Diffractive network-based single-pixel machine vision

2021 ◽  
Author(s):  
Jingxi Li ◽  
Deniz Mengu ◽  
Nezih T. Yardimci ◽  
Yi Luo ◽  
Xurong Li ◽  
...  
Keyword(s):  
Machine Vision ◽  
Single Pixel

scholarly journals Spectrally encoded single-pixel machine vision using diffractive networks

2021 ◽  
Vol 7 (13) ◽  
pp. eabd7690
Author(s):  
Jingxi Li ◽  
Deniz Mengu ◽  
Nezih T. Yardimci ◽  
Yi Luo ◽  
Xurong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Optical Networks ◽  
3D Imaging ◽  
Spatial Information ◽  
Spectral Power ◽  
Measurement Systems ◽  
Terahertz Spectrum ◽  
Spectral Encoding ◽  
Single Pixel

We demonstrate optical networks composed of diffractive layers trained using deep learning to encode the spatial information of objects into the power spectrum of the diffracted light, which are used to classify objects with a single-pixel spectroscopic detector. Using a plasmonic nanoantenna-based detector, we experimentally validated this single-pixel machine vision framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also coupled this diffractive network-based spectral encoding with a shallow electronic neural network, which was trained to rapidly reconstruct the images of handwritten digits based on solely the spectral power detected at these ten distinct wavelengths, demonstrating task-specific image decompression. This single-pixel machine vision framework can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with diffractive network-based spectral encoding of information.

Subpixel Edge Detection of Autofocus for Micro-Machine Vision System

2011 ◽  
Vol 216 ◽  
pp. 228-232
Author(s):  
Ji Gang Wu ◽  
Kuan Fang He ◽  
Bin Qin
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Spline Interpolation ◽  
Vision System ◽  
Detection Algorithm ◽  
Noise Performance ◽  
Threshold Algorithm ◽  
Edge Location ◽  
Micro Machine ◽  
Single Pixel

Aiming at the subpixle edge detection of speckle in autofocus for micro-machine vision, a novel accurate subpixel edge detection algorithm was proposed. The image of the part to be inspected was binaried by simple threshold algorithm. The noise in image was eliminated by blob area threshold algorithm. The pixel level edge detection was done and the single-pixel width connected pixel level contour was acquired by binary mathematical morphological algorithm. The subpixel level edge detection was completed and the subpixel level contour was obtained by 9×9 pixel rectangular lens algorithm based on cubic spline interpolation. The example result indicate that calculation speed of the algorithm proposed herein is fast, anti-noise performance is high, inspection accuracy is high, and subpixel edge location accuracy can reach to μm level.

Single-Pixel Machine Vision Using Spectral Encoding Through Diffractive Optical Networks

2021 ◽  
Author(s):  
Jingxi Li ◽  
Deniz Mengu ◽  
Nezih T. Yardimci ◽  
Yi Luo ◽  
Xurong Li ◽  
...  
Keyword(s):  
Machine Vision ◽  
Optical Networks ◽  
Spectral Encoding ◽  
Single Pixel

Machine Vision

2009 ◽  
Author(s):  
Wesley E. Snyder ◽  
Hairong Qi
Keyword(s):  
Machine Vision

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

Machine Vision, Let's Go into the Street

1997 ◽  
Vol 117 (10) ◽  
pp. 1339-1344
Author(s):  
Katsuhiko Sakaue ◽  
Hiroyasu Koshimizu
Keyword(s):  
Machine Vision

Machine Vision Technologies to Connect with Humans

2005 ◽  
Vol 125 (11) ◽  
pp. 692-695
Author(s):  
Kazunori UMEDA ◽  
Yoshimitsu AOKI
Keyword(s):  
Machine Vision

Multifunctional stereoscopic machine vision system with multiple 3D outputs

2020 ◽  
Keyword(s):  
Machine Vision ◽  
International Symposium ◽  
Fast Track ◽  
Vision System ◽  
Machine Vision System ◽  
Stereoscopic Displays ◽  
Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.

A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

2020 ◽  
Vol 64 (5) ◽  
pp. 50411-1-50411-8
Author(s):  
Hoda Aghaei ◽  
Brian Funt
Keyword(s):  
Machine Vision ◽  
Spectral Reflectance ◽  
Color Constancy ◽  
Ground Truth ◽  
Practical Approach ◽  
Post Processing ◽  
Illumination Estimation ◽  
Processing Step ◽  
Image Dataset

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

Effect of Field Setup on Accuracy of Traffic Data Collection Using Machine Vision Technology

2002 ◽  
Vol 1804 (1) ◽  
pp. 62-66 ◽  
Author(s):  
Sunita Nadella ◽  
Lloyd A. Herman
Keyword(s):  
Machine Vision ◽  
Focal Length ◽  
Reference Points ◽  
Specific Reference ◽  
Traffic Data ◽  
Video Traffic ◽  
Vertical Angle ◽  
Traffic Data Collection ◽  
Camera Position ◽  
Parameter Ranges

Video traffic data were collected in 24 combinations of four different camera position parameters. A machine vision processor was used to detect vehicle speeds and volumes from the videotapes. The machine vision results were then compared with the actual vehicle volumes and speeds to give the percentage errors in each case. The results of the study provide a procedure with which to establish camera position parameters with specific reference points to help machine vision users select suitable camera positions and develop appropriate measurement error expectations. The camera position parameters that were most likely to produce the least overall volume and speed errors, for the specific site and field setup with the parameter ranges used in this study, were the low height of approximately 7.6 m (25 ft), with an upstream orientation (traffic moving toward the camera), a 50-mm (midangle) focal length, and a 15° vertical angle.

Sign in / Sign up

Export Citation Format

Share Document