Global feature space neural network for active object recognition

Sipe, Michael A.; Casasent, David P.

doi:10.1109/ijcnn.1999.836151

Cited by 3 publications

(2 citation statements)

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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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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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“…The 3D-Base Project [14] converted CAD models into a voxel representation, which was then used to perform comparisons using geometric moments and other features. Sipe, Casasent and Talukder [15][16][17] used acquired 2D image data to correlated real machined parts to CAD models and perform classification and pose estimation. Osada et al [18] presented a method for matching 3D topological models using probability distributions of samples from a shape function acting on the models.…”

Section: Comparing Shape Modelsmentioning

confidence: 99%

Reeb Graph Based Shape Retrieval for CAD

Bespalov

Regli

Shokoufandeh

2003

Volume 1: 23rd Computers and Information in Engineering Conference, Parts a and B

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Our recent work has described a framework for matching solid of mechanical artifacts models based on scale-space feature decomposition. In this work we adopt a method of comparing solid models based on Multiresolutional Reeb Graphs (MRG) similarity computations. This method was originally proposed by Hilaga et al. in [1]. Reeb Graph technique applies MRG structure to comparisons of approximate models found in the graphics community, such as polygonal meshes, faceted representation and Virtual Reality Modeling Language (VRML) models. First, we provide a brief review of shape matching using Multiresolutional Reeb Graphs and present an approach to matching solid models. Second, we show the performance of the Reeb Graph technique when handling primitive CAD models, such as cubes and spheres; then we perform experiments with more complex models, such as LEGO models and mechanical parts, and we discuss Reeb Graph technique’s performance on complex CAD models. Third, we emphasize several problems with the existing technique. Finally, we conclude with discussion of future work.

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