As LLMs excel on standard reading comprehension benchmarks, attention is shifting toward evaluating their capacity for complex abstract reasoning and inference.Literaturebased benchmarks, with their rich narrative and moral depth, provide a compelling framework for evaluating such deeper comprehension skills.Here, we present MORABLES, a humanverified benchmark built from fables and short stories drawn from historical literature.The main task is structured as multiple-choice questions targeting moral inference, with carefully crafted distractors that challenge models to go beyond shallow, extractive question answering.To further stress-test model robustness, we introduce adversarial variants designed to surface LLM vulnerabilities and shortcuts due to issues such as data contamination.Our findings show that, while larger models outperform smaller ones, they remain susceptible to adversarial manipulation and often rely on superficial patterns rather than true moral reasoning.This brittleness results in significant self-contradiction, with the best models refuting their own answers in roughly 20% of cases depending on the framing of the moral choice.Interestingly, reasoning-enhanced models fail to bridge this gap, suggesting that scale -not reasoning ability -is the primary driver of performance.

Puns are a form of humorous wordplay that exploits polysemy and phonetic similarity.While LLMs have shown promise in detecting puns, we show in this paper that their understanding often remains shallow, lacking the nuanced grasp typical of human interpretation.By systematically analyzing and reformulating existing pun benchmarks, we demonstrate how subtle changes in puns are sufficient to mislead LLMs.Our contributions include comprehensive and nuanced pun detection benchmarks, human evaluation across recent LLMs, and an analysis of the robustness challenges these models face in processing puns.

EXplainable Artificial Intelligence (XAI) is a vibrant research topic in the artificial intelligence community. It is raising growing interest across methods and domains, especially those involving high stake decision-making, such as the biomedical sector. Much has been written about the subject, yet XAI still lacks shared terminology and a framework capable of providing structural soundness to explanations. In our work, we address these issues by proposing a novel definition of explanation that synthesizes what can be found in the literature. We recognize that explanations are not atomic but the combination of evidence stemming from the model and its input-output mapping, and the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation is an accurate description of the model’s inner workings and decision-making process) and plausibility (i.e., how much the explanation seems convincing to the user). Our theoretical framework simplifies how these properties are operationalized, and it provides new insights into common explanation methods that we analyze as case studies. We also discuss the impact that our framework could have in biomedicine, a very sensitive application domain where XAI can have a central role in generating trust.

Modern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1-score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy.

Structured-light 3D reconstruction techniques are employed in a wide range of applications for industrial inspection. In particular, some tasks require micrometric precision for the identification of microscopic surface irregularities. We propose a novel calibration technique for structured-light systems adopting telecentric lenses for both camera and projector. The device exploits a fixed light pattern (striped-based) to perform accurate microscopic surface reconstruction and measurements. Our method employs a sphere with a known radius as calibration target and takes advantage of the orthographic projection model of the telecentric lenses to recover the bundle of planes originated by the projector. Once the sheaf of parallel planes is properly described in the camera reference frame, the triangulation of the surface’s object hit by the light stripes is immediate. Moreover, we tested our technique in a real-world scenario for industrial surface inspection by implementing a complete pipeline to recover the intersections between the projected planes and the surface. Experimental analysis shows the robustness of the proposed approach against synthetic and real-world test data.

Industrial manufacturing processes often involve a visual control system to detect possible product defects during production. Such inspection devices usually include one or more cameras and several light sources designed to highlight surface imperfections under different illumination conditions (e.g. bumps, scratches, holes). In such scenarios, a preliminary calibration procedure of each component is a mandatory step to recover the system’s geometrical configuration and thus ensure a good process accuracy. In this paper we propose a procedure to estimate the position of each light source with respect to a camera network using an inexpensive Lambertian spherical target. For each light source, the target is acquired at different positions from different cameras, and an initial guess of the corresponding light vector is recovered from the analysis of the collected intensity isocurves. Then, an energy minimization process based on the Lambertian shading model refines the result for a pr ecise 3D localization. We tested our approach in an industrial setup, performing extensive experiments on synthetic and real-world data to demonstrate the accuracy of the proposed approach.

