Feature space trajectory methods for active computer vision

Sipe, Michael A.; Casasent, David P.

doi:10.1109/tpami.2002.1114854

Cited by 23 publications

(18 citation statements)

References 27 publications

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“…While early work primarily optimized views using a geometric approach, later work incorporated probabilistic models into active perception systems (Arbel and Ferrie, 2001;Sipe and Casasent, 2002;Denzler and Brown, 2002). 1 Such approaches have also been applied to depth maps in the context of medical imagery (Zhou et al, 2003).…”

Section: Related Workmentioning

confidence: 99%

Active planning for underwater inspection and the benefit of adaptivity

Hollinger

Englot

Hover

et al. 2012

The International Journal of Robotics Research

133

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We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). Unlike a large body of prior work, we focus on planning the views of the AUV to improve the quality of the inspection, rather than maximizing the accuracy of a given data stream. We formulate the inspection planning problem as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We rigorously analyze the benefit of adaptive re-planning for such problems, and we prove that the potential benefit of adaptivity can be reduced from exponential to a constant factor by changing the problem from cost minimization with a constraint on information gain to variance reduction with a constraint on cost. Such analysis allows the use of robust, nonadaptive planning algorithms that perform competitively with adaptive algorithms. Based on our analysis, we propose a method for constructing 3D meshes from sonar-derived point clouds, and we introduce uncertainty modeling through non-parametric Bayesian regression. Finally, we demonstrate the benefit of active inspection planning using sonar data from ship hull inspections with the Bluefin-MIT Hovering AUV.

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Section: Related Workmentioning

confidence: 99%

Active planning for underwater inspection and the benefit of adaptivity

Hollinger

Englot

Hover

et al. 2012

The International Journal of Robotics Research

133

View full text Add to dashboard Cite

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“…Early techniques were often concerned with the "next-best" action to maximize information with relatively less focus on long-term planning methods. While this early work made limited use of information theoretic methods, later work in this area has increasingly focused on incorporating information theory and probabilistic techniques into active perception systems [25]. Two recent surveys describe the breadth of work in active perception and its development over the past two decades [7,22].…”

Section: Related Workmentioning

confidence: 99%

Active and Adaptive Dive Planning for Dense Bathymetric Mapping

Hollinger

Mitra

Sukhatme

2013

Springer Tracts in Advanced Robotics

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We examine the problem of planning dives for an Autonomous Underwater Vehicle (AUV) to generate a dense bathymetric map using sidescan sonar. The three key challenges in this scenario are (1) proper modeling of the local uncertainty of the 3D reconstruction, (2) efficient dive planning to reduce this uncertainty, and (3) determination of when to re-plan adaptively based on new information. To address these challenges, we propose using non-parametric Bayesian regression to model the expected accuracy of the map, which provides principled cost functions for planning subsequent dives. In addition, we propose an efficient greedy method to reduce this uncertainty, and we show that it achieves theoretically bounded performance given assumptions on the sensor model and the form of the uncertainty function. We present experiments on the propeller-driven YSI EcoMapper AUV equipped with a sidescan sonar in an inland lake. The experiments demonstrate the benefit of efficient dive planning, with our results providing performance gains of up to 83% versus standard lawnmower patterns.

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“…References [111,112] present an active recognition and pose estimation system based on a neural network scheme to evaluate distances in global feature space. A feature space trajectory (FST) in the eigenspace generated from intensity images is employed to represent 3D views of an object.…”

Section: Active Vision Schemesmentioning

confidence: 99%

Robust sequential view planning for object recognition using multiple cameras

Farshidi

Sirouspour

Kirubarajan

2009

Image and Vision Computing

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In this thesis the problem of object recognition/pose estimation using active sensing is investigated. It is assumed that multiple cameras acquire images from different view angles of an object belonging to a set of a priori known objects. The eigenspace method is used to process the sensory observations and produce an abstract measurement vector. This step is necessary to avoid the manipulation of the original sensor data, i.e. large images, that can render the sensor modelling and matching process practically infeasible.The eigenspace representation is known to have shortcomings in dealing with structured noise such as occlusion. To overcome this problem, models of occlusions and sensor noise have been incorporated into the probabilistic model of sensor/object to increase robustness with respect to such uncertainties. The active recognition algorithm has also been modified to consider the possibility of occlusion, as well as variation in the occlusion levels due to camera movements.A recursive Bayesian state estimation problem is formulated to model the observation uncertainties through a probabilistic scheme. This enables us to identify the object and estimate its pose by fusing the information obtained from individual cameras. To this end, an extensive training step is performed, providing the system with the sensor model required for the Bayesian estimation. In order to enhance the quality A multi-camera active vision system has been developed in order to implement the ideas proposed in this thesis. Comparative Monte Carlo experiments conducted with the two-camera system demonstrate the effectiveness of the proposed methods in object classification/pose estimation in the presence of structured noise. Different concepts introduced in this work, i.e., the multi-camera data fusion, the occlusion modelling, and the active camera movement, all improve the recognition process significantly. Specifically, these approaches all increase the recognition rate, decrease the number of steps taken before recognition is completed, and enhance robustness with respect to partial occlusion considerably.iv

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Feature space trajectory methods for active computer vision

Cited by 23 publications

References 27 publications

Active planning for underwater inspection and the benefit of adaptivity

Active planning for underwater inspection and the benefit of adaptivity

Active and Adaptive Dive Planning for Dense Bathymetric Mapping

Robust sequential view planning for object recognition using multiple cameras

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