Pedestrian Detection Algorithms Using Shearlets
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Pedestrian Detection Algorithms using Shearlets
Author: Lienhard Pfeifer
language: en
Publisher: Logos Verlag Berlin GmbH
Release Date: 2019-01-15
In this thesis, we investigate the applicability of the shearlet transform for the task of pedestrian detection. Due to the usage of in several emerging technologies, such as automated or autonomous vehicles, pedestrian detection has evolved into a key topic of research in the last decade. In this time period, a wealth of different algorithms has been developed. According to the current results on pedestrian detection benchmarks, the algorithms can be divided into two categories. First, application of hand-crafted image features and of a classifier trained on these features. Second, methods using Convolutional Neural Networks in which features are learned during the training phase. It is studied how both of these types of procedures can be further improved by the incorporation of shearlets, a framework for image analysis which has a comprehensive theoretical basis. To this end, we adapt the shearlet framework according to the requirements of the practical application of pedestrian detection algorithms. One main application area of pedestrian detection is located in the automotive domain. In this field, algorithms have to be runable on embedded devices. Therefore, we study the embedded realization of a pedestrian detection algorithm based on the shearlet transform.
Advances in Simulation and Digital Human Modeling
This book provides readers with a timely snapshot of modeling and simulation tools, including virtual and mixed-reality environment, for human factors research. It covers applications in healthcare and physical ergonomics, military and transportation systems, industrial monitoring, as well as economics and social sciences. Based on the AHFE 2021 International Conference on Human Factors and Simulation and the AHFE 2021 International Conference on Digital Human Modeling and Applied Optimization, held virtually on 25–29 July, 2021, from USA, the book offers a unique resource for modelling and simulation researchers seeking insights into human factors research and to human factors experts seeking reliable computational tools.
A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images
A fundus image is an effective tool for ophthalmologists studying eye diseases. Retinal vessel detection is a significant task in the identification of retinal disease regions. This study presents a retinal vessel detection approach using shearlet transform and indeterminacy filtering. The fundus image’s green channel is mapped in the neutrosophic domain via shearlet transform. The neutrosophic domain images are then filtered with an indeterminacy filter to reduce the indeterminacy information. A neural network classifier is employed to identify the pixels whose inputs are the features in neutrosophic images. The proposed approach is tested on two datasets, and a receiver operating characteristic curve and the area under the curve are employed to evaluate experimental results quantitatively. The area under the curve values are 0.9476 and 0.9469 for each dataset respectively, and 0.9439 for both datasets. The comparison with the other algorithms also illustrates that the proposed method yields the highest evaluation measurement value and demonstrates the efficiency and accuracy of the proposed method.â