Phase-shift is one of the most effective techniques in 3D structured-light scanning for its accuracy and noise resilience. However, the periodic nature of the signal causes a spatial ambiguity when the fringe periods are shorter than the projector resolution. To solve this, many techniques exploit multiple combined signals to unwrap the phases and thus recovering a unique consistent code. In this paper, we study the phase estimation and unwrapping problem in a stochastic context. Assuming the acquired fringe signal to be affected by additive white Gaussian noise, we start by modelling each estimated phase as a zero-mean Wrapped Normal distribution with variance σ ̄ 2 . Then, our contributions are twofolds. First, we show how to recover the best projector code given multiple phase observations by means of a ML estimation over the combined fringe distributions. Second, we exploit the Cramér-Rao bounds to relate the phase variance σ ̄ 2 to the variance of the observed signal, that can be easily estimated online during the fringe acquisition. An extensive set of experiments demonstrate that our approach outperforms other methods in terms of code recovery accuracy and ratio of faulty unwrappings.

The ubiquitous presence of cylindrical shapes in both natural and man-made environments makes their automated extraction a pivotal task for a broad range of applications such as robot manipulation, reverse engineering and automated industrial inspection. Albeit conceptually simple, the task of fitting cylinders from 3D data can quickly become challenging if performed "in-the-wild", when no prior is given to the number of primitives to find or when the point cloud is noisy and not oriented. In this paper we introduce a new robust approach to iteratively extract cylindrical primitives from a 3D point cloud by exploiting mutual consensus of different cylinder candidates. First, a set of possible axes is generated by slicing the point cloud with multiple random planes. Then, a game-theoretic inlier selection process is performed to extract a subset of axes maximizing the fitness against a payoff function based on the shortest geodesic path in SE(3) between pairs of corresponding 3D lines. Finally, the probability distribution resulting from the previous selection step is used to weight the input candidates and robustly obtain the final cylinder coefficients. Compared to other methods, our approach does not require point normals, offers superior resilience to noise and does not depend on delicate tuning of multiple parameters.

Within modern Deep Learning setups, data augmentation is the weapon of choice when dealing with narrow datasets or with a poor range of different samples. However, the benefits of data augmentation are abysmal when applied to a dataset which is inherently unable to cover all the categories to be classified with a significant number of samples. To deal with such desperate scenarios, we propose a possible last resort: Cross-Dataset Data Augmentation. That is, the creation of new samples by morphing observations from a different source into credible specimens for the training dataset. Of course specific and strict conditions must be satisfied for this trick to work. In this paper we propose a general set of strategies and rules for Cross-Dataset Data Augmentation and we demonstrate its feasibility over a concrete case study. Even without defining any new formal approach, we think that the preliminary results of our paper are worth to produce a broader discussion on this topic.

Among several structured light approaches, phase shift is the most widely adopted in real-world 3D reconstruction devices. This is mainly due to its high accuracy, strong resilience to noise and straightforward implementation. However, Phase shift also exhibits an inherent weakness, that is the spatial ambiguity resulting from the periodicity of the sinusoidal wave adopted. Of course many phase unwrapping methods have been proposed to solve such ambiguity. One of the most promising methods exploits additional signals of mutually prime periods, in order to observe a distinct combination of phases for each spatial point. Unfortunately, for such combination to be properly recognized, a very high accuracy in phase recovery must be attained for each signal. In fact, even modest errors could lead to unwrapping faults, making the overall approach much less resilient to noise than plain phase shift. With this paper we introduce a feasible and effective fault recovery method that can be directly applied to multi-period phase shift. The combined pipeline offers an optimal accuracy and coverage even with high noise conditions, overcoming the setbacks of the original method. The performance of such pipeline is established by means of an in depth set of experimental evaluations and comparison, both with real and synthetically generated data.

Among structured light strategies, the ones based on phase shift are considered to be the most adaptive with respect to the features of the objects to be captured. Inter alia, the theoretical invariance to signal strength and the absence of discontinuities in intensity, make phase shift an ideal candidate to deal with complex surfaces of unknown geometry, color and texture. However, in practical scenarios, unexpected artifacts could still result due to the characteristics of real cameras. This is the case, for instance, with high contrast areas resulting from abrupt changes in the albedo of the captured objects. In fact, the not negligible size of pixels and the presence of blur can produce a mix of signal integration from adjacent areas with different albedo. This, in turn, would result in a bias in the phase recovery and, consequentially, in an inaccurate 3D reconstruction of the surface. While this problem affects most structure light methods based on phase shift or derived techniques, little effort has been put in addressing it. With this paper we propose a model for the phase corruption and a theoretically sound correction step to be adopted to compensate the bias. The practical effectiveness of our approach is well demonstrated by a complete set of experimental evaluations.

