Luigi Di Stefano

A fast area-based stereo matching algorithm

Abstract 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 algorithms 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.

Segmentation-based adaptive support for accurate stereo correspondence

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. n nThis 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.

ZNCC-based template matching using bounded partial correlation

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).

People Tracking Using a Time-of-Flight Depth Sensor

Visually track several moving persons engaged in close interactions is known to be a very hard problem, though 3-D approaches based on stereo vision and plan-view maps offer much promise for dealing effectively with major issues such as occlusions and quick changes in body pose and appearance. However, in case of untextured scenes due to homogeneous objects or poor illumination, stereo-based tracking systems rapidly drop their performance. In this work, we present a real time people tracking system able to work even under severe low-lighting conditions. The system relies on a novel active sensor that provides brightness and depth images based on a Time of Flight (TOF) technology. The tracking algorithm is simple yet efficient, being based on geometrical constraints and invariants. Experiments accomplished under changing lighting conditions and involving multiple people closely interacting with each other have proved the reliability of the system.

Fast template matching using bounded partial correlation

Abstract. This paper describes a novel, fast template-matching technique, referred to as bounded partial correlation (BPC), based on the normalised cross-correlation (NCC) function. The technique consists in checking at each search position a suitable elimination condition relying on the evaluation of an upper-bound for the NCC function. The check allows for rapidly skipping the positions that cannot provide a better degree of match with respect to the current best-matching one. The upper-bounding function incorporates partial information from the actual cross-correlation function and can be calculated very efficiently using a recursive scheme. We show also a simple improvement to the basic BPC formulation that provides additional computational benefits and renders the technique more robust with respect to the parameters choice.

Stereo vision enabling precise border localization within a scanline optimization framework

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.

An Evaluation Methodology for Image Mosaicing Algorithms

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.

Real-time stereo within the VIDET Project

VIDET is a research project active at the University of Bologna and aimed at the development of a mobility aid for the visually impaired. VIDETs approach consists in the conversion of depth data gathered through a stereo-vision system into a 3D model perceivable by the user by means of a wire-actuated haptic interface. In this paper we describe VIDETs PC-based, real-time, stereo-vision system. As for systems description, we outline the structure of the stereo-matching algorithm and address in more detail the optimization strategies that lead us to a fast PC-based implementation. These involve massive reduction of redundant calculations and use of the parallel multimedia instructions available in current general-purpose microprocessors. We provide experimental results showing that the system is capable of recovering correctly the 3D structure of the observed scene and allows for prompt perception of the depth changes generated by moving objects. We also report execution time measurements and compare our stereo system with the PC-based systems from SRI and Point Grey Research.

Detecting Changes in Grey Level Sequences by ML Isotonic Regression

We present a robust and efficient change detection algorithm for grey-level sequences. A deep investigation of the effects of disturbance factors (illumination changes and automatic or manual adjustments of the camera transfer function, such as AGC, AE and gamma-correction) on image brightness allows to assume locally an order-preservation of pixel intensities. By a simple statistical modelling of camera noise, an ML isotonic regression procedure can thus be applied to perform change detection. Although the proposed approach may be used as a stand-alone pixel-level change detector, here we apply it to reduced-resolution images. In fact, we aim at using the algorithm as the coarse-level of a coarse-to-fine change detector we presented in [2].

An Algorithm for Efficient and Exhaustive Template Matching

This paper proposes an algorithm for efficient and exhaustive template matching based on the Zero mean Normalized Cross Correlation (ZNCC) function. The algorithm consists in checking at each position a sufficient condition capable of rapidly skipping most of the expensive calculations involved in the evaluation of ZNCC scores at those points that cannot improve the best score found so far. The sufficient condition devised in this paper extends the concept of Bounded Partial Correlation (BPC) from Normalized Cross Correlation (NCC) to the more robust ZNCC function. Experimental results show that the proposed technique is effective in speeding up the standard procedure and that the behavior, in term of computational savings, follows that obtained by the BPC technique in the NCC case.