Validating Vision and Robotic Algorithms for Dynamic Real World Environments

Proceedings of the International Conference on Computer Vision Theory and Applications

2013

Visual attention can support autonomous robots in visual tasks by assigning resources to relevant portions of an image. In this biologically inspired concept, conspicuous elements of the image are typically determined with regard to different features such as color, intensity or orientation. The assessment of human visual attention suggests that these bottom-up processes are complemented -and in many cases overruled -by top-down influences that modulate the attentional focus with respect to the current task or a priori knowledge. In artificial attention, one branch of research investigates visual search for a given object within a scene by the use of top-down attention. Current models require extensive training for a specific target or are limited to very simple templates. Here we propose a multi-region template model that can direct the attentional focus with respect to complex target appearances without any training. The template can be adaptively adjusted to compensate gradual changes of the object's appearance. Furthermore, the model is integrated with the framework of region-based attention and can be combined with bottom-up saliency mechanisms. Our experimental results show that the proposed method outperforms an approach that uses single-region templates and performs equally well as state-of-the-art feature fusion approaches that require extensive training.

Section: Visapp2013-internationalconferenceoncomputervisiontheoryandamentioning

confidence: 99%

Top-Down Visual Attention with Complex Templates

Tünnermann

Born

Proceedings of the International Conference on Computer Vision Theory and Applications

2013

“…The research presented in this paper is intended to be included in active vision applications [12], [13]. In order to analyze those applications in a scalable complex scene, a virtual environment for simulating a mobile robot platform (SIMORE) is used [14].…”

Section: Introductionmentioning

confidence: 99%

Real time stereo-based biologically inspired 3D motion classifier cells

Shafik

2011 6th IEEE Conference on Industrial Electronics and Applications

2011

In this paper, we present a real time biologically motivated 3D motion classifier cells integrating the depth information generated from a stereo input implemented in an active vision system. The proposed approach is accurately able to detect and estimate multiple interfered 3D complex motions under the absence of predefined spatial coherence. Moreover, the system has ability to examine the response of input 3D motion vector fields to a certain 3D motion patterns (3D motion classifier cells) such as motion in the Z direction representing movements towards the system, which is very important to overcome typical problem in autonomous mobile robotic vision such as collision detection and inhibition of the ego-motion defects of a moving camera head. The output of the algorithm is part in a multi-object segmentation approach implemented in an active vision system.

“…The research presented in this paper is intended to be included in active vision applications (Ali and Mertsching, 2009;Aziz and Mertsching, 2009). In order to analyze those applications in a scalable complex scene, a virtual environment for simulating a mobile robot platform (SIMORE) is used (Kotthäuser and Mertsching, 2010).…”

Section: Introductionmentioning

confidence: 99%

Fast Depth-Integrated 3d Motion Estimation and Visualization for an Active Vision System

Shafik

Proceedings of the International Conference on Computer Vision Theory and Applications

2011

In this paper, we present a fast 3D motion parameter estimation approach integrating the depth information acquired by a stereo camera head mounted on a mobile robot. Afterwards, the resulting 3D motion parameters are used to generate and accurately position motion vectors of the generated depth sequence in the 3D space using the geometrical information of the stereo camera head. The proposed approach has successfully detected and estimated predefined motion patterns such as motion in the Z direction and motion vectors pointing to the robot which is very important to overcome typical problems in autonomous mobile robotic vision such as collision detection and inhibition of the ego-motion defects of a moving camera head. The output of the algorithm is part of a multi-object segmentation approach implemented in an active vision system.