This work introduces an architecture for a robotic active vision system equipped with a manipulator that is able to integrate visual and non-visual (tactile) sensorimotor experiences. Inspired by the human vision syst...
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ISBN:
(纸本)9781908187000
This work introduces an architecture for a robotic active vision system equipped with a manipulator that is able to integrate visual and non-visual (tactile) sensorimotor experiences. Inspired by the human vision system, we have implemented a strict separation of object location (where-data) and object features (what-data) in the visual data stream. This separation of what- and where-data has computational advantage but requires sequential fixation of visual cues in order to create and update a coherent view of the world. Hence, visual attention mechanisms must be put in place to decide which is the most task-relevant cue to fixate next. Regarding object manipulation many task relevant object properties (e.g. tactile feedback) are not necessarily related to visual features. Therefore, it is important that non-visual object features can influence visual *** present and demonstrate visual attention mechanisms for an active vision system that are modulated by visual and non-visual object features.
Recently, several image gradient and edge based features have been introduced. In unison, they all discovered that object shape is a strong cue for recognition and tracking. Generally their basic feature extraction re...
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Recently, several image gradient and edge based features have been introduced. In unison, they all discovered that object shape is a strong cue for recognition and tracking. Generally their basic feature extraction relies on pixel-wise gradient or edge computation using discrete filter masks, while scale invariance is later achieved by higher level operations like accumulating histograms or abstracting edgels to line segments. In this paper we show a novel and fast way to compute region based gradient features which are scale invariant themselves. We developed specialized, quick learnable weak classifiers that are integrated into our adaptively boosted observation model for particle filter based tracking. With an ensemble of region based gradient features this observation model is able to reliably capture the shape of the tracked object. The observation model is adapted to new object and background appearances while tracking. Thus we developed advanced methods to decide when to update the model, or in other words, if the filter is on target or not. We evaluated our approach using the BoBoT1 as well as the PROST2 datasets.
A typical video surveillance system consists of at least one camera, controlled by an operator. To decrease the human error rate and to generally lessen the burden of operators, many object tracking systems have been ...
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A typical video surveillance system consists of at least one camera, controlled by an operator. To decrease the human error rate and to generally lessen the burden of operators, many object tracking systems have been implemented, most of which work in 2D image space. If used centralized, this is a very expensive task. Furthermore, if several views are to be fused, large inaccuracies arise due to ground plane assumptions, for instance. Lastly, in outdoor setups, quite often there is a need for slower channels like Wireless LAN which cannot cope with the full resolution data stream. We provide a smart camera system which performs the intensive tasks like background estimation or feature extraction. A central unit only has to process the received data in feature space, increasing scalability. Additionally, the object tracking problem is converted to an accurate 3D feature tracking, avoiding difficulties such as proper object segmentation and adding increased trajectory accuracy. The feature regions are computed within the smart camera. A wide-baseline feature matching approach has been employed to allow more freedom in the placement of the single smart cameras.
Biologically inspired robotics is a well known approach for the design of autonomous intelligent robot systems. Very often it is assumed that biologically inspired models successfully implemented on robots offer new s...
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ISBN:
(纸本)1902956923
Biologically inspired robotics is a well known approach for the design of autonomous intelligent robot systems. Very often it is assumed that biologically inspired models successfully implemented on robots offer new scientific knowledge for biology too. In other words, robots experiments serving as a replacement for the biological system under investigation are assumed to provide new scientific knowledge for biology. This article is a critical investigation of this assumption. We begin by clarifying what we mean by "new scientific knowledge." Following Karl Popper's work the The Logic of Scientific Discovery we conclude that in general robotic experiments serving as replacement for biological systems can never directly deliver any new scientific knowledge for biology. We further argue that there is no formal guideline which defines the level of "biological plausibility" for biologically inspired robot implementations. Therefore, there is no reason to prefer some kind of robotic setup before others. Any claimed relevance for biology, however, is only justified if results from robotic experiments are translated back into new models and hypotheses amenable to experimental tests within the domain of biology. This translation "back" into biology is very often missing and we will discuss popular robotics frameworks in the context of Brain Research, Cognitive science and Developmental robotics in order to highlight this issue. Nonetheless, such frameworks are valuable and important, like pure mathematics, because they might lead to new formalisms and methods which in future might be essential for gaining new scientific knowledge if applied in biology. No one can tell, if and which of the current robotics frameworks will provide these new scientific tools. What we can already say-the main message of this article-is that robot systems serving as a replacement for biological systems won't be sufficient for the test of biological models, i.e. gaining new scientific knowledge i
An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image se...
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This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor v...
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An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image se...
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ISBN:
(纸本)9781424475421
An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially extracted through a clustering algorithm. Therefore, the final segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well as on complex ground and aerial outdoor images. Comparisons with state-of the-art unsupervised texture segmenters are also provided.
This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor v...
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ISBN:
(纸本)9781424475421
This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likely-inhomogeneous by means of the edginess maps generated in the first step. Third, the likely-homosgeneous pixels are segmented through an efficient graph-based segmenter. Finally, a modified version of the same graph-based segmenter is applied to the likely-inhomogeneous pixels in order to obtain the final segmentation. Experiments show that the proposed algorithm has a better performance than the state-of-the-art.
This paper addresses the problem of moving obstacle detection for autonomous mobile robots in unknown urban environments through the fusion of (vehicle-mounted) forward looking laser and vision sensors. In this approa...
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Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). According to recent literature, han...
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Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). According to recent literature, handoff safety margin is introduced to sensor planning so that sufficient overlapped FOVs among adjacent cameras are reserved for successful and smooth target transition. In this paper, we investigate the sensor planning problem when considering the dynamic interactions between moving targets and observing cameras. The probability of camera overload is explored to model the aforementioned interactions. The introduction of the probability of camera overload also considers the limitation that a given camera can simultaneously monitor or track a fixed number of targets and incorporates the target's dynamics into sensor planning. The resulting camera placement not only achieves the optimal balance between coverage and handoff success rate but also maintains the optimal balance in environments with various target densities. The proposed camera placement method is compared with a reference algorithm by Erdem and Sclaroff. Consistently improved handoff success rate is illustrated via experiments using typical office floor plans with various target densities.
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