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Recent local state-of-the-art stereo algorithms based on variable cost aggregation strategies allow for inferring disparity maps comparable to those yielded by algorithms based on global optimization schemes. Unfortunately, thought these results are excellent, they are obtained at the expense of high computational requirements that are comparable or even higher than those required by global approaches. In this paper, we propose a cost aggregation strategy based on joint bilateral filtering and incremental calculation schemes that allow for efficient and accurate inference of disparity maps. Experimental comparison with state-of-the-art techniques shows the effectiveness of our proposal. [ABSTRACT FROM AUTHOR]

Several methods aimed at effectively speeding up the block matching and template matching tasks have been recently proposed. A class of these methods, referred to as exhaustive due to the fact that they optimally solve the minimization problem of the matching cost, often deploys a succession of bounding functions based on a partitioning of the template and subwindow to perform rapid and reliable detection of non-optimal candidates. In this paper we propose a study aimed at improving the efficiency of one of these methods, that is, a state-of-the-art template matching technique known as Incremental Dissimilarity Approximations (IDA). In particular, we outline a methodology to order the succession of bounding functions deployed by this technique based on the analysis of the template only. Experimental results prove that the proposed approach is able to achieve improved efficiency. [ABSTRACT FROM AUTHOR]

Gharavi-Alkhansari [1] proposed a full-search equivalent algorithm for speeding-up template matching based on Lp-norm distance measures. This algorithm performs a pruning of mismatching candidates based on multilevel pruning conditions and it has been shown that, under certain assumptions on the distortion between the image and the template, it is faster than the other full-search equivalent algorithms proposed so far, including algorithms based on the Fast Fourier Transform. In this paper we propose an original contribution with respect to Gharavi-Alkhansari΄s work that is based on the exploitation of an initial estimation of the global minimum aimed at increasing the efficiency of the pruning process. [ABSTRACT FROM AUTHOR]

Several image mosaicing algorithms claiming to advance the state of the art have been proposed so far. Though sometimes improvements can be recognised without quantitative evidences, the importance of a principled methodology to compare different algorithms is essential as this discipline evolves. Which is the best? What means the best? How to ascertain the supremacy? To answer such questions, in this paper we propose an evaluation methodology including standard data sets, ground-truth information and performance metrics. We also compare three variants of a well-known mosaicing algorithm according to the proposed methodology. [ABSTRACT FROM AUTHOR]

Time-of-Flight (TOF) cameras relate to a very recent and growing technology which has already proved to be useful for computer vision tasks. In this paper we investigate on the use of a TOF camera to perform video-based graffiti detection, which can be thought of as a monitoring system able to detect acts of vandalism such as dirtying, etching and defacing walls and objects surfaces. Experimental results show promising capabilities of the proposed approach, with improvements expected as the technology gets more mature. [ABSTRACT FROM AUTHOR]

Augmented reality is a powerful tool for delivering spatially coherent information to a user moving in a known environment. Accurate and reliable pose estimation is the key to success. Many approaches track reference objects into the scene but as the environment grows larger more objects need to be tracked leading to computationally intensive methods. Instead, we propose a practical approach that is suitable for environment where big planar structures are present. All the objects laying on the structure are composed into a large reference object using image mosaicing techniques, so that the problem is reduced to that of finding the pose from a single plane. Experimental results show the effectiveness of this approach on two interesting case studies such as aeronautical servicing and cultural heritage. [ABSTRACT FROM AUTHOR]

A novel algorithm for obtaining accurate dense disparity measurements and precise border localization from stereo pairs is proposed. The algorithm embodies a very effective variable support approach based on segmentation within a Scanline Optimization framework. The use of a variable support allows for precisely retrieving depth discontinuities while smooth surfaces are well recovered thanks to the minimization of a global function along multiple scanlines. Border localization is further enhanced by symmetrically enforcing the geometry of the scene along depth discontinuities. Experimental results show a significant accuracy improvement with respect to comparable stereo matching approaches. [ABSTRACT FROM AUTHOR]

Significant achievements have been attained in the field of dense stereo correspondence by local algorithms based on an adaptive support. Given the problem of matching two correspondent pixels within a local stereo process, the basic idea is to consider as support for each pixel only those points which lay on the same disparity plane, rather than those belonging to a fixed support. This paper proposes a novel support aggregation strategy which includes information obtained from a segmentation process. Experimental results on the Middlebury dataset demonstrate that our approach is effective in improving the state of the art. [ABSTRACT FROM AUTHOR]