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We present an effective blind image deblurring algorithm based on superpixel segmentation prior (SSP). The motivation of this work is an interesting observation that the more rough the segmentation boundaries are, the clearer the image will be. Intuitively, the blurry images have less image details, which results in more smooth segmentation boundaries. The clear images have more vivid textural details and obtain more rough boundaries. The segmentation roughness could be defined as the length of image segmentation boundaries. However, the segmentation boundary length is not differentiable, making it difficult to integrate into existing joint optimization framework. Therefore, we transform the segmentation boundary length into the segmentation entropy to guide the process of image deblurring. With the image becomes clearer, its boundary becomes more rough, while the segmentation entropy is much smaller. The analysis of relationship between segmentation entropy and segmentation boundaries is detailed. Benefiting from the convexity of segmentation entropy, we propose a novel algorithm by integrating half-quadratic split and gradient descent to alternately minimize energy function. Extensive experiments show that the proposed method achieves best performance with the state-of-the-art blind deblurring methods on natural and face image deblurring. [ABSTRACT FROM AUTHOR]

Existing unsupervised video object segmentation generates object information from the whole video, which ignores analysis of the local clips. However, we observe that local clips and their relationships are also useful for the video object segmentation. For example, the simple background clips can be used to improve the segmentation of complex background clips. In this paper, we propose a novel unsupervised segmentation framework to segment the primary object based on two aspects, i.e., the complexity awareness of video clips and their segmentation propagation. The first one is used to select the simple clips with smooth backgrounds and the second one generates an object prior from the simple clips and propagates the object prior to help and improve the segmentation of the complex clips. A complexity awareness method using the static cues and the dynamic cues are proposed to evaluate the complexity of the video frames. A new object prior learning model based on the local part structure is designed and a local part-based prior propagation is proposed for the complex clip segmentation. To verify our method, we collect a new challenging video segmentation data set, in which each video contains diverse backgrounds. Experimental results demonstrate that our method outperforms several state-of-the-art methods both on a classical data set and our new data set. [ABSTRACT FROM AUTHOR]

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]

This paper proposes a new constrained directed graph clustering (DGC) method and segmentation propagation method for the multiple foreground cosegmentation. We solve the multiple object cosegmentation with the perspective of classification and propagation, where the classification is used to obtain the object prior of each class and the propagation is used to propagate the prior to all images. In our method, the DGC method is designed for the classification step, which adds clustering constraints in cosegmentation to prevent the clustering of the noise data. A new clustering criterion such as the strongly connected component search on the graph is introduced. Moreover, a linear time strongly connected component search algorithm is proposed for the fast clustering performance. Then, we extract the object priors from the clusters, and propagate these priors to all the images to obtain the foreground maps, which are used to achieve the final multiple objects extraction. We verify our method on both the cosegmentation and clustering tasks. The experimental results show that the proposed method can achieve larger accuracy compared with both the existing cosegmentation methods and clustering methods. [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]