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Auflistung Künstliche Intelligenz 29(1) - März 2015 nach Titel
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- ZeitschriftenartikelA Required Paradigm Shift in Today’s Vision Research(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Krüger, Norbert
- ZeitschriftenartikelAttentional Scene-Exploration and Object Discovery in Image and RGB-D Data(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Martín García, Germán; Werner, Thomas; Frintrop, SimoneIn 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.
- ZeitschriftenartikelBeyond Simple and Complex Neurons: Towards Intermediate-level Representations of Shapes and Objects(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Rodríguez-Sánchez, Antonio; Neumann, Heiko; Piater, JustusKnowledge 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.
- ZeitschriftenartikelBio-inspired Vision Systems(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Frintrop, Simone
- ZeitschriftenartikelDeep Representation Hierarchies for 3D Active Vision(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Sabatini, Silvio P.Starting from the acknowledged properties of visual cortical neurons, we show how diversified and composite visual descriptors come up from different hierarchical combinations of the harmonic content of the visual signal. The resulting deep hierarchy networks can specialize to solve different tasks and trigger different behaviors, without necessarily getting through an explicit measure of the re-constructive visual attributes of the observed scene. Distinct specializations for stereopis and for active control of the vergence movements of a binocular system are presented. In particular, the advantage of not abandoning distributed representations of multiple solutions to prematurely construct integrated description of cognitive entities and commit the system to a particular behavior is discussed. Pilot CPU-GPU implementations of the proposed cortical-like architectures prove to be promising solutions for the next-generation of robot vision systems, which should be capable of calibrating and adapting autonomously through the interaction with the environment.
- ZeitschriftenartikelEfficient Learning of Pre-attentive Steering in a Driving School Framework(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Rudzits, Reinis; Pugeault, NicolasAutonomous 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.
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015)
- ZeitschriftenartikelObject Detection for Robotic Applications Using Perceptual Organization in 3D(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Richtsfeld, Andreas; Zillich, Michael; Vincze, MarkusObject segmentation of unknown objects with arbitrary shape in cluttered scenes is still a challenging task in computer vision. A framework is introduced to segment RGB-D images where data is processed in a hierarchical fashion. After pre-segmentation and parametrization of surface patches, support vector machines are used to learn the importance of relations between these patches. The relations are derived from perceptual grouping principles. The proposed framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. Furthermore, the problem of segmenting partially occluded objects is tackled.
- ZeitschriftenartikelSpatial Cognition of Humans and Brain-inspired Artificial Agents(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Hamker, FredPresent vision systems primarily operate on still images or an image sequence but hardly consider continuous perception across actions. If sensors are attached to the body of a human-like agent who interacts with the environment, several questions arise about how to update the reference systems with each action. In our European research project “Spatial Cognition” we address this topic by a combination of experimental and computational work which should finally merge into a large-scale model of human-like space perception and spatial memory being tested on a humanoid agent in virtual reality.
- ZeitschriftenartikelSpecial Issue on Bio-inspired Vision Systems(KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Zillich, Michael; Krüger, Norbert