Bärbel Mertsching

Bio: Bärbel Mertsching is an academic researcher from University of Paderborn. The author has contributed to research in topics: Mobile robot & Optical flow. The author has an hindex of 14, co-authored 114 publications receiving 994 citations. Previous affiliations of Bärbel Mertsching include University of Hamburg & Fraunhofer Society.

Fast and Robust Generation of Feature Maps for Region-Based Visual Attention

Abstract: Visual attention is one of the important phenomena in biological vision which can be followed to achieve more efficiency, intelligence, and robustness in artificial vision systems. This paper investigates a region-based approach that performs pixel clustering prior to the processes of attention in contrast to late clustering as done by contemporary methods. The foundation steps of feature map construction for the region-based attention model are proposed here. The color contrast map is generated based upon the extended findings from the color theory, the symmetry map is constructed using a novel scanning-based method, and a new algorithm is proposed to compute a size contrast map as a formal feature channel. Eccentricity and orientation are computed using the moments of obtained regions and then saliency is evaluated using the rarity criteria. The efficient design of the proposed algorithms allows incorporating five feature channels while maintaining a processing rate of multiple frames per second. Another salient advantage over the existing techniques is the reusability of the salient regions in the high-level machine vision procedures due to preservation of their shapes and precise locations. The results indicate that the proposed model has the potential to efficiently integrate the phenomenon of attention into the main stream of machine vision and systems with restricted computing resources such as mobile robots can benefit from its advantages.

Data- and model-driven gaze control for an active-vision system

Abstract: Models of visual attention provide a general approach to control the activities of active vision systems. We introduce a new model of attentional control that differs in important aspects from conventional ones. We divide the selection into two stages, which is more suitable for the system as well as explaining different phenomena found in natural visual attention, such as the dispute between early and late selection. The proposed model is especially designed for use in dynamic scenes. Our approach aims at modeling as much of a general active vision system as possible and designing clean interfaces for the integration of the remaining specific aspects needed in order to solve specific problems.

Illumination-Robust Optical Flow Using a Local Directional Pattern

Abstract: Most of the variational optical flow methods are based on the well-known brightness constancy assumption or high-order constancy assumptions to implement the data term in the optimization energy function. Unfortunately, any variation in the lighting within the scene violates the brightness constancy constraint; in turn, the gradient constancy assumption does not work properly with large illumination changes. This paper proposes an illumination-robust constancy based on a robust texture descriptor rather than the brightness constancy. Thus, the similarity function used as a data term was obtained from extracting texture features through the local directional pattern descriptor for two consecutive frames within the duality total variational optical flow algorithm. In addition, a weighted nonlocal term that depends on both the color similarity and the occlusion state of pixels is integrated during the optimization process to increase the accuracy of the resulting flow field. The experimental results show a qualitative comparison with the proposed approach and yield state-of-the-art results on the KITTI, Midleburry, and MPI-sintel data sets.

Closest Gap based (CG) reactive obstacle avoidance Navigation for highly cluttered environments

Abstract: A new reactive collision avoidance approach for mobile robots moving in cluttered and complex environments was developed and implemented. The novelty of this approach lies in the creation of a new method for analyzing openings in front of the robot that highly reduces their number when compared with the Nearness-Diagram Navigation (ND) technique, particularly in complex scenarios. Moreover, the angular width of the chosen (selected) gap with respect to the robot vision is taken into consideration. Consequently, oscillations are alleviated, the computational complexity is reduced and a smoother behavior will be achieved. Our technique adjusts the motion law proposed in the Smooth Nearness-Diagram Navigation (SND) method to generate safer paths for the robot by considering the ratio of threats on its sides and applying stricter deviation against an obstacle as it gets closer to the robot. Hence, the problem of deadlock occurring in narrow corridors, with high threats on one side and low threats on the other, is solved without affecting the smoothness behavior. Simulation and experimental results demonstrate the power of the proposed approach.

Illumination Robust Optical Flow Model Based on Histogram of Oriented Gradients

Abstract: The brightness constancy assumption has widely been used in variational optical flow approaches as their basic foundation. Unfortunately, this assumption does not hold when illumination changes or for objects that move into a part of the scene with different brightness conditions. This paper proposes a variation of the L1-norm dual total variational (TV-L1) optical flow model with a new illumination-robust data term defined from the histogram of oriented gradients computed from two consecutive frames. In addition, a weighted non-local term is utilized for denoising the resulting flow field. Experiments with complex textured images belonging to different scenarios show results comparable to state-of-the-art optical flow models, although being significantly more robust to illumination changes.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

Abstract: Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes