A New A Contrario Approach for the Robust Determination of the Fundamental Matrix

International audienceThe fundamental matrix is a two-view tensor that plays a central role in Computer Vision geometry. We address its robust estimation given correspondences between image features. We use a non-parametric estimate of the distribution of image features, and then follow a probabilistic approach to select the best possible set of inliers among the given feature correspondences. The use of this perception-based \acontrario principle allows us to avoid the selection of a precision threshold as in RANSAC, since we provide a decision criterion that integrates all data and method parameters (total number of points, precision threshold, number of inliers given this threshold). Our proposal is analyzed in simulated and real data experiments; it yields a significant improvement of the ORSA method proposed in 2004, in terms of reprojection error and relative motion estimation, especially in situations of low inlier ratios

New approach to calculating the fundamental matrix

The estimation of the fundamental matrix (F) is to determine the epipolar geometry and to establish a geometrical relation between two images of the same scene or elaborate video frames. In the literature, we find many techniques that have been proposed for robust estimations such as RANSAC (random sample consensus), least-squares median (LMeds), and M estimators as exhaustive. This article presents a comparison between the different detectors that are (Harris, FAST, SIFT, and SURF) in terms of detected points number, the number of correct matches and the computation speed of the ‘F’. Our method based first on the extraction of descriptors by the algorithm (SURF) was used in comparison to the other one because of its robustness, then set the threshold of uniqueness to obtain the best points and also normalize these points and rank it according to the weighting function of the different regions at the end of the estimation of the matrix''F'' by the technique of the M-estimator at eight points, to calculate the average error and the speed of the calculation ''F''. The results of the experimental simulation were applied to the real images with different changes of viewpoints, for example (rotation, lighting, and moving object), give a good agreement in terms of the counting speed of the fundamental matrix and the acceptable average error. The results of the simulation show this technique of use in real-time application

Pentagon-Match (PMatch): Identification of View-Invariant Planar Feature for Local Feature Matching-Based Homography Estimation

In computer vision, finding correct point correspondence among images plays an important role in many applications, such as image stitching, image retrieval, visual localization, etc. Most of the research works focus on the matching of local feature before a sampling method is employed, such as RANSAC, to verify initial matching results via repeated fitting of certain global transformation among the images. However, incorrect matches may still exist. Thus, a novel sampling scheme, Pentagon-Match (PMatch), is proposed in this work to verify the correctness of initially matched keypoints using pentagons randomly sampled from them. By ensuring shape and location of these pentagons are view-invariant with various evaluations of cross-ratio (CR), incorrect matches of keypoint can be identified easily with homography estimated from correctly matched pentagons. Experimental results show that highly accurate estimation of homography can be obtained efficiently for planar scenes of the HPatches dataset, based on keypoint matching results provided by LoFTR. Besides, accurate outlier identification for the above matching results and possible extension of the approach for multi-plane situation are also demonstrated.Comment: arXiv admin note: text overlap with arXiv:2211.0300

Combining local regularity estimation and total variation optimization for scale-free texture segmentation

Texture segmentation constitutes a standard image processing task, crucial to many applications. The present contribution focuses on the particular subset of scale-free textures and its originality resides in the combination of three key ingredients: First, texture characterization relies on the concept of local regularity ; Second, estimation of local regularity is based on new multiscale quantities referred to as wavelet leaders ; Third, segmentation from local regularity faces a fundamental bias variance trade-off: In nature, local regularity estimation shows high variability that impairs the detection of changes, while a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the present contribution proposes several variational problem formulations based on total variation and proximal resolutions that effectively circumvent this trade-off. Estimation and segmentation performance for the proposed procedures are quantified and compared on synthetic as well as on real-world textures

Three dimensional information estimation and tracking for moving objects detection using two cameras framework

Calibration, matching and tracking are major concerns to obtain 3D information consisting of depth, direction and velocity. In finding depth, camera parameters and matched points are two necessary inputs. Depth, direction and matched points can be achieved accurately if cameras are well calibrated using manual traditional calibration. However, most of the manual traditional calibration methods are inconvenient to use because markers or real size of an object in the real world must be provided or known. Self-calibration can solve the traditional calibration limitation, but not on depth and matched points. Other approaches attempted to match corresponding object using 2D visual information without calibration, but they suffer low matching accuracy under huge perspective distortion. This research focuses on achieving 3D information using self-calibrated tracking system. In this system, matching and tracking are done under self-calibrated condition. There are three contributions introduced in this research to achieve the objectives. Firstly, orientation correction is introduced to obtain better relationship matrices for matching purpose during tracking. Secondly, after having relationship matrices another post-processing method, which is status based matching, is introduced for improving object matching result. This proposed matching algorithm is able to achieve almost 90% of matching rate. Depth is estimated after the status based matching. Thirdly, tracking is done based on x-y coordinates and the estimated depth under self-calibrated condition. Results show that the proposed self-calibrated tracking system successfully differentiates the location of objects even under occlusion in the field of view, and is able to determine the direction and the velocity of multiple moving objects