At the core of many Computer Vision applications stands the need to define a mathematical model describing the imaging process. To this end, the pinhole model with radial distortion is probably the most commonly used, as it balances low complexity with a precision that is sufficient for most applications. On the other hand, unconstrained non-parametric models, despite being originally proposed to handle specialty cameras, have been shown to outperform the pinhole model, even with the simpler setups. Still, notwithstanding the higher accuracy, the inability of describing the imaging model by simple linear projective operators severely limits the use of standard algorithms with unconstrained models. In this paper we propose a parameter-free camera model where each imaging ray is constrained to a common optical center, forcing the camera to be central. Such model can be easily calibrated with a practical procedure which provides a convenient undistortion map that can be used to obtain a virtual pinhole camera. The proposed method can also be used to calibrate a stereo rig with a displacement map that simultaneously provides stereo rectification and corrects lens distortion.

Abstract Recent efforts in the area of joint object matching approach the problem by taking as input a set of pairwise maps, which are then jointly optimized across the whole collection so that certain accuracy and consistency criteria are satisfied. One natural requirement is cycle‐consistency—namely the fact that map composition should give the same result regardless of the path taken in the shape collection. In this paper, we introduce a novel approach to obtain consistent matches without requiring initial pairwise solutions to be given as input. We do so by optimizing a joint measure of metric distortion directly over the space of cycle‐consistent maps; in order to allow for partially similar and extra‐class shapes, we formulate the problem as a series of quadratic programs with sparsity‐inducing constraints, making our technique a natural candidate for analysing collections with a large presence of outliers. The particular form of the problem allows us to leverage results and tools from the field of evolutionary game theory. This enables a highly efficient optimization procedure which assures accurate and provably consistent solutions in a matter of minutes in collections with hundreds of shapes.

When a planar structure is observed from multiple views, the projections of its corresponding 3D points on each image are related by a homography. Its estimation is a key step in many computer vision tasks where either the rigid motion between views or a per-pixel image correspondence is sought. The vast majority of multi-view homography estimation techniques relies on matching a sparse set of point-to-point correspondences to establish a connected graph in the camera network. This track creation step is critical to ensure that the following bundle adjustment can estimate a globally optimal alignment in which the error is diffused coherently on each pairwise homography. On the other hand, erroneous or short tracks often cause misalignments among the views. We propose an optimization technique to simultaneously recover a transitively consistent network of planar homographies between multiple views together with a segmentation of the pixels comprising the observed plane (Fig. 1). Our method acts on a per-pixel basis to avoid a preliminary multi-view sparse feature matching step. Similarly to bundle adjustment, the error is diffused so that each homography in the view graph is transitively consistent with the others. The effectiveness of the proposed approach is evaluated in real-world scenarios and synthetically generated scenes.

Feature matching is a key step in most Computer Vision tasks involving several views of the same subject. In fact, it plays a crucial role for a successful reconstruction of 3D information of the corresponding material points. Typical approaches to construct stable feature tracks throughout a sequence of images operate via a two-step process: First, feature matches are extracted among all pairs of points of view; these matches are then given in input to a regularizer that provides a final, globally consistent solution. In this paper, we formulate this matching problem as a simultaneous optimization over the entire image collection, without requiring previously computed pairwise matches to be given as input. As our formulation operates directly in the space of feature across multiple images, the final matches are consistent by construction. Our matching problem has a natural interpretation as a non-cooperative game, which allows us to leverage tools and results from Game Theory. We performed a specially crafted set of experiments demonstrating that our approach compares favorably with the state of the art, while retaining a high computational efficiency.

A number of critical factors arises when a complex 3D scene is to be reconstructed by means of a large sequence of different views. Some of them are related to the ability to recover the correct identity and the accurate projection of each observed feature. Other sources of error are tied to the reliability of the orientation estimate for each view. With this paper we propose a method that tries to solve both problems at the same time, while being also inherently resilient to outliers. At the core of the approach stands a widely adopted game-theoretical selection technique, which has already been successfully embraced to address similar tasks. The original inception, however, has been further refined to address a wider range of scenarios, as well as to offer a reduced memory consumption and computation complexity. By exploiting these enhancements, we were able to apply our technique to a large-scale setup involving several hundreds of view points and tens of thousands of independent observations.

