Learning how to be robust: Deep polynomial regression

Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results when the input data is heavily contaminated by outliers. Moreover, the problem is even harder when outliers have strong structure. Departing from problem-tailored heuristics for robust estimation of parametric models, we explore deep convolutional neural networks. Our work aims to find a generic approach for training deep regression models without the explicit need of supervised annotation. We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand. We demonstrate the value of our findings by comparing with standard robust regression methods. Furthermore, we demonstrate how to use such models for a real computer vision problem, i.e., video stabilization. The qualitative and quantitative experiments show that neural networks are able to learn robustness for general polynomial regression, with results that well overpass scores of traditional robust estimation methods.Comment: 18 pages, conferenc

3D MODELING WITH PHOTOGRAMMETRY BY UAVS AND MODEL QUALITY VERIFICATION

This paper deals with a test lead by Geomatics laboratory (DICEAM, Mediterranea University of Reggio Calabria), concerning the application of UAV photogrammetry for survey, monitoring and checking. The study case relies with the surroundings of the Department of Agriculture Sciences. In the last years, such area was interested by landslides and survey activities carried out to take the phenomenon under control. For this purpose, a set of digital images were acquired through a UAV equipped with a digital camera and GPS. Successively, the processing for the production of a 3D georeferenced model was performed by using the commercial software Agisoft PhotoScan. Similarly, the use of a terrestrial laser scanning technique allowed to product dense cloud and 3D models of the same area. To assess the accuracy of the UAV-derived 3D models, a comparison between image and range-based methods was performed

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

OUTLIERS DETECTION BY RANSAC ALGORITHM IN THE TRANSFORMATION OF 2D COORDINATE FRAMES

Over the years there have been a number of different computational methodsthat allow for the identification of outliers. Methods for robust estimation are knownin the set of M-estimates methods (derived from the method of MaximumLikelihood Estimation) or in the set of R-estimation methods (robust estimationbased on the application of some rank test). There are also algorithms that are notclassified in any of these groups but these methods are also resistant to gross errors,for example, in M-split estimation. Another proposal, which can be used to detectoutliers in the process of transformation of coordinates, where the coordinates ofsome points may be affected by gross errors, can be a method called RANSACalgorithm (Random Sample and Consensus). The authors present a study that wasperformed in the process of 2D transformation parameter estimation usingRANSAC algorithm to detect points that have coordinates with outliers. Thecalculations were performed in three scenarios on the real geodetic network.Selected coordinates were burdened with simulated values of errors to confirm theefficiency of the proposed method

Graph matching with a dual-step EM algorithm

This paper describes a new approach to matching geometric structure in 2D point-sets. The novel feature is to unify the tasks of estimating transformation geometry and identifying point-correspondence matches. Unification is realized by constructing a mixture model over the bipartite graph representing the correspondence match and by affecting optimization using the EM algorithm. According to our EM framework, the probabilities of structural correspondence gate contributions to the expected likelihood function used to estimate maximum likelihood transformation parameters. These gating probabilities measure the consistency of the matched neighborhoods in the graphs. The recovery of transformational geometry and hard correspondence matches are interleaved and are realized by applying coupled update operations to the expected log-likelihood function. In this way, the two processes bootstrap one another. This provides a means of rejecting structural outliers. We evaluate the technique on two real-world problems. The first involves the matching of different perspective views of 3.5-inch floppy discs. The second example is furnished by the matching of a digital map against aerial images that are subject to severe barrel distortion due to a line-scan sampling process. We complement these experiments with a sensitivity study based on synthetic data

Image mosaicing of panoramic images

Image mosaicing is combining or stitching several images of a scene or object taken from different angles into a single image with a greater angle of view. This is practised a developing field. Recent years have seen quite a lot of advancement in the field. Many algorithms have been developed over the years. Our work is based on feature based approach of image mosaicing. The steps in image mosaic consist of feature point detection, feature point descriptor extraction and feature point matching. RANSAC algorithm is applied to eliminate variety of mismatches and acquire transformation matrix between the images. The input image is transformed with the right mapping model for image stitching. Therefore, this paper proposes an algorithm for mosaicing two images efficiently using Harris-corner feature detection method, RANSAC feature matching method and then image transformation, warping and by blending methods