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This paper presents a new generic framework for human visual system inspired object detection and recognition and introduces the idea of feature extraction based on the human visual sensitivity. These methods can greatly enhance robotic vision applications. Additionally a new computationally effective object detection algorithm is presented based on image morphology and visual sensitivity. This new...
Detection of moving objects is a key component in mobile robotic perception and understanding of the environment. In this paper, we describe a realtime independent motion detection algorithm for this purpose. The method is robust and is capable of detecting difficult degenerate motions, where the moving objects is followed by a moving camera in the same direction. This robustness is attributed to...
This paper proposes a model for trail detection that builds upon the observation that trails are salient structures in the robot's visual field. Due to the complexity of natural environments, the straightforward application of bottom-up visual saliency models is not sufficiently robust to predict the location of trails. As for other detection tasks, robustness can be increased by modulating the saliency...
This paper presents a novel encoding method for scene change detection and appearance-based topological localization framework. The relation computation over convex hull points is used to compare the similarity between the scenes. It relies on the relative ordering of the feature strength, not directly on the feature vectors. We first deal with multiple convex hulls over the detected features and...
The developed mobile manipulator is primarily composed of a mobile base, a robot manipulator and an eye-in-hand vision system. The material handling of a mobile manipulator has two stages: guiding the mobile base between stations, and picking up a workpiece from a station. Fast landmark recognition and obstacle detection based on color segmentation are proposed for path following, obstacle avoidance...
This paper is concerned with the problem of keyframe detection in appearance-based visual SLAM. Appearance SLAM models a robot's environment topologically by a graph whose nodes represent strategically interesting places that have been visited by the robot and whose arcs represent spatial connectivity between these places. Specifically, we discuss and compare various methods for identifying the next...
Visually estimating a robot's own motion has been an active field of research within the last years. Though impressive results have been reported, some application areas still exhibit huge challenges. Especially for car-like robots in urban environments even the most robust estimation techniques fail due to a vast portion of independently moving objects. Hence, we move one step further and propose...
Scene recognition is a highly valuable perceptual ability for an indoor mobile robot, however, current approaches for scene recognition present a significant drop in performance for the case of indoor scenes. We believe that this can be explained by the high appearance variability of indoor environments. This stresses the need to include high-level semantic information in the recognition process....
The selectivity of visual attention mechanism is influenced by bottom-up competition and top-down biasing. This paper presents an object-based visual attention model which simulates top-down influences. Five components of top-down influences are modeled: learning of object representations stored in long-term memory (LTM), deduction of task-relevant feature(s), estimation of top-down biases, mediation...
In this paper an implementation of an algorithm for fast visual tracking and localization of mobile agents has been described. Based on an extremely rapid method for visual detection of an object, described localization strategy provides a real time solution suitable for the design of multi-agent control schemes. The agents tracking and localization is carried out through five differently trained...
We have been developing a humanoid robot that interacts with people through multimodal, long-term and continuous learning. Three approaches i) word acquisition, ii) self-modeling and iii) action-oriented perception will be introduced in this paper. In particular, we first describe the word acquisition from raw multimodal sensory stimulus by seeing given objects and listening to spoken utterance by...
For successful SLAM, landmarks for pose estimation should be continuously observed. This paper proposes autonomous detection of objects as visual landmarks for visual SLAM. Primitive features such as color and intensity, SIFT keypoints, and contour information are integrated to investigate environmental images and to distinguish objects from the background. Autonomous object detection can enable a...
The unsupervised selection and posterior recognition of visual landmarks is a highly valuable perceptual capability for a mobile robot. Recently, we proposed a system that aims to achieve this capability by combining a bottom-up data driven approach with top-down feedback provided by high level semantic representations. The bottom-up approach is based on three main mechanisms: visual attention, area...
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