年画作为中国民间艺术的优秀代表之一,其风格对艺术创作具有重要意义。为了快速方便地实现自然图像的年画风格化,设计了一种自动化的年画风格化算法框架,并尽可能准确地保持自然图像的原有图案与颜色分配。对于一幅用户给定的自然图像,算法首先对图像进行自动颜色分割聚类,提取关键线条作为年画轮廓线;然后运用基于优化的年画配色映射方法将不同的分割区域重新着色;最后通过年画风格细节增强,使结果图的颜色风格更加接近年画风格。针对颜色配色优化,算法综合考量了生成图像与原图像的颜色相似度和相邻色块的颜色对比度,并利用差分进化策略对能量方程进行最小化求解得出合理的颜色配置方案。在实验中进行了几组不同产地的年画风格化实验,结果是算法可有效地将一幅自然图像自动转换为年画风格,且效果较自然,颜色具有地方特色。

In this paper, we present a novel approach for environment map sampling, which is an effective and pragmatic technique to reduce the computational cost of realistic rendering and get plausible rendering images. The proposed approach exploits the advantage of adaptive mean-shift image clustering with aid of tone-mapping, yielding oversegmented strata that have uniform intensities and capture shapes of light regions. The resulted strata, however, have unbalanced importance metric values for rendering, and the strata number is not user-controlled. To handle these issues, we develop an adaptive split-and-merge scheme that refines the strata and obtains a better balanced strata distribution. Compared to the state-of-the-art methods, our approach achieves comparable and even better rendering quality in terms of SSIM, RMSE and HDRVDP2 image quality metrics. Experimental results further show that our approach is more robust to the variation of viewpoint, environment rotation, and sample number.

In this paper, we propose SPHORB, a new fast and robust binary feature detector and descriptor for spherical panoramic images. In contrast to state-of-the-art spherical features, our approach stems from the geodesic grid, a nearly equal-area hexagonal grid parametrization of the sphere used in climate modeling. It enables us to directly build fine-grained pyramids and construct robust features on the hexagonal spherical grid, thus avoiding the costly computation of spherical harmonics and their associated bandwidth limitation. We further study how to achieve scale and rotation invariance for the proposed SPHORB feature. Extensive experiments show that SPHORB consistently outperforms other existing spherical features in accuracy, efficiency and robustness to camera movements. The superior performance of SPHORB has also been validated by real-world matching tests.
© 2014, Springer Science+Business Media New York.

This paper addresses a challenging problem of regularizing arbitrary superpixels into an optimal grid structure, which may significantly extend current low-level vision algorithms by allowing them to use superpixels (SPs) conveniently as using pixels. For this purpose, we aim at constructing maximum cohesive SP-grid, which is composed of real nodes, i.e. SPs, and dummy nodes that are meaningless in the image with only position-taking function in the grid. For a given formation of image SPs and proper number of dummy nodes, we first dynamically align them into a grid based on the centroid localities of SPs. We then define the SP-grid coherence as the sum of edge weights, with SP locality and appearance encoded, along all direct paths connecting any two nearest neighboring real nodes in the grid. We finally maximize the SP-grid coherence via cascade dynamic programming. Our approach can take the regional objectness as an optional constraint to produce more semantically reliable SP-grids. Experiments on object localization show that our approach outperforms state-of-the-art methods in terms of both detection accuracy and speed. We also find that with the same searching strategy and features, object localization at SP-level is about 100-500 times faster than pixel-level, with usually better detection accuracy.

Online virtual navigation systems enable users to hop from one panorama to another, which belong to a sparse point-to-point collection, resulting in a less pleasant viewing experience. In this paper, we present a novel method, namely Cube2Video, to support navigating between cubic panoramas in a video-viewing mode. Our method circumvents the intrinsic challenge of cubic panoramas, i.e., the discontinuities between cube faces, in an efficient way. The proposed method extends the matching-triangulation-interpolation procedure with special considerations of the spherical domain. A triangle-to-triangle homography-based warping is developed to achieve physically plausible and visually pleasant interpolation results. The temporal smoothness of the synthesized video sequence is improved by means of a compensation transformation. As experimental results demonstrate, our method can synthesize pleasant video sequences in real time, thus mimicking walking or driving navigation.

The intrinsic information of a graph can be fully encoded into its spectrum and corresponding eigenvectors of its adjacency matrix, which provides a solid foundation for the success of spectral graph matching methods. The spectral multiplicity, however, may significantly affect the matching accuracy. In this paper, we propose a spectral-multiplicity-tolerant graph matching approach. We start from modeling the spectral multiplicity in the matching error measurement. Next, we address the equal-size graph matching problem, and show how to establish the vertex-to-vertex correspondence by alternatively optimizing the multiplicity matrix C and the permutation matrix P. We also propose a reliable initialization method to make the iterative optimization process converge rapidly. Then, we extend the algorithm to unequal-size graph matching by optimally warping two graphs into the same size. A comprehensive performance evaluation has been conducted on a large synthetic dataset. We also demonstrate the effectiveness of our approach on shape retrieval. The experimental results show that compared with existing methods, the proposed approach is more robust to noise and structural corruption and has a comparable complexity. © 2013 Elsevier Ltd. All rights reserved.

Image co-saliency detection is a valuable technique to highlight perceptually salient regions in image pairs. In this paper, we propose a self-contained co-saliency detection algorithm based on superpixel affinity matrix. We first compute both intra and inter similarities of superpixels of image pairs. Bipartite graph matching is applied to determine most reliable inter similarities. To update the similarity score between every two superpixels, we next employ a GPU-based all-pair SimRank algorithm to do propagation on the affinity matrix. Based on the inter superpixel affinities we derive a co-saliency measure that evaluates the foreground cohesiveness and locality compactness of superpixels within one image. The effectiveness of our method is demonstrated in experimental evaluation.