Object detection

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Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. (Wikipedia.org)






Conferences related to Object detection

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2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.


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Periodicals related to Object detection

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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Most published Xplore authors for Object detection

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Xplore Articles related to Object detection

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Frequency band selection of radars for buried object detection

IEEE Transactions on Geoscience and Remote Sensing, 1999

Choice of the operational frequency is one of the most responsible parts of any radar design process. Parameters of radars for buried object detection (BOD) are very sensitive to both carrier frequency and ranging signal bandwidth. Such radars have a specific propagation environment with a strong frequency-dependent attenuation and, as a result, short operational range. This fact dictates some features ...


Foliage penetration experiment

IEEE Transactions on Aerospace and Electronic Systems, 1996

This series papers describes analyses of a foliage penetration experiment undertaken by MIT Lincoln Laboratory to assess the ability of synthetic aperture radar (SAR) to detect targets under trees. Data were taken using the NASA/JPL UHF, L-, C-band fully polarimetric SAR over a forested area in Maine in July 1990. Future experiments are planned to measure the polarimetric properties of ...


Cross-borehole sensing: identification and localization of underground tunnels in the presence of a horizontal stratification

IEEE Transactions on Geoscience and Remote Sensing, 1997

This paper addresses the detection and localization of a buried two- dimensional (2D) dielectric object in the presence of an air-Earth interface. The techniques used are modifications of the well-known backpropagation operator, including plane-wave angular spectral filtering and detection of the cross-polarized scattered field. Cross-correlation of the received signal with a known target signature is included for comparison and found ...


Buried object detection and location estimation from electromagnetic field measurements

IEEE Transactions on Antennas and Propagation, 1999

A translation property is derived describing the field scattered from a known buried object placed at distinct locations. The result is used to derive the optimum algorithm for detecting the known buried object and estimating its location from noisy scattered electromagnetic field measurements.


Electromagnetic detection of dielectric cylinders by a neural network approach

IEEE Transactions on Geoscience and Remote Sensing, 1999

The neural network approach is applied to the detection of cylindric objects as well as their geometric and electrical characteristics inside a given investigation domain. The electric field values scattered by the object and available at a small number of locations are fed into the network, whose output is the dielectric permittivity, and the location and radius of the cylinder. ...


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Educational Resources on Object detection

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IEEE.tv Videos

Critical use cases for video capturing systems in autonomous driving applications
Low Power Image Recognition: The Challenge Continues
Anticipating Human Activities for Reactive Robotic Response
Geoffrey Hinton receives the IEEE/RSE James Clerk Maxwell Medal - Honors Ceremony 2016
Welcome to ICRA 2015: Robot Challenges
Experience ICRA 2015: Robot Challenges
Handling of a Single Object by Multiple Mobile Robots based on Caster-Like Dynamics
Recording and Using 3D Object Models with RoboEarth
An FPGA-Quantum Annealer Hybrid System for Wide-Band RF Detection - IEEE Rebooting Computing 2017
Multi-Function VCO Chip for Materials Sensing and More - Jens Reinstaedt - RFIC Showcase 2018
Virtual Reality Support for Teleoperation Using Online Grasp Planning
Computing Conversations: Bertrand Meyer: Eiffel Programming Language
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 5 of 7 - SQUID Instrumentation for Early Cancer Diagnostics
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems
Implantable, Insertable and Wearable Micro-optical Devices for Early Detection of Cancer - Plenary Speaker, Christopher Contag - IPC 2018
CB: Exploring Neuroscience with a Humanoid Research Platform
Michele Nitti: Searching the Social Internet of Things by Exploiting Object Similarity - Special Session on SIoT: WF-IoT 2016
Developing Automated Analysis Tools for Space/Time Sidechannel Detection - IEEE SecDev 2016
IEEE Medal for Environmental and Safety Technologies - Jerome Faist and Frank K. Tittell - 2018 IEEE Honors Ceremony
Control of a Fully-Actuated Airship for Satellite Emulation

IEEE-USA E-Books

  • Frequency band selection of radars for buried object detection

    Choice of the operational frequency is one of the most responsible parts of any radar design process. Parameters of radars for buried object detection (BOD) are very sensitive to both carrier frequency and ranging signal bandwidth. Such radars have a specific propagation environment with a strong frequency-dependent attenuation and, as a result, short operational range. This fact dictates some features of the radar's parameters: wideband signal-to provide a high range resolution (fractions of a meter) and a low carrier frequency (tens or hundreds megahertz) for deeper penetration. The requirement to have a wideband ranging signal and low carrier frequency are partly in contradiction. As a result, low-frequency (LF) ultrawide-band (UWB) signals are used. The major goal of this paper is to examine the influence of the frequency band choice on the radar performance and develop relevant methodologies for BOD radar design and optimization. In this article, high- efficient continuous wave (CW) signals with most advanced stepped frequency (SF) modulation are considered; however, the main conclusions can be applied to any kind of ranging signals.

