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Localization is one of the key research topics in robotics. Although computer vision based methodologies such as Augmented Markers provides a realtime localization approach, the sensing range and localization accuracy are the main limitations for applications. In this paper, we proposed the use of a problem solving tool, TRIZ to overcome these limitations. Among the tools provided by TRIZ, we illustrated...
In this paper ceiling affixed ARToolKitPlus 2D code artificial landmarks are evaluated for purposes of robot localization and navigation. Ceiling affixed codes rarely come in contact with people, equipment or robots, and for this reason they are more likely to stay detectable over a longer period of time. Multi threshold averaging, light gradient compensation and neighbourhood search techniques further...
In this paper, we present a novel bilinear invariant representation for video classification and retrieval. We rely on the kernel space in functional analysis to formulate a general invariants theory. We show that null-space invariants is a special case of the general theory when the transformation is linear. Subsequently, we derive an invariant basis representation for bilinear transformations. We...
View-invariant representation has been shown to be a powerful tool in classification and retrieval of motion events due to camera motions. Traditional null space representation is invariant only for linear transformations and does not yield high accuracy for camera with non-linear motions. In this paper, we propose a novel general framework for non-linear kernel space invariant representation (NKSI),...
Event/object classification and recognition is an extremely challenging problem, particularly when the query or stored data undergo an affine transformation due to camera motion. The complexity of the problem is further compounded when the input or stored data contain only partial information (e.g. due to object occlusion). Most of the existing representation do not allow view invariant representation...
In this paper, we propose a novel robust retrieval and classification system for video and motion events based on null space representation. In order to analyze the robustness of the system, the perturbed null operators have been derived with the first order perturbation theory. Subsequently, the sensitivity of the null operators is discussed in terms of the error ratio and the SNR respectively. Meanwhile,...
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