In this paper, an efficient interactive image segmentation method based on maximization of submodular function under user’s scribble constraint is presented. The problem of interactive image segmentation is formulated as the maximum entropy rate under user’s constraints. The objective function is submodular, and we solve the constrained submodular function maximization by incorporating a new data structure and some aggregating rules into the greedy algorithm. The main steps of our algorithm are as follows. First, the pixels scribbled by the user are clustered separately as target foreground and background clusters. Second, in the process of greedy algorithm, unscribbled pixels are aggregated to the corresponding target cluster according to the proposed aggregating rules. Finally, the segmentation result is presented by the two target clusters. The experiments and comparisons on three standard benchmarks show that our method has good performance. Our method is straightforward and efficient, and the time complexity of our method is between linear and polynomial. Furthermore, we analyze the influence of different scribbles, and propose some optimal scribble strategies.

Abstract Image stitching, as the important field of computer graphics and vision, has received much attention in recent years. Image stitching techniques are generally decomposed into two phases: image alignment, which aligns target images with the reference images; and image composition, which fixes ghosting and visual artefacts. This work aims to propose a new strategy for the seam‐cutting method which provides visually appealing result. Seam‐cutting is one of the most influential methods in image composition, which can relieve artefacts and produce plausible results. However, it is observed that the state‐of‐the‐art seam‐cutting approaches usually lead to undesirable seams in some challenging scenes. Here, the authors put forward a novel seam‐cutting method by defining a new energy function. This method uses power of norm as a colour difference which can magnify the weight of colour distinction to avoid undesirable seams and artefacts. The proposed method can be easily implemented. The test images are collected from the public available challenging datasets and taken by ourselves. Experiments demonstrate that the proposed method can create comparable or even better stitching results compared to other state‐of‐the‐art seam‐cutting approaches.

Interactive segmentation has recently become a hot topic for its wide application. The authors propose an efficacious appearance separation model for interactive binary segmentation, which incorporates the difference of foreground and background colour models and the difference of corresponding geodesic models into the popular densely connected conditional random field (Dense CRF) framework. The proposed method can adaptively set relevant parameter values in this framework according to the characteristics of target images in a per‐image manner, therefore, it gets rid of the dependence on specific datasets. After accomplishing a mean‐field inference, the authors are able to get satisfactory results without the time‐consuming parameter learning process and multiple iterative optimisations. Overall, the proposed approach is highly efficient and mitigates the contradiction between accuracy and segmentation efficiency. In addition, the proposed approach reduces the efforts of scribble‐style interaction from users. The experimental results on three famous datasets show that the proposed method is superior to the other five new algorithms released in recent years regarding accuracy, and is faster than or close to them in runtime.

Effective and efficient image segmentation is an important task in computer vision. As the full‐automatic image segmentation is usually difficult to segment the natural image, it is an excellent solution to use interactive schemes. Here, to overcome the defects of SSNCut in its low quality and speed, the authors proposed a new interactive image segmentation method based on superpixels, must‐link, cannot‐link constraints and improved normalised cuts. The main contribution of their work is as follows: first, the similarity between two superpixel regions is calculated using Bhattacharyya distance. Second, they adaptively modify the weights of must‐link and cannot‐link constraints. Compared to SSNCut, their method greatly improves the accuracy of segmentation. Comparative experiments on open datasets show that the proposed method can get better results compared with SSNCut, GrabCut in one cut, interactive segmentation using binary partition tree, interactive graph cut, seed region growing, and simple interactive object extraction.

This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.

The scale space-based method has been recently studied for multispectral alignment; however, due to the significant intensity difference between the image pairs, there are usually not enough keypoint correspondences found, and the robustness of the alignment tends to be compromised. In this letter, we attempt to improve the performance from the following two aspects: 1) to avoid the boundary blurring of Gaussian scale space, we adopt nonlinear scale space to explore more keypoints with potential of being correctly matched, and 2) a robust feature descriptor is proposed, and the resulting feature matrix is matched using the previously proposed rotation-invariant distance to obtain more correct keypoint correspondences. Experimental results for multispectral remote images indicate that the proposed method improves the matching performance compared to state-of-the-art methods in terms of correctly matched number of keypoints, aligning accuracy, and rate of correctly matched image pairs. It is also revealed in this letter that, if the descriptor is carefully designed, the local features are distinctive enough for produce good matching even when the main orientation is not present.

