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Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods. Recently, the rise of machine learning and the rapid proliferation of deep learning enhanced stereo matching with new exciting trends and applications unthinkable until a few years ago. Interestingly, the relationship between these two worlds is two-way. While machine, and especially deep, learning advanced the state-of-the-art in stereo matching, stereo itself enabled new ground-breaking methodologies such as self-supervised monocular depth estimation based on deep networks. In this paper, we review recent research in the field of learning-based depth estimation from single and binocular images highlighting the synergies, the successes achieved so far and the open challenges the community is going to face in the immediate future.

This paper proposes a novel method for fast pattern matching based on dissimilarity functions derived from the Lp norm, such as the Sum of Squared Differences (SSD) and the Sum of Absolute Differences (SAD). The proposed method is full-search equivalent, i.e. it yields the same results as the Full Search (FS) algorithm. In order to pursue computational savings the method deploys a succession of increasingly tighter lower bounds of the adopted Lp norm-based dissimilarity function. Such bounding functions allow for establishing a hierarchy of pruning conditions aimed at skipping rapidly those candidates that cannot satisfy the matching criterion. The paper includes an experimental comparison between the proposed method and other full-search equivalent approaches known in literature, which proves the remarkable computational efficiency of our proposal.

As reported in the recent image velocimetry literature, tracking the motion of sparse feature points floating on the river surface as done by the Optical Tracking Velocimetry (OTV) algorithm is a promising strategy to address surface flow monitoring. Moreover, the lightweight nature of OTV coupled with computational optimizations makes it suited even for its deployment in situ to perform measurements at the edge with cheap embedded devices without the need to perform offload processing. Despite these notable achievements, the actual practical deployment of OTV in remote environments would require cheap and self-powered systems enabling continuous measurements without the need for cumbersome and expensive infrastructures rarely found in situ. Purposely, in this paper, we propose an additional simplification to the OTV algorithm to reduce even further its computational requirements, and we analyze self-powered off-the-shelf setups for in situ deployment. We assess the performance of such set-ups from different perspectives to determine the optimal solution to design a cost-effective self-powered measurement node. [ABSTRACT FROM AUTHOR]

We propose a novel algorithm, referred to as enhanced bounded correlation (EBC), that significantly reduces the number of computations required to carry out template matching based on normalized cross correlation (NCC) and yields exactly the same result as the full search algorithm. The algorithm relies on the concept of bounding the matching function: finding an efficiently computable upper bound of the NCC rapidly prunes those candidates that cannot provide a better NCC score with respect to the current best match. In this framework, we apply a succession of increasingly tighter upper bounding functions based on Cauchy-Schwarz inequality. Moreover, by including an online parameter prediction step into EBC, we obtain a parameter free algorithm that, in most cases, affords computational advantages very similar to those attainable by optimal offline parameter tuning. Experimental results show that the proposed algorithm can significantly accelerate a full-search equivalent template matching process and outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a class of algorithms enabling efficient and exhaustive matching of a template into an image based on the Zero mean Normalized Cross-Correlation function (ZNCC). The approach consists in checking at each image position two sufficient conditions obtained at a reduced computational cost. This allows to skip rapidly most of the expensive calculations required to evaluate the ZNCC at those image points that cannot improve the best correlation score found so far. The algorithms shown in this paper generalize and extend the concept of Bounded Partial Correlation (BPC), previously devised for a template matching process based on the Normalized Cross-Correlation function (NCC). [Copyright &y& Elsevier]

Pattern matching is widely used in signal processing, computer vision, and image and video processing. Full search equivalent algorithms accelerate the pattern matching process and, in the meantime, yield exactly the same result as the full search. This paper proposes an analysis and comparison of state-of-the-art algorithms for full search equivalent pattern matching. Our intention is that the data sets and tests used in our evaluation will be a benchmark for testing future pattern matching algorithms, and that the analysis concerning state-of-the-art algorithms could inspire new fast algorithms. We also propose extensions of the evaluated algorithms and show that they outperform the original formulations. [ABSTRACT FROM AUTHOR]

This paper proposes an area-based stereo algorithm suitable to real time applications. The core of the algorithm relies on the uniqueness constraint and on a matching process that rejects previous matches as soon as more reliable ones are found. The proposed approach is also compared with bidirectional matching (BM), since the latter is the basic method for detecting unreliable matches in most area-based stereo algorithms. We describe the algorithm''s matching core, the additional constraints introduced to improve the reliability and the computational optimizations carried out to achieve a very fast implementation. We provide a large set of experimental results, obtained on a standard set of images with ground-truth as well as on stereo sequences, and computation time measurements. These data are used to evaluate the proposed algorithm and compare it with a well-known algorithm based on BM. [Copyright &y& Elsevier]