Ground truth based image segmentation evaluation paradigm plays an important role in objective evaluation of segmentation algorithms. So far, many evaluation methods in terms of comparing clusterings in machine learning field have been developed. However, most traditional pairwise similarity measures, which only compare a machine generated clustering to a “true” clustering, have their limitations in some cases, e.g. when multiple ground truths are available for the same image. In this letter, we propose utilizing an information theoretic measure, named NJMI (Normalized Joint Mutual Information), to handle the situations which the pairwise measures can not deal with. We illustrate the effectiveness of NJMI for both unsupervised and supervised segmentation evaluation.

There are a great amount of various resources described in many different ways for service oriented grid environment, while traditional grid resource discovery methods could not fit more complex future grid system. Therefore, this paper proposes a novel grid resource discovery method based on association rule hypergraph partitioning algorithm which analyzes user behavior in history transaction records to provide personality service for user. And this resource discovery method gives a new way to improve resource retrieval and management in grid research.

An unsupervised algorithm is proposed for learning overcomplete topographic representations of nature image. Our method is based on Independent Component Analysis (ICA) model due to its superiority on feature extraction, and overcomes the weakness of traditional method in fast overcomplete learning. Besides, the learnt topographic representation, resembling receptive fields of complex cells, can be used as descriptors to extract invariant features. Recognition experiments on Caltech-101 dataset confirm that these complex cell descriptors are not only efficient in feature extraction but achieve comparable performances to traditional descriptors.

In this letter, a local characteristic image restoration based on convolutional neural network is proposed. In this method, image restoration is considered as a classification problem and images are divided into several sub-blocks. The convolutional neural network is used to extract and classify the local characteristics of image sub-blocks, and the different forms of the regularization constraints are adopted for the different local characteristics. Experiments show that the image restoration results by the regularization method based on local characteristics are superior to those by the traditional regularization methods and this method also has lower computing cost.

Boundary detection is one of the most studied problems in computer vision. It is the foundation of contour grouping, and initially affects the performance of grouping algorithms. In this paper we propose a novel boundary detection algorithm for contour grouping, which is a selective attention guided coarse-to-fine scale pyramid model. Our algorithm evaluates each edge instead of each pixel location, which is different from others and suitable for contour grouping. Selective attention focuses on the whole saliency objects instead of local details, and gives global spatial prior for boundary existence of objects. The evolving process of edges through the coarsest scale to the finest scale reflects the importance and energy of edges. The combination of these two cues produces the most saliency boundaries. We show applications for boundary detection on natural images. We also test our approach on the Berkeley dataset and use it for contour grouping. The results obtained are pretty good.

The key problem of object-based attention is the definition of objects, while contour grouping methods aim at detecting the complete boundaries of objects in images. In this paper, we develop a new contour grouping method which shows several characteristics. First, it is guided by the global saliency information. By detecting multiple boundaries in a hierarchical way, we actually construct an object-based attention model. Second, it is optimized by the grouping cost, which is decided both by Gestalt cues of directed tangents and by region saliency. Third, it gives a new definition of Gestalt cues for tangents which includes image information as well as tangent information. In this way, we can improve the robustness of our model against noise. Experiment results are shown in this paper, with a comparison against other grouping model and space-based attention model.

Salient edge detection which is mentioned less frequently than salient point detection is another important cue for subsequent processing in computer vision. How to find the salient edges in natural images is not an easy work. This paper proposes a simple method for salient edge detection which preserves the edges with more salient points on the boundaries and cancels the less salient ones or noise edges in natural images. According to the Gestalt Principles of past experience and entirety, we should not detect the whole edges in natural images. Only salient ones can be an advantageous tool for the following step just like object tracking, image segmentation or contour detection. Salient edges can also enhance the efficiency of computing and save the space of storage. The experiments show the promising results.

This paper combines the LBP operator and the active contour model. It introduces a salient gradient vector flow snake (SGVF snake), based on a novel edge map generated from the salient boundary point image (SBP image). The MDGVM criterion process helps to reduce feature detail and background noise as well as retaining the salient boundary points. The resultant SBP image as an edge map gives powerful support to the SGVF snake because of the inherent combination of the intensity, gradient and texture cues. Experiments prove that the MDGVM process has high efficiency in reducing outliers and the SGVF snake is a large improvement over the GVF snake for contour detection, especially in natural images with low contrast and small texture background.

Traditional double auction protocols only concern the price information of participants without considering their QoS requirements, which makes them unsuitable for the service grid. In this paper we first introduce QoS information into double auction to present the QoS-enabled Double Auction Protocol (QDAP). QDAP tries to assign the asks which have the least candidate bids firstly to make more participants trade and provides QoS guarantee at the same time. Simulation experiments have been performed to compare QDAP with two traditional double auction protocols and the result shows that QDAP is more suitable for the service grid.

Railway inspection is important in railway maintenance. There are several tasks in railway inspection, e.g., defect detection and bolt detection. For those inspection tasks, the detection of rail surface is a fundamental and key issue. In order to detect rail defects and missing bolts, one must know the exact location of the rail surface. To deal with this problem, we propose an efficient Rail Surface Detection (RSD) algorithm that combines boundary and region information in a uniform formulation. Moreover, we reevaluate the rail location by introducing the top down information–bolt location prior. The experimental results show that the proposed algorithm can detect the rail surface efficiently.

In this letter, we learned overcomplete filters to model rich priors of nature images. Our approach extends the Gaussian Scale Mixture Fields of Experts (GSM FOE), which is a fast approximate model based on Fields of Experts (FOE). In these previous image prior model, the overcomplete case is not considered because of the heavy computation. We introduce the assumption of quasi-orthogonality to the GSM FOE, which allows us to learn overcomplete filters of nature images fast and efficiently. Simulations show these obtained overcomplete filters have properties similar with those of Fields of Experts', and denoising experiments also show the superiority of our model.

Automatic thresholding is an important technique for rail defect detection, but traditional methods are not competent enough to fit the characteristics of this application. This paper proposes the Maximum Weighted Object Correlation (MWOC) thresholding method, fitting the features that rail images are unimodal and defect proportion is small. MWOC selects a threshold by optimizing the product of object correlation and the weight term that expresses the proportion of thresholded defects. Our experimental results demonstrate that MWOC achieves misclassification error of 0.85%, and outperforms the other well-established thresholding methods, including Otsu, maximum correlation thresholding, maximum entropy thresholding and valley-emphasis method, for the application of rail defect detection.