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Estimating the distance to an object from grayscale stereo images using deep learning

Kulawik Joanna

Journal of Applied Mathematics and Computational Mechanics

2022

Vol. 21, nr 4

60--72

This article presents an innovative proposal for estimating the distance between an autonomous vehicle and an object in front of it. Such information can be used, for example, to support the process of controlling an autonomous vehicle. The primary source of information in research is monochrome stereo images. The images were made in compliance with the laws of the canonical order. The developed convolutional neural network model was used for the estimation. A proprietary dataset was developed for the experiments. The analysis was based on the phenomenon of disparity in stereo images. As a result of the research, a correctly trained model of the CNN network was obtained in six variants. High accuracy of distance estimation was achieved. This publication describes an original proposal for a hybrid blend of digital image analysis, stereo-vision, and deep learning for engineering applications.

Exploratory data analysis for outlier detection in bioequivalence studies

Mogoş B.

Biocybernetics and Biomedical Engineering

2013

Vol. 33, no. 3

164--170

Exploratory Data Analysis techniques are recognized as useful tools in outlier detection through visual representations. One limitation of this direction is the lack of studies concerning the reliability of the visual interpretation. In this paper we propose a method that combines an Exploratory Data Analysis technique, Andrews curves, with a statistical approach which can be applied to automatically classify the data. Using a simulation study we show that the results provided by the Andrews curves approach are markedly superior to the estimates distance test (the best proposed method for detecting outliers revealed in the literature) for the crossover bioequivalence design.