Классификация, сравнение и анализ систем подачи газового топлива для питания дизельных двигателей

In this paper we present a new method to implement a robust estimator: B-spline channel smoothing. We show that linear smoothing of channels is equivalent to a robust estimator, where we make use of the channel representation based upon quadratic B-splines. The linear decoding from B-spline channels allows to derive a robust error norm which is very similar to Tukey's biweight error norm. Using channel smoothing instead of iterative robust estimator implementations like non-linear diffusion, bilateral filtering, and mean-shift approaches is advantageous since channel smoothing is faster, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on non-linear spaces, such as orientation space. As an application, we implemented orientation smoothing and compared it to the other three approaches

Hinge-Wasserstein: Mitigating Overconfidence in Regression by Classification

Modern deep neural networks are prone to being overconfident despite their drastically improved performance. In ambiguous or even unpredictable real-world scenarios, this overconfidence can pose a major risk to the safety of applications. For regression tasks, the regression-by-classification approach has the potential to alleviate these ambiguities by instead predicting a discrete probability density over the desired output. However, a density estimator still tends to be overconfident when trained with the common NLL loss. To mitigate the overconfidence problem, we propose a loss function, hinge-Wasserstein, based on the Wasserstein Distance. This loss significantly improves the quality of both aleatoric and epistemic uncertainty, compared to previous work. We demonstrate the capabilities of the new loss on a synthetic dataset, where both types of uncertainty are controlled separately. Moreover, as a demonstration for real-world scenarios, we evaluate our approach on the benchmark dataset Horizon Lines in the Wild. On this benchmark, using the hinge-Wasserstein loss reduces the Area Under Sparsification Error (AUSE) for horizon parameters slope and offset, by 30.47% and 65.00%, respectively

Window Matching using Sparse Templates

This report describes a novel window matching technique. We perform window matching by transforming image data into sparse features, and apply a computationally efficient matching technique in the sparse feature space. The gain in execution time for the matching is roughly 10 times compared to full window matching techniques such as SSD, but the total execution time for the matching also involves an edge filtering step. Since the edge responses may be used for matching of several regions, the proposed matching technique is increasingly advantageous when the number of regions to keep track of increases, and when the size of the search window increases. The technique is used in a real-time ego-motion estimation system in the WITAS project. Ego-motion is estimated by tracking of a set of structure points, i.e. regions that do not have the aperture problem. Comparisons with SSD, with regard to speed and accuracy are made

Observations Concerning Reconstructions with Local Support

This report describes how the choice of kernel affects a non-parametric density estimation. Methods for accurate localisation of peaks in the estimated densities are developed for Gaussian and cos2 kernels. The accuracy and robustness of the peak localisation methods are studied with respect to noise, number of samples, and interference between peaks. Although the peak localisation is formulated in the framework of non-parametric density estimation, the results are also applicable to associative learning with localised responses

Image Analysis using Soft Histograms

This paper advocates the use of overlapping bins in histogram creation. It is shown how conventional histogram creation has an inherent quantisation that cause errors much like those in sampling with insufficient band limitation. The use of overlapping bins is shown to be the deterministic equivalent to dithering. Two applications of soft histograms are shown: Improved peak localisation in an estimated probability density function (PDF) without requiring more samples, and accurate estimation of image rotation

Updating Camera Location and Heading using a Sparse Displacement Field

This report describes the principles of an algorithm developed within the WITAS project. The goal of the WITAS project is to build an autonomous helicopter that can navigate autonomously, using differential GPS, GIS-data of the underlying terrain (elevation models and digital orthophotographs) and a video camera. Using differential GPS and other non-visual sensory equipment, the system is able to obtain crude estimates of its position and heading direction. These estimates can be refined by matching of camera-images and the on-board GIS-data. This refinement process, however is rather time consuming, and will thus only be made every once in a while. For real-time refinement of camera position and heading, the system will iteratively update the estimates using frame to frame correspondence only. In each frame a sparse set of image displacement estimates is calculated, and from these the perspective in the current image can be found. Using the calculated perspective and knowledge of the camera parameters, new values of camera position and heading can be obtained. The resultant camera position and heading can exhibit a slow drift if the original alignment was not perfect, and thus a corrective alignment with GIS-data should be performed once every minute or so

Image Analysis using Soft Histograms

This paper advocates the use of overlapping bins in histogram creation. It is shown how conventional histogram creation has an inherent quantisation that cause errors much like those in sampling with insufficient band limitation. The use of overlapping bins is shown to be the deterministic equivalent to dithering. Two applications of soft histograms are shown: Improved peak localisation in an estimated probability density function (PDF) without requiring more samples, and accurate estimation of image rotation

Channel Smoothing using Integer Arithmetic

This paper presents experiments on using integer arithmetic with the channel representation. Integer arithmetic allows reduction of memory requirements, and allows efficient implementations using machine code vector instructions, integer-only CPUs, or dedicated programmable hardware such as FPGAs possible. We demonstrate the effects of discretisation on a non-iterative robust estimation technique called channel smoothing, but the results are also valid for other applications

Successive Recognition using Local State Models

This paper describes how a world model for successive recognition can be learned using associative learning. The learned world model consists of a linear mapping that successively updates a high-dimensional system state using performed actions and observed percepts. The actions of the system are learned by rewarding actions that are good at resolving state ambiguities. As a demonstration, the system is used to resolve the localisation problem in a labyrinth