Scale invariant feature transform (SIFT) has been widely used in image matching. But when SIFT is introduced in the registration of remote sensing images, the keypoint pairs which are expected to be matched are often assigned two different value of main orientation owing to the significant difference in the image intensity between remote sensing image pairs, and therefore a lot of incorrect matches of keypoints will appear. This paper presents a method using rotation-invariant distance instead of Euclid distance to match the scale invariant feature vectors associated with the keypoints. In the proposed method, the feature vectors are reorganized into feature matrices, and fast Fourier transform (FFT) is introduced to compute the rotation-invariant distance between the matrices. Much more correct matches are obtained by the proposed method since the rotation-invariant distance is independent of the main orientation of the keypoints. Experimental results indicate that the proposed method improves the match performance compared to other state-of-art methods in terms of correct match rate and aligning accuracy.

Normalized cross correlation (NCC) has been widely used to match control points (CP) in image alignment. This method will produce a lot of incorrect matches owing to the significant difference in the image intensity between multispectral image pairs, and furthermore, it is very computationally expensive to handle rotational displacement. This letter presents a method using rotation-invariant distance to match CPs; a local descriptor matrix is built to describe each CP, and fast Fourier transform is introduced to compute the rotation-invariant distance between the matrices. The computational load is sharply decreased by rotation-invariant distance compared to NCC, and furthermore, the load will remain unchanged in circumstance with arbitrary rotational angle. Experimental results indicate that the proposed method improves the match performance compared to other state-of-the-art methods in terms of correct match rate and aligning accuracy.

Fingerprinting is an emerging technology to protect multimedia data from piracy, where each distributed copy is labelled with unique identification information. To protect the rights of both the merchant and the customer, the fingerprinting is designed to be asymmetric, where the merchant can trace the traitor by means of the embedded fingerprint and the customer is immune to being framed due to the asymmetric property. This paper proposes an asymmetric fingerprinting scheme that is efficient from the bandwidth usage point of view, where a one-out-of-two oblivious transfer protocol is used to achieve the asymmetric property. In our scheme, symmetric encryption instead of public-key encryption, is performed on the multimedia data, which can reduce the complexity and communication cost. In addition, multicast that is an efficient transport technology for one-to-many communication is exploited, which can reduce the bandwidth usage significantly.

When the scale-invariant feature transform (SIFT) is adopted in the registration of remote sensing images, a lot of incorrect matches of keypoints will appear owing to the significant difference in the image intensity between remote sensing images compared to visible images. Scale–orientation joint restriction criteria are proposed to achieve robust feature matching for keypoints in remote sensing images. Moreover, the feature descriptor of each keypoint is also refined to overcome the difference in the gradient intensity and orientation between remote image pairs. Experimental results for multidate, multispectral, and multisensor remote images indicate that the proposed method improves the match performance compared to intensity- and SIFT-based methods in terms of correct-match rate and aligning accuracy.

The super edge connectivity properties of a graph G can be measured by the restricted edge connectivity λ′(G). We evaluate λ′(G) and the number of i-cutsets Ci(G), d ≤ i ≤ 2d − 3, explicitly for each d-regular edge-symmetric graph G. These results improve the previous one by R. Tindell on the same subject. © 1999 John Wiley & Sons, Inc. Networks 33: 157–159, 1999

The circulant graphs are of particular interest as models of communication networks. In this work, we present new reliability analysis results for circulants based on the concept of restricted edge connectivity, which generalizes the super-λ property of a graph. We evaluate the restricted edge connectivity λ′ and the number of i-cutsets Ni(G), λ ≤ i < λ′, for any circulant graph explicitly. This improves the previous results on the subject. © 1998 John Wiley & Sons, Inc. Networks 31:61–65, 1998