Artificial markers are successfully adopted to solve several vision tasks, ranging from tracking to calibration. While most designs share the same working principles, many specialized approaches exist to address specific application domains. Some are specially crafted to boost pose recovery accuracy. Others are made robust to occlusion or easy to detect with minimal computational resources. The sheer amount of approaches available in recent literature is indeed a statement to the fact that no silver bullet exists. Furthermore, this is also a hint to the level of scholarly interest that still characterizes this research topic. With this paper we try to add a novel option to the offer, by introducing a general purpose fiducial marker which exhibits many useful properties while being easy to implement and fast to detect. The key ideas underlying our approach are three. The first one is to exploit the projective invariance of conics to jointly find the marker and set a reading frame for it. Moreover, the tag identity is assessed by a redundant cyclic coded sequence implemented using the same circular features used for detection. Finally, the specific design and feature organization of the marker are well suited for several practical tasks, ranging from camera calibration to information payload delivery.

Given the raising interest in light-field technology and the increasing availability of professional devices, a feasible and accurate calibration method is paramount to unleash practical applications. In this paper we propose to embrace a fully non-parametric model for the imaging and we show that it can be properly calibrated with little effort using a dense active target. This process produces a dense set of independent rays that cannot be directly used to produce a conventional image. However, they are an ideal tool for 3D reconstruction tasks, since they are highly redundant, very accurate and they cover a wide range of different baselines. The feasibility and convenience of the process and the accuracy of the obtained calibration are comprehensively evaluated through several experiments.

A simple and natural interaction is considered the most important feature of an interface for multiple impromptu users in public spaces. Intuitiveness and forthright feedback are key factors to enable a steep learning curve for untrained users that need to grasp the interaction model in a short time span. The lack of proper constraints, designed to restrict and guide the user actions, might hinder such intuitiveness, mainly when the number of users grows or their behavior is exceedingly unrestrained.

We evaluate the accuracy of perception of a viewer-dependent system that has been implemented through a simple augmentation of basic shutter glasses for stereoscopic setups. The evaluation is based on length measures performed by a group of users on two different scenes, rendered through different perspectives computed from the dynamic user position and from a fixed point of view.

In many visual interaction applications the user needs to explore a scene by moving with respect to the virtual environment. Using a fixed camera viewpoint leads to visual inconsistencies, which can be avoided only if the exact pose of the user head is known and can be used to produce a perspective correct rendering. To this end, tracking devices are often used, however many of them are relatively expensive or require the user to wear special apparel. With this paper we present a tracking system that can be implemented with a simple and very low cost modification of standard shutter glasses. The accuracy of such approach has been evaluated quantitatively with a specially crafted experimental setup.

Many 3D scanning techniques rely on two or more well calibrated imaging cameras and a structured light source. Within these setups the light source does not need any calibration. In fact the shape of the target surface can be inferred by the cameras geometry alone, while the structured light is only exploited to establish stereo correspondences. Unfortunately, this approach requires each reconstructed point to exhibit an unobstructed line of sight from three independent points of views. This requirement limits the amount of scene points that can be effectively captured with each shot. To overcome this restriction, several systems that combine a single camera with a calibrated projector have been proposed. However, this type of calibration is more complex to be performed and its accuracy is hindered by both the indirect measures involved and the lower precision of projector optics. In this paper we propose an online calibration method for structured light sources that computes the projector parameters concurrently with regular scanning shots. This results in an easier and seamless process that can be applied directly to most current scanning systems without modification. Moreover, we attain high accuracy by adopting an unconstrained imaging model that is able to handle well even less accurate optics. The improved surface coverage and the quality of the measurements are thoroughly assessed in the experimental section.

The estimation of camera intrinsic parameters plays a crucial role in all computer vision tasks for which the underlying model that drives the image formation process has to be known. As a consequence, a deluge set of different approaches has been proposed in literature over the last decades. Most of those lean on the observation of a known object (i.e. a calibration target) from different point of views, providing the necessary data to estimate the model through different optimization approaches. In this work, we exploit the projective properties of conics to estimate the focal length and optical center of a pinhole camera just by observing a set of coplanar circles, where neither the radius nor the reciprocal position of each circle has to be known a-priori. This make such method particularly interesting whenever the usage of a calibration target is not a feasible option. Our contribution is twofold. First, we propose a reliable method to locate coplanar circles from images by means of a non-cooperative evolutionary game. Second, we refine the estimation of camera parameters with a non-linear function minimization through a simple yet effective gradient descent. Performance of the proposed approach is assessed through an experimental section consisting on both quantitative and qualitative tests.

