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Stereo --- Blaasinstrumenten --- Percussie

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This thesis is realized in the context of the RoboCup contest: a competition where two teams of robots play against each other in a soccer game. The purpose of this work is to determine if a stereo vision system could be implemented on a constrained robot platform, and provide, in real-time, useful 3D information about the playing area and the game elements. This paper starts by giving a theoretical explanation of the principles of stereo vision systems, followed by a quick review of the state of the art. As the computation power of the considered robot platform is limited, this paper then proposes an adaptation of an algorithm developed by Sudeep Pillai, claiming to achieve a good semi-dense approximation of the 3D environment at a frame rate that can reach 120Hz on a single CPU thread. The testing of this algorithm with a stereo setup of two wide-lens cameras separated by a small distance shows that the depth of a soccer ball can be estimated with a mean absolute error of 5cm/m, by directly looking at the depth of generated 3D points supposed to belong to the ball. Another analysis also reveals that the inclination at which the floor is observed by the cameras can be estimated with a precision of less than 1 degree. It is thus likely that the accuracy of the ball localization could be further improved taking advantage of this precise floor plane estimation instead of assuming that the 3D surface of a ball would always be correctly rendered by the algorithm.

stereo --- vision --- stereovision --- robotics --- robot --- humanoid --- 3D --- soccer --- Robocup --- Ingénierie, informatique & technologie > Sciences informatiques

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Nowadays, broadcasters must continually innovate to create compelling content that evokes emotions in viewers. Photo-realistic depth of field effect, augmented reality, or even 3D reconstruction could be one way to achieve this. Depth estimation is one of the steps to video understanding that allows these applications. 

In this master thesis, methods to estimate a depth map from a single image were explored. Especially, deep learning methods were preferred to those based on geometry or the use of sensors. Thus, the solution provided by this work is entirely software-based. 

In particular, a custom model has been created for a sports broadcast dataset. However, this dataset does not contain any ground truth depth map. Hence, self-supervised monocular depth estimation methods were investigated in this document. Specifically, trainings were performed with non-rectified and synchronized stereo images captured with uncalibrated cameras. In contrast to trainings based on monocular sequences, the scenes are static and the only movement is that of the camera. This is the ideal configuration for training with self-supervised methods. However, to our knowledge, we are the only ones to have attempted to train self-supervised monocular depth estimation methods using non-rectified stereo images captured with uncalibrated cameras. More precisely, in this work, the intrinsic and extrinsic parameters of the cameras are estimated during training along with the depth map. 

During the course of this research, we were able to demonstrate the ability of self-supervised methods to predict coherent depth maps when trained on non-rectified stereo sports broadcast videos captured with uncalibrated cameras. Moreover, we addressed the limitations regarding the generation of depth maps under those conditions.

Deep learning --- computer vision --- depth estimation --- self-supervised --- monodepth2 --- stereo vision --- monocular vision --- Ingénierie, informatique & technologie > Sciences informatiques

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The 5th IEEE International Conference on Applied System Innovation 2019 (IEEE ICASI 2019, https://2019.icasi-conf.net/), which was held in Fukuoka, Japan, on 11–15 April, 2019, provided a unified communication platform for a wide range of topics. This Special Issue entitled “Selected Papers from IEEE ICASI 2019” collected nine excellent papers presented on the applied sciences topic during the conference. Mechanical engineering and design innovations are academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation by mechanical engineering includes information technology (IT)-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology. These new technologies that implant intelligence in machine systems represent an interdisciplinary area that combines conventional mechanical technology and new IT. The main goal of this Special Issue is to provide new scientific knowledge relevant to IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology.

HVAC energy conservation --- optimal control --- genetic algorithm --- FCU group control --- central pattern generator --- Theo Jansen Linkage --- non-collocated actuators --- fast Fourier transform --- FFT --- kernel --- MIMO --- OFDM --- multistandard --- electrical resistivity tomography --- boundary effect --- 3D effect --- error compensation --- free-form surface --- on-machine measurement --- mirror compensation method --- stereo matching --- cost aggregation --- image filtering --- binocular stereo vision --- deep learning for network security --- long short-term memory --- malicious traffic classification --- assembly design --- usability and operation complexity --- fuzzy theory --- product design --- layout strategy --- design method --- n/a

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The 5th IEEE International Conference on Applied System Innovation 2019 (IEEE ICASI 2019, https://2019.icasi-conf.net/), which was held in Fukuoka, Japan, on 11–15 April, 2019, provided a unified communication platform for a wide range of topics. This Special Issue entitled “Selected Papers from IEEE ICASI 2019” collected nine excellent papers presented on the applied sciences topic during the conference. Mechanical engineering and design innovations are academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation by mechanical engineering includes information technology (IT)-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology. These new technologies that implant intelligence in machine systems represent an interdisciplinary area that combines conventional mechanical technology and new IT. The main goal of this Special Issue is to provide new scientific knowledge relevant to IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology.

History of engineering & technology --- HVAC energy conservation --- optimal control --- genetic algorithm --- FCU group control --- central pattern generator --- Theo Jansen Linkage --- non-collocated actuators --- fast Fourier transform --- FFT --- kernel --- MIMO --- OFDM --- multistandard --- electrical resistivity tomography --- boundary effect --- 3D effect --- error compensation --- free-form surface --- on-machine measurement --- mirror compensation method --- stereo matching --- cost aggregation --- image filtering --- binocular stereo vision --- deep learning for network security --- long short-term memory --- malicious traffic classification --- assembly design --- usability and operation complexity --- fuzzy theory --- product design --- layout strategy --- design method --- n/a