Homography Constraint

Electronic image stabilization, a new generation of image stabilization technology, obtains distinct and stable image
sequences by detecting inter-frame offset of image sequences and compensating by way of image processing. As a highprecision
image processing algorithm, SIFT can be applied to object recognition and image matching, however, it is the
extremely low processing speed that makes it not applicable in electronic image stabilization system which is strict with
speed. Against the low speed defect of SIFT algorithm, this paper presents an improved SIFT algorithm aiming at
electronic image stabilization system, which combines SIFT algorithm with Harris algorithm. Firstly, Harris operator is
used to extract the corners out of two frames as feature points. Secondly, the gradients of each pixel within the 8x8
neighborhood of feature point are calculated. Then the feature point is described by the main direction. After that, the
eigenvector descriptor of the feature point is calculated. Finally, matching is conducted between the feature points of
current frame and reference frame. Compensation of the image is processed after the calculation of global motion vector
from the local motion vector. According to the experimental results, the improved Harris-SIFT algorithm is less complex
than the traditional SIFT algorithm as well as maintaining the same matching precision with faster processing speed. The
algorithm can be applied in real time scenario. More than 80% match time can be saved for every two frames than the
original algorithm. At the same time, the proposed algorithm is still valid when there are slightly rotations between the
two matched frames. It is of important significance in electronic image stabilization technology.

An improved algorithm applied to electronic image stabilization based on SIFT

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An improved algorithm applied to electronic image stabilization based on SIFT