Traditional stereoscopic displays assume the viewer to be standing at a specific location, that is the same pose (relative to the screen) of the stereo camera pair that depicted the scene (physically or virtually). Even for basic applications, such as movies or games, this leads to visual inconsistencies as soon as the user moves his head. Moreover, with this premises, it is not possible at all to develop more sophisticated 3D applications, involving the freedom for the user to walk around objects or to interact with them. The most popular solution to these limitation is to track the user head in order to produce a scene rendering that can be seen correctly from his point of view. With this paper we propose a viewer-dependent system that is very easy to implement since it is based on a simple augmentation of basic shutter glasses used in standard stereoscopic setups. Furthermore, we introduce a practical and sound method to quantitatively assess the accuracy of any view-dependent display approach. This fills a clear shortcoming of the currently adopted evaluation methods, that are for the most part qualitative.

Traditional camera models are often the result of a compromise between the ability to account for non-linearities in the image formation model and the need for a feasible number of degrees of freedom in the estimation process. These considerations led to the definition of several ad hoc models that best adapt to different imaging devices, ranging from pinhole cameras with no radial distortion to the more complex catadioptric or polydioptric optics. In this paper we propose the use of an unconstrained model even in standard central camera settings dominated by the pinhole model, and introduce a novel calibration approach that can deal effectively with the huge number of free parameters associated with it, resulting in a higher precision calibration than what is possible with the standard pinhole model with correction for radial distortion. This effectively extends the use of general models to settings that traditionally have been ruled by parametric approaches out of practical considerations. The benefit of such an unconstrained model to quasi-pinhole central cameras is supported by an extensive experimental validation.

We consider the problem of minimum distortion intrinsic correspondence between deformable shapes, many useful formulations of which give rise to the NP-hard quadratic assignment problem (QAP). Previous attempts to use the spectral relaxation have had limited success due to the lack of sparsity of the obtained “fuzzy” solution. In this paper, we adopt the recently introduced alternative L1relaxation of the QAP based on the principles of game theory. We relate it to the Gromov and Lipschitz metrics between metric spaces and demonstrate on state-of-the-art benchmarks that the proposed approach is capable of finding very accurate sparse correspondences between deformable shapes.

Over the last decades fiducial markers have provided widely adopted tools to add reliable model-based features into an otherwise general scene. Given their central role in many computer vision tasks, countless different solutions have been proposed in the literature. Some designs are focused on the accuracy of the recovered camera pose with respect to the tag; some other concentrate on reaching high detection speed or on recognizing a large number of distinct markers in the scene. In such a crowded area both the researcher and the practitioner are licensed to wonder if there is any need to introduce yet another approach. Nevertheless, with this paper, we would like to present a general purpose fiducial marker system that can be deemed to add some valuable features to the pack. Specifically, by exploiting the projective properties of a circular set of sizeable dots, we propose a detection algorithm that is highly accurate. Further, applying a dot pattern scheme derived from error-correcting codes, allows for robustness with respect to very large occlusions. In addition, the design of the marker itself is flexible enough to accommodate different requirements in terms of pose accuracy and number of patterns. The overall performance of the marker system is evaluated in an extensive experimental section, where a comparison with a well-known baseline technique is presented.

Surface registration is a fundamental step in the reconstruction of three-dimensional objects. While there are several fast and reliable methods to align two surfaces, the tools available to align multiple surfaces are relatively limited. In this paper we propose a novel multiview registration algorithm that projects several pairwise alignments onto a common reference frame. The projection is performed by representing the motions as dual quaternions, an algebraic structure that is related to the group of 3D rigid transformations, and by performing a diffusion along the graph of adjacent (i.e., pairwise alignable) views. The approach allows for a completely generic topology with which the pair-wise motions are diffused. An extensive set of experiments shows that the proposed approach is both orders of magnitude faster than the state of the art, and more robust to extreme positional noise and outliers. The dramatic speedup of the approach allows it to be alternated with pairwise alignment resulting in a smoother energy profile, reducing the risk of getting stuck at local minima.