  • Foliage penetration experiment

    This series papers describes analyses of a foliage penetration experiment undertaken by MIT Lincoln Laboratory to assess the ability of synthetic aperture radar (SAR) to detect targets under trees. Data were taken using the NASA/JPL UHF, L-, C-band fully polarimetric SAR over a forested area in Maine in July 1990. Future experiments are planned to measure the polarimetric properties of clutter and targets using the latest ultrawideband sensors with submeter resolutions and fully polarimetric data collection capabilities.

  • Cross-borehole sensing: identification and localization of underground tunnels in the presence of a horizontal stratification

    This paper addresses the detection and localization of a buried two- dimensional (2D) dielectric object in the presence of an air-Earth interface. The techniques used are modifications of the well-known backpropagation operator, including plane-wave angular spectral filtering and detection of the cross-polarized scattered field. Cross-correlation of the received signal with a known target signature is included for comparison and found to be useful for detection. It is shown that use of the positive spatial frequency components of the fields scattered by the buried target, together with the backpropagation of vertical cross-polarized fields, yield results that are relatively insensitive to the presence of the horizontal stratification. Examples illustrate that backpropagation can be used for detection and localization purposes in a cross-borehole configuration. Some experimental results utilizing a 2.5 GHz laboratory scale model and a tunnel-like target are also included for comparison.

  • Buried object detection and location estimation from electromagnetic field measurements

    A translation property is derived describing the field scattered from a known buried object placed at distinct locations. The result is used to derive the optimum algorithm for detecting the known buried object and estimating its location from noisy scattered electromagnetic field measurements.

  • Electromagnetic detection of dielectric cylinders by a neural network approach

    The neural network approach is applied to the detection of cylindric objects as well as their geometric and electrical characteristics inside a given investigation domain. The electric field values scattered by the object and available at a small number of locations are fed into the network, whose output is the dielectric permittivity, and the location and radius of the cylinder. The results are evaluated using different sets of testing data, and the dependence of the various output parameters to the input are considered. The algorithm performance shows that the approach is able to solve the inverse scattering problem quickly. This may be useful for real-time remote-sensing applications.

  • Underwater buried object recognition using wavelet packets and Fourier descriptors

    Underwater object identification has been of great interest for a few years to acousticians (detection of boulders), marines (detection of buried mines), or archaeologists (detection of wreckage). Image and signal processing succeed in identifying objects lying on the sea bottom, however identification of an object buried in sediment remains complex. The purpose of this work is to develop a complete identification of objects embedded in the sediment using an adapted technology. We use a parametric source, whose properties are based on the nonlinear propagation characteristics of the water; it has many advantages as an acoustic source (high relative bandwidth, narrow beam) which are useful for object detection and classification. This paper presents two algorithms: the first one improves the object detection and the second procedure computes discriminant parameters from images to classify these objects.

  • A wavelet approach for the discrimination of buried objects

    The detection of buried objects generally requires the discrimination between targets and clutters through the EM signature of the object. However the signature is generally limited to the magnitude or just to a single component of the field, making the detection problem tremendously difficult, especially when dealing with plastic target. The purpose of the present work is to numerically investigate a new detection system, based on both the magnitude and phase of all the components of the E-field at high frequencies, together with a new wavelet post-processing

  • Recognition of Buried Objects by Their EM Scattering

    Electromagnetic (EM) scattering is effectively used in the detection of buried objects. However, the most challenging problem is represented by the discrimination of targets and clutters. The electromagnetic signature of known objects is often used to this purpose, but generally it is limited either to the r.m.s. value of the scattered field or to one component. In this work a technique based on the analysis of all the components of the EM field is presented and applied to the detection of landmines

  • Corrections to "asymptotic solutions for the scattered fields of plane wave by a cylindrical obstacle buried in a dielectric half-space"

    None

  • Microwave Subsurface Crosswell Imaging using Finite Difference Frequency Domain Modeling

    We have developed a new algorithm for electromagnetic inverse scattering problem in inhomogeneous media using finite difference frequency domain (FDFD) forward modeling, referred as FDFD-based inversion method. The key issue of this method is to build a linear expression for the inverse problem from the FDFD forward modeling by using the Born approximation. An important advantage of this matrix-based method is that there is no need to specify a Green's function. This inversion algorithm is applied to microwave subsurface object detection using cross well radar. The new method is compared with the conventional inversion using Green function-based Born Approximation (GFBA). This EM scattering inverse algorithm is easily implemented and is robust to the heterogeneity of background. Numerical experiments are presented for a two dimensional borehole geometry for buried object detection in soil.



Standards related to Object detection

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No standards are currently tagged "Object detection"