A multi-aperture optical flow estimation method for an artificial compound eye

© 2019 IOS Press and the authors. All rights reserved. An artificial compound eye (ACE) is a bio-inspired vision sensor which mimics a natural compound eye (typical of insects). This artificial eye is able to visualize large fields of the outside world through multi-aperture. Due to its functioning, the ACE is subject to optical flow, that is an apparent motion of the object visualized by the eye. This paper proposes a method to estimate the optical flow based on capturing multiple images (multi-aperture). In this method, based on descriptors-based initial optical flows, a unified global energy function is presented to incorporate the information of multi-aperture and simultaneously recover the optical flows of multi-aperture. The energy function imposes a compound flow fields consistency assumption along with the brightness constancy and piecewise smoothness assumptions. This formula efficiently binds the flow field in time and space, and further enables view-consistent optical flow estimation. Experimental results on real and synthetic data demonstrate that the proposed method recovers view-consistent optical flows crossed multi-aperture and performs better than other optical flow methods on the multi-aperture images

Real-time visual off-road path detection

In this paper, we propose a fast and real-time capable system for visual off-road path detection. We equipped our robot AMOR with a single monocular camera and explored unstructured environments like woods. In these areas, it is almost harder to identify and classify drivable and non-drivable parts in an image. In urban regions, roads can be detected by lane markers or delimitations whereas the boundaries of a forest path blend into the environment almost seamlessly. In our work, we developed a software system that is based on mostly simple and low computationally intensive algorithms. We developed and tested the functions with a large dataset of camera images and also generated a manually Ground Truth for the evaluation

Appearance Based Recognition of Complex Objects by Genetic Prototype-Learning

This paper describes a method to recognize and classify complex objects in digital images. To this end, a uniform representation of prototypes is introduced. The notion of a prototype describes a set of local features which allow to recognize objects by their appearance. During a training step a genetic algorithm is applied to the prototypes to optimize them with regard to the classification task. After training the prototypes are compactly stored in a decision tree which allows a fast detection of matches between prototypes and images. The proposed method is tested with natural images of highway scenes, which were divided into 15 classes (including one class for rejection). The learning process is documented and the results show a classification rate of up to 93 percent for the training and test samples

Autonomous Vehicle Steering based on Evaluative Feedback by Reinforcement Learning

Abstract. Steering an autonomous vehicle requires the permanent adaptation of behavior in relation to the various situations the vehicle is in. This paper describes a research which implements such adaptation and optimization based on Reinforcement Learning (RL) which in detail purely learns from evaluative feedback in contrast to instructive feedback. Convergence of the learning process has been achieved at various experimental results revealing the impact of the different RL parameters. While using RL for autonomous steering is in itself already a novelty, additional attention has been given to new proposals for post-processing and interpreting the experimental data. 1

A Learning Algorithm for the Appearance- Based Recognition of Complex Objects

Abstract — This paper addresses the problem of recognizing complex objects in images. The proposed approach is based on a prototype-centered object representation which describes objects as sets of local features. During an evolutionary learning step the model is derived from a set of sample images. The proceeding of the training is measured with regard to the recognition rate as well as the coverage of the training samples. The proposed method is tested by the recognition of 14 classes of cars in highway scenes. A classification rate of 98 percent is achieved. Index Terms — Image processing, object recognition, decision trees, genetic algorithms

Autonomous Driving through Intelligent Image Processing and Machine Learning

Overview. This abstract describes the current research in the area of autonomously driving of a vehicle along different road courses [1]. The focus of this paper are two main aspects: firstly, parameters of the environment are being extracted from a video image coming from one single camera which is installed in or in front of the vehicle which is to drive along the road course; secondly, the incoming images from the camera need to be processed by a computer system that way, that not only Steering Commands for the vehicle are being generated (for accelerator / brake as well as the steering wheel) but the appropriateness of those Steering Commands is being constantly weighed and continuously improved over time. Consequently, the current work focuses on a system which is able to learn and to develop completely on its own the ability to steer different vehicles in different environments and combines research in the areas of Intelligent Image Processing, Machine Learning and Robotics

K.-D.: Appearance Based Recognition of Complex Objects by Genetic Prototype-Learning

This paper describes a method to recognize and classify complex objects in digital images. To this end, a uniform representation of prototypes is introduced. The notion of a prototype describes a set of local features which allow to recognize objects by their appearance. During a training step a genetic algorithm is applied to the prototypes to optimize them with regard to the classification task. After training the prototypes are compactly stored in a decision tree which allows a fast detection of matches between prototypes and images. The proposed method is tested with natural images of highway scenes, which were divided into 15 classes (including one class for rejection). The learning process is documented and the results show a classification rate of up to 93 percent for the training and test samples

Fish motion capture with refraction synthesis

3D fish animations become more and more popular in fish behavioral research. It empowers the experimenter to design fish stimuli and their specific behavior to the experiment’s needs. The fish animation can be done manually or derived from video footage. Especially automatic fish model parameter recovery for 3D animations is not well studied yet. Here we present a novel, flexible method for this purpose. It can be used to recover position, pose, bone rotation and size from single or multiple view and for single or multiple fish. Additionally we implement a novel method to compensate the fish tank’s refraction effect and show that this method can decrease the error up to 80 %. We successfully applied the proposed method to two different data sets and recovered fish parameters out of single- and double-view video stream. A video attached to this paper demonstrates the results