Accurate intrinsic camera calibration is essential to any computer vision task that \ninvolves image based measurements. Given its crucial role with respect to precision, a \nlarge number of approaches have been proposed over the last decades. Despite this rich \nliterature, steady advancements in imaging hardware regularly push forward the need \nfor even more accurate techniques. Some authors suggest generalizations of the camera \nmodel itself, others propose novel designs for calibration targets or different optimization \nschemas. In this paper we take a completely different route by directly addressing one \nof the most overlooked problems in practical calibration scenarios. Specifically, we drop \nthe assumption that the target is known with enough precision and we adjust it in an \niterative way as part of the whole process. This is in fact the case with the typical target \nused in most of the calibration literature, which is usually printed on paper and stitched \non a flat surface. In the experimental section we show that even with such a cheaply \ncrafted target it is possible to obtain a very accurate camera calibration that outperforms \nthose obtained with well-known standard techniques.

Surface registration is a fundamental step in the reconstruction of three-dimensional objects. This is typically a two step process where an initial coarse motion estimation is followed by a refinement. Most coarse registration algorithms exploit some local point descriptor that is intrinsic to the shape and does not depend on the relative position of the surfaces. By contrast, refinement techniques iteratively minimize a distance function measured between pairs of selected neighboring points and are thus strongly dependent on initial alignment. In this paper we propose a novel technique that allows to obtain a fine surface registration in a single step, without the need of an initial motion estimation. The main idea of our approach is to cast the selection of correspondences between points on the surfaces in a game-theoretic framework, where a natural selection process allows mating points that satisfy a mutual rigidity constraint to thrive, eliminating all the other correspondences. This process yields a very robust inlier selection scheme that does not depend on any particular technique for selecting the initial strategies as it relies only on the global geometric compatibility between correspondences. The practical effectiveness of the proposed approach is confirmed by an extensive set of experiments and comparisons with state-of-the-art techniques.

Feature-based image matching relies on the assumption that the features contained in the model are distinctive enough. When both model and data present a sizeable amount of clutter, the signal-to-noise ratio falls and the detection becomes more challenging. If such clutter exhibits a coherent structure, as it is the case for textured background, matching becomes even harder. In fact, the large amount of repeatable features extracted from the texture dims the strength of the relatively few interesting points of the object itself. In this paper we introduce a game-theoretic approach that allows to distinguish foreground features from background ones. In addition the same technique can be used to deal with the object matching itself. The whole procedure is validated by applying it to a practical scenario and by comparing it with a standard point-pattern matching technique.

In this paper we introduce a robust matching technique that allows very accurate selection of corresponding feature points from multiple views. Robustness is achieved by enforcing global geometric consistency at an early stage of the matching process, without the need of subsequent verification through reprojection. The global consistency is reduced to a pairwise compatibility making use of the size and orientation information provided by common feature descriptors, thus projecting what is a high-order compatibility problem into a pairwise setting. Then a game-theoretic approach is used to select a maximally consistent set of candidate matches, where highly compatible matches are enforced while incompatible correspondences are driven to extinction.

With this paper we offer a game-theoretic perspective for the all-pervasive matching problem in computer vision. Specifically, we formulate the matching problem as a (population) non-cooperative game where the potential associations between the items to be matched correspond to (pure) strategies, while payoffs reflect the degree of compatibility between competing hypotheses. Within this formulation, the solutions of the matching problem correspond to evolutionary stable states (ESS's), a robust population-based generalization of the notion of a Nash equilibrium. In order to find ESS's of our matching game, we propose using a novel, fast evolutionary game dynamics motivated by Darwinian selection processes, which let the pure strategies play against each other until an equilibrium is reached. A distinguishing feature of the proposed framework is that it allows one to naturally deal with general many-to-many matching problems even in the presence of asymmetric compatibilities. The potential of the proposed approach is demonstrated via two sets of image matching experiments, both of which show that our results outperform those obtained using well-known domain-specific algorithms.

A problem commonly encountered in Computer Vision is the recovery of the transformation parameters between two affinely distorted images. In this paper, we propose a novel feature-based approach that casts the matching problem to the search of a maximum clique over an auxiliary hypergraph. We also introduce a continuous-based characterization of cliques in hypergraphs that allows us to handle the hard combinatorial problem using tools from the continuous domain. Finally, we present experimental result and comparisons with a state-of-the-art algorithm.