Automatic inspection of analog and digital meters in a robot vision system

A critical limitation of most of the robots utilized in industrial environments arises due to their inability to utilize sensory feedback. This forces robot operation into totally preprogrammed or teleoperation modes. In order to endow the new generation of robots with higher levels of autonomy techniques for sensing of their work environments and for accurate and efficient analysis of the sensory data must be developed. In this paper detailed development of vision system modules for inspecting various types of meters, both analog and digital, encountered in a robotic inspection and manipulation tasks are described. These modules are tested using industrial robots having multisensory input capability

A Novel Optical/digital Processing System for Pattern Recognition

This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network

The ArgoNeuT Detector in the NuMI Low-Energy beam line at Fermilab

The ArgoNeuT liquid argon time projection chamber has collected thousands of neutrino and antineutrino events during an extended run period in the NuMI beam-line at Fermilab. This paper focuses on the main aspects of the detector layout and related technical features, including the cryogenic equipment, time projection chamber, read-out electronics, and off-line data treatment. The detector commissioning phase, physics run, and first neutrino event displays are also reported. The characterization of the main working parameters of the detector during data-taking, the ionization electron drift velocity and lifetime in liquid argon, as obtained from through-going muon data complete the present report.Comment: 43 pages, 27 figures, 5 tables - update referenc

Online Pattern Recognition for the ALICE High Level Trigger

The ALICE High Level Trigger has to process data online, in order to select interesting (sub)events, or to compress data efficiently by modeling techniques.Focusing on the main data source, the Time Projection Chamber (TPC), we present two pattern recognition methods under investigation: a sequential approach "cluster finder" and "track follower") and an iterative approach ("track candidate finder" and "cluster deconvoluter"). We show, that the former is suited for pp and low multiplicity PbPb collisions, whereas the latter might be applicable for high multiplicity PbPb collisions, if it turns out, that more than 8000 charged particles would have to be reconstructed inside the TPC. Based on the developed tracking schemes we show, that using modeling techniques a compression factor of around 10 might be achievableComment: Realtime Conference 2003, Montreal, Canada to be published in IEEE Transactions on Nuclear Science (TNS), 6 pages, 8 figure

Experimental test of an alignment-sensing scheme for a gravitational-wave interferometer

An alignment-sensing scheme for all significant angular degrees of freedom of a power-recycled Michelson interferometer with Fabry Perot cavities in the arms was tested on a tabletop interferometer. The response to misalignment of all degrees of freedom was measured at each sensor, and good agreement was found between measured and theoretical values

Event Reconstruction in the PHENIX Central Arm Spectrometers

The central arm spectrometers for the PHENIX experiment at the Relativistic Heavy Ion Collider have been designed for the optimization of particle identification in relativistic heavy ion collisions. The spectrometers present a challenging environment for event reconstruction due to a very high track multiplicity in a complicated, focusing, magnetic field. In order to meet this challenge, nine distinct detector types are integrated for charged particle tracking, momentum reconstruction, and particle identification. The techniques which have been developed for the task of event reconstruction are described.Comment: Accepted for publication in Nucl. Instrum. A. 34 pages, 23 figure

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)