Vegetation detection and terrain classification for autonomous navigation

Diese Arbeit beleuchtet sieben neuartige Ansätze aus zwei Bereichen der maschinellen Wahrnehmung: Erkennung von Vegetation und Klassifizierung von Gelände. Diese Elemente bilden den Kern eines jeden Steuerungssystems für effiziente, autonome Navigation im Außenbereich. Bezüglich der Vegetationserkennung, wird zuerst ein auf Indizierung basierender Ansatz beschrieben (1), der die reflektierenden und absorbierenden Eigenschaften von Pflanzen im Bezug auf sichtbares und nah-infrarotes Licht auswertet. Zweitens wird eine Fusionmethode von 2D/3D Merkmalen untersucht (2), die das menschliche System der Vegetationserkennung nachbildet. Zusätzlich wird ein integriertes System vorgeschlagen (3), welches die visuelle Wahrnehmung mit multi-spektralen Methoden ko mbiniert. Aufbauend auf detaillierten Studien zu Farb- und Textureigenschaften von Vegetation wird ein adaptiver selbstlernender Algorithmus eingeführt der robust und schnell Pflanzen(bewuchs) erkennt (4). Komplettiert wird die Vegetationserkennung durch einen Algorithmus zur Befahrbarkeitseinschätzung von Vegetation, der die Verformbarkeit von Pflanzen erkennt. Je leichter sich Pflanzen bewegen lassen, umso größer ist ihre Befahrbarkeit. Bezüglich der Geländeklassifizierung wird eine struktur-basierte Methode vorgestellt (6), welche die 3D Strukturdaten einer Umgebung durch die statistische Analyse lokaler Punkte von LiDAR Daten unterstützt. Zuletzt wird eine auf Klassifizierung basierende Methode (7) beschrieben, die LiDAR und Kamera-Daten kombiniert, um eine 3D Szene zu rekonstruieren. Basierend auf den Vorteilen der vorgestellten Algorithmen im Bezug auf die maschinelle Wahrnehmung, hoffen wir, dass diese Arbeit als Ausgangspunkt für weitere Entwicklung en von zuverlässigen Erkennungsmethoden dient.This thesis introduces seven novel contributions for two perception tasks: vegetation detection and terrain classification, that are at the core of any control system for efficient autonomous navigation in outdoor environments. Regarding vegetation detection, we first describe a vegetation index-based method (1), which relies on the absorption and reflectance properties of vegetation to visual light and near-infrared light, respectively. Second, a 2D/3D feature fusion (2), which imitates the human visual system in vegetation interpretation, is investigated. Alternatively, an integrated vision system (3) is proposed to realise our greedy ambition in combining visual perception-based and multi-spectral methods by only using a unit device. A depth study on colour and texture features of vegetation has been carried out, which leads to a robust and fast vegetation detection through an adaptive learning algorithm (4). In addition, a double-check of passable vegetation detection (5) is realised, relying on the compressibility of vegetation. The lower degree of resistance vegetation has, the more traversable it is. Regarding terrain classification, we introduce a structure-based method (6) to capture the world scene by inferring its 3D structures through a local point statistic analysis on LiDAR data. Finally, a classification-based method (7), which combines the LiDAR data and visual information to reconstruct 3D scenes, is presented. Whereby, object representation is described more details, thus enabling an ability to classify more object types. Based on the success of the proposed perceptual inference methods in the environmental sensing tasks, we hope that this thesis will really serve as a key point for further development of highly reliable perceptual inference methods

Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes

Engineering Data Compendium. Human Perception and Performance, Volume 1

The concept underlying the Engineering Data Compendium was the product an R and D program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design of military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by system designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is Volume 1, which contains sections on Visual Acquisition of Information, Auditory Acquisition of Information, and Acquisition of Information by Other Senses

KINE[SIS]TEM'17 From Nature to Architectural Matter

Kine[SiS]tem – From Kinesis + System. Kinesis is a non-linear movement or activity of an organism in response to a stimulus. A system is a set of interacting and interdependent agents forming a complex whole, delineated by its spatial and temporal boundaries, influenced by its environment. How can architectural systems moderate the external environment to enhance comfort conditions in a simple, sustainable and smart way? This is the starting question for the Kine[SiS]tem’17 – From Nature to Architectural Matter International Conference. For decades, architectural design was developed despite (and not with) the climate, based on mechanical heating and cooling. Today, the argument for net zero energy buildings needs very effective strategies to reduce energy requirements. The challenge ahead requires design processes that are built upon consolidated knowledge, make use of advanced technologies and are inspired by nature. These design processes should lead to responsive smart systems that deliver the best performance in each specific design scenario. To control solar radiation is one key factor in low-energy thermal comfort. Computational-controlled sensor-based kinetic surfaces are one of the possible answers to control solar energy in an effective way, within the scope of contradictory objectives throughout the year.FC

NASA Tech Briefs, May 1994

Topics covered include: Robotics/Automation; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports

Gesture and listening: towards a social and eco-systemic hyperinstrument composition

The research implements interactive music processes involving sound synthesis and symbolic treatments within a single environment. The algorithms are driven by classical instrumental performance through hybrid systems called hyperinstruments, in which the sensing of the performance gestures leads to open and goal-oriented generative music forms. The interactions are composed with MAX/Msp, designing contexts and relationships between real-time instrumental timbre analysis (sometimes with added inertial motion tracking) with a gesture-based idea of form shaping. Physical classical instruments are treated as interfaces, giving rise to the need to develop unconventional mapping strategies on account of the multi-dimensional and interconnecting quality of timbre. Performance and sound gestures are viewed as salient energies, phrasings and articulations carrying information about human intentions, in this way becoming able to change the musical behaviour of a composition inside a coded dramaturgy. The interactive networks are designed in order to integrate traditional music practices and “languages” with computational systems designed to be self-regulating, through the mediation of timbre space and performance gestural descriptions. Following its classic definition, technology aims to be mainly related not to mechanical practices but rather to rhetorical approaches: for this reason the software often foresees interactive scores, and must be performed in accordance with a set of external verbal (and video) explanations, whose technical detail should nevertheless not impair the most intuitive approach to music making

Edgeless Active Contours for Natural Color Images based on Angular Mapping and Level Sets

Abstract: We propose an angular map driven active contouring based on curve evolution using level sets to extract object from natural color images. The constraint parameters to stop the intrinsically evolving curve on the angular map of the color space (RGB used) are derived based upon a modified minimal partition problem

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum