http://dl.gi.de/handle/20.500.12116/11087 2026-07-23T10:27:19Z http://dl.gi.de/handle/20.500.12116/11446 Efficient Learning of Pre-attentive Steering in a Driving School Framework Rudzits, Reinis; Pugeault, Nicolas Autonomous driving is an extremely challenging problem and existing driverless cars use non-visual sensing to palliate the limitations of machine vision approaches. This paper presents a driving school framework for learning incrementally a fast and robust steering behaviour from visual gist only. The framework is based on an autonomous steering program interfacing in real time with a racing simulator: hence the teacher is a racing program having perfect insight into its position on the road, whereas the student learns to steer from visual gist only. Experiments show that (i) such a framework allows the visual driver to drive around the track successfully after a few iterations, demonstrating that visual gist is sufficient input to drive the car successfully; and (ii) the number of training rounds required to drive around a track reduces when the student has experienced other tracks, showing that the learnt model generalises well to unseen tracks. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11449 Special Issue on Bio-inspired Vision Systems Zillich, Michael; Krüger, Norbert 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11444 Attentional Scene-Exploration and Object Discovery in Image and RGB-D Data Martín García, Germán; Werner, Thomas; Frintrop, Simone In this paper, we summarize our project work of the last two years, where we addressed the tasks of visually exploring a scene with visual attention mechanisms based on saliency computation, and of locating unknown objects in the environment. The latter is also called object discovery and consists in finding candidate objects without previous knowledge about the objects themselves or the scene. We follow an approach motivated from human perception and combine saliency and segmentation to generate object candidates. We show results on 2D images as well as on 3D sequences obtained from an RGB-D camera. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11450 Beyond Simple and Complex Neurons: Towards Intermediate-level Representations of Shapes and Objects Rodríguez-Sánchez, Antonio; Neumann, Heiko; Piater, Justus Knowledge of the brain has much advanced since the concept of the neuron doctrine developed by Ramón y Cajal (R Trim Histol Norm Patol 1:33–49, 1888). Over the last six decades a wide range of functionalities of neurons in the visual cortex have been identified. These neurons can be hierarchically organized into areas since neurons cluster according to structural properties and related function. The neurons in such areas can be characterized to a first order approximation by their (static) receptive field function, viz their filter characteristic implemented by their connection weights to neighboring cells. This paper aims to provide insights on the steps that computer models in our opinion must pursue in order to develop robust recognition mechanisms that mimic biological processing capabilities beyond the level of cells with classical simple and complex receptive field response properties. We stress the importance of intermediate-level representations to achieve higher-level object abstraction in the context of feature representations, and summarize two current approaches that we consider are advances toward achieving that goal. 2015-01-01T00:00:00Z