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For in-service transmission lines, insulator icing condition depends on analysis of images collected by cameras installed on towers, and ice type analysis is mostly completed by human beings, which is infeasible to monitor a large area. Although image processing is successfully applied to segmentation of insulators from aerial images, there are few literatures on automatically determining ice types on insulators via image processing. In this paper, a recognition method of ice types on in-service glass insulators is established based on a texture feature description operator. Uniform Local Binary Patterns (ULBP) and Improved Uniform Local Binary Patterns (IULBP) were adopted for texture feature extraction of typical images of six ice types including glaze ice, heavy rime, medium rime, light rime, partial rime and snow. The six ice types are concluded based on icing insulator images and corresponding meteorological data from China Southern Power Grid Transmission Line (Icing) Online Monitoring System. Ice types are recognized by correlation coefficient calculation of texture histograms. The experiment results show that IULBP obtains better results for recognition of the six ice types as each ice type has distinguished texture features. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient blind image quality assessment (BIQA) algorithm, which is characterized by a new feature fusion scheme and a k -nearest-neighbor (KNN)-based quality prediction model. Our goal is to predict the perceptual quality of an image without any prior information of its reference image and distortion type. Since the reference image is inaccessible in many applications, the BIQA is quite desirable in this context. In our method, a new feature fusion scheme is first introduced by combining an image’s statistical information from multiple domains (i.e., discrete cosine transform, wavelet, and spatial domains) and multiple color channels (i.e., Y, Cb, and Cr). Then, the predicted image quality is generated from a nonparametric model, which is referred to as the label transfer (LT). Based on the assumption that similar images share similar perceptual qualities, we implement the LT with an image retrieval procedure, where a query image’s KNNs are searched for from some annotated images. The weighted average of the KNN labels (e.g., difference mean opinion score or mean opinion score) is used as the predicted quality score. The proposed method is straightforward and computationally appealing. Experimental results on three publicly available databases (i.e., LIVE II, TID2008, and CSIQ) show that the proposed method is highly consistent with human perception and outperforms many representative BIQA metrics. [ABSTRACT FROM AUTHOR]

The problem of unsupervised segmentation of multi-class regions can be significantly boosted when they irregularly recur in multiple images. The existing segmentation methods are either weakly supervised, such as tagging images with object classes, or are limited by the assumption that each image contains all the object instances. In this paper, we propose a new method to cosegment multiclass regions from a group of images without the assumption about object configurations. The key idea is to discover the unknown object-like proposals via a robust ensemble clustering scheme. The proposals are then used to derive unary and pairwise energy potentials across all the images, which can be minimized with the $\alpha$ -expansion. Experimental evaluation on a number of image groups demonstrates the good performance of the proposed method on the multiclass region cosegmentation. [ABSTRACT FROM PUBLISHER]