3D SEM Surface Reconstruction: An Optimized, Adaptive, and Intelligent Approach

Structural analysis of microscopic objects is a longstanding topic in several scientific disciplines, including biological, mechanical, and material sciences. The scanning electron microscope (SEM), as a promising imaging equipment has been around to determine the surface properties (e.g., compositions or geometries) of specimens by achieving increased magnification, contrast, and resolution greater than one nanometer. Whereas SEM micrographs still remain two-dimensional (2D), many research and educational questions truly require knowledge and information about their three-dimensional (3D) surface structures. Having 3D surfaces from SEM images would provide true anatomic shapes of micro samples which would allow for quantitative measurements and informative visualization of the systems being investigated. In this research project, we novel design and develop an optimized, adaptive, and intelligent multi-view approach named 3DSEM++ for 3D surface reconstruction of SEM images, making a 3D SEM dataset publicly and freely available to the research community. The work is expected to stimulate more interest and draw attention from the computer vision and multimedia communities to the fast-growing SEM application area

Automated calibration of smartphone cameras for 3D reconstruction of mechanical pipes

This paper outlines a new framework for the calibration of optical instruments, in particular smartphone cameras, using highly redundant circular black-and-white target fields. New methods were introduced for (i) matching targets between images; (ii) adjusting the systematic eccentricity error of target centres; and (iii) iteratively improving the calibration solution through a free-network self-calibrating bundle adjustment. The proposed method effectively matched circular targets in 270 smartphone images, taken within a calibration laboratory, with robustness to type II errors (false negatives). The proposed eccentricity adjustment, which requires only camera projective matrices from two views, behaved comparably to available closed-form solutions, which require additional a priori object-space target information. Finally, specifically for the case of mobile devices, the calibration parameters obtained using the framework were found to be superior compared to in situ calibration for estimating the 3D reconstructed radius of a mechanical pipe (approximately 45% improvement on average)

Automated Calibration of Mobile Cameras for 3D Reconstruction of Mechanical Pipes

This manuscript provides a new framework for calibration of optical instruments, in particular mobile cameras, using large-scale circular black and white target fields. New methods were introduced for (i) matching targets between images; (ii) adjusting the systematic eccentricity error of target centers; and (iii) iteratively improving the calibration solution through a free-network self-calibrating bundle adjustment. It was observed that the proposed target matching effectively matched circular targets in 270 mobile phone images from a complete calibration laboratory with robustness to Type II errors. The proposed eccentricity adjustment, which requires only camera projective matrices from two views, behaved synonymous to available closed-form solutions, which require several additional object space target information a priori. Finally, specifically for the case of the mobile devices, the calibration parameters obtained using our framework was found superior compared to in-situ calibration for estimating the 3D reconstructed radius of a mechanical pipe (approximately 45% improvement)

Modelling bargaining behaviors within biotech clusters - Towards the "power of the weak" emergence?

If spatial and industrial economics theorical models, such as industrial districts, clusters, or learning regions propose a large analysis of differentiated coordination mecanisms, it however not really takes into account behavior of dispute dynamics, such as conflict of bargaining and power, which can explain both diversity and ambivalence of local coordinations. So, our purpose in this contribution is to bring to light that bargaining and power conflicts are at stake in coordinations structuration within territories. We base this contribution on Artificial Life simulations involving public and private local actors who bargain to share a local resource using more or less sophisticated strategies. On a methodologic point of view, our thought is based on an empirical established fact. Analysis of a biotechnology cluster in Toulouse-France (Leroux I., 2002, 2004) indeed contributes to bring to light that coordinations involving pharmaceutical industry, local communities and local research laboratories are based on direct or indirect evolving domination and concession bargaining games. If industrial firms play "the power of the weak" game, making concession of their decision power to public research laboratories, they endeavour systematically to exerce an influence or a discrimination power, by using hided and indirect means that forward by local communities.Starting from this established fact, we propose Artificial Life simulations of local bargaining games, inspired from the T. Ellingsen (1997) bargaining evolutionnary game. This is a Nash demand game under ultimatum. It leads to the interaction of obstinate agents whose demands are independent of those of the adversaries, and sophisticated agents who adapt their demand to that hoped for of their adversaries rather than gain nothing. As a result, our simulations show that bargainings between these local actors lead to an agreement which is not a perfect share, or an "universal" rule, but a compromise frequently hiding complex mecanisms of domination and concession. The main contribution of these simulations, which are based on genetic algorithms, is to put in a prominent position the variations of behavioral rules. We show how bargaining is an evolving processus based on domination and concession behaviors (influence, coercion,…) bringing to light the T. Schelling (1960) "power of the weak". This result brings to the fore the question of flexibility and phasing dynamics of power behaviors in local coordination bargainings. This model can contributes to open new researches focused on power and conflict strategies within local coordinations.