CVPR2018: Difference between revisions
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== | This page contains links relevant to Elphel presentation at CVPR 2018 Expo. | ||
==Presentation== | |||
* [https://community.elphel.com/files/presentations/Elphel_TP-CNN_slides.pdf High Resolution Wide FoV CNN System for Target Classification, Ranging and Tracking (pdf)] | |||
==3D models demo and image sets== | |||
* [https://community.elphel.com/3d+biquad/?lat=40.74257500&lng=-112.06741333&zoom=10&rating=5 3D BiQuad] | |||
==Research and Development Focus== | |||
=== Passive 3D Reconstruction and Long Ranging=== | |||
* [https://blog.elphel.com/category/3d/ 3D Reconstruction and Ranging] | |||
=== FPGA-RTL-ASIC: Efficient Implementation of Frequency Domain Processing === | |||
* [https://blog.elphel.com/category/rtl/ RTL] | |||
=== Calibration for Aberration Correction === | |||
* [https://blog.elphel.com/category/calibration/ Calibration] | |||
==Development blog== | |||
* [https://blog.elphel.com/2018/07/cvpr-2018-from-elphels-perspective/ <font size='2'>'''2018/07/21'''</font> CVPR 2018 – from Elphel’s perspective] | |||
* [https://blog.elphel.com/2018/07/two-dimensional-phase-correlation-as-neural-network-input-for-3d-imaging/ <font size='2'>'''2018/07/20'''</font> Two Dimensional Phase Correlation as Neural Network Input for 3D Imaging] | |||
* [https://blog.elphel.com/2018/07/reading-quad-stereo-tiff-image-stacks-in-python-and-formatting-data-for-tensorflow/ <font size='2'>'''2018/07/20'''</font> Reading quad stereo TIFF image stacks in Python and formatting data for TensorFlow] | |||
* [https://blog.elphel.com/2018/05/capturing-aircraft-position-with-the-long-range-quad-stereo-camera/ <font size='2'>'''2018/05/06'''</font> Capturing Aircraft Position with the Long Range Quad Stereo Camera] | |||
* [https://blog.elphel.com/2018/03/dual-quad-camera-rig-for-capturing-image-sets/ <font size='2'>'''2018/03/20'''</font> Dual Quad-Camera Rig for Capturing Image Sets] | |||
* [https://blog.elphel.com/2018/02/high-resolution-multi-vew-stereo-tile-processor-and-convolutional-neural-network/ <font size='2'>'''2018/02/05'''</font> High Resolution Multi-View Stereo: Tile Processor and Convolutional Neural Network] | |||
* [https://blog.elphel.com/2018/01/complex-lapped-transform-bayer/ <font size='2'>'''2018/01/08'''</font> Efficient Complex Lapped Transform Implementation for the Space-Variant Frequency Domain Calculations of the Bayer Mosaic Color Images] | |||
==Products== | |||
* [https://www3.elphel.com/products Our multiple and single camera systems products] | |||
Latest revision as of 16:49, 31 July 2018
This page contains links relevant to Elphel presentation at CVPR 2018 Expo.
Presentation
3D models demo and image sets
Research and Development Focus
Passive 3D Reconstruction and Long Ranging
FPGA-RTL-ASIC: Efficient Implementation of Frequency Domain Processing
Calibration for Aberration Correction
Development blog
- 2018/07/21 CVPR 2018 – from Elphel’s perspective
- 2018/07/20 Two Dimensional Phase Correlation as Neural Network Input for 3D Imaging
- 2018/07/20 Reading quad stereo TIFF image stacks in Python and formatting data for TensorFlow
- 2018/05/06 Capturing Aircraft Position with the Long Range Quad Stereo Camera
- 2018/03/20 Dual Quad-Camera Rig for Capturing Image Sets
- 2018/02/05 High Resolution Multi-View Stereo: Tile Processor and Convolutional Neural Network
- 2018/01/08 Efficient Complex Lapped Transform Implementation for the Space-Variant Frequency Domain Calculations of the Bayer Mosaic Color Images