Three-dimensional passive sensing photon counting for object classification

2007 ◽  
Author(s):  
Seokwon Yeom ◽  
Bahram Javidi ◽  
Edward Watson
Keyword(s):  
Photon Counting ◽  
Three Dimensional ◽  
Object Classification ◽  
Passive Sensing

Three-dimensional Object Classification with Photon Counting Linear Discriminant Analysis

2007 ◽  
Author(s):  
Seokwon Yeom ◽  
Bahram Javidi ◽  
Edward Watson ◽  
Jón Atli Benediktsson ◽  
Bahram Javidi ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Photon Counting ◽  
Three Dimensional ◽  
Object Classification ◽  
Linear Discriminant ◽  
Dimensional Object

scholarly journals Three-Dimensional Imaging via Time-Correlated Single-Photon Counting

2020 ◽  
Vol 10 (6) ◽  
pp. 1930
Author(s):  
Chengkun Fu ◽  
Huaibin Zheng ◽  
Gao Wang ◽  
Yu Zhou ◽  
Hui Chen ◽  
...  
Keyword(s):  
3D Imaging ◽  
Single Photon ◽  
Signal To Noise Ratio ◽  
Photon Counting ◽  
Three Dimensional ◽  
Similarity Index ◽  
Signal To Noise ◽  
Single Photon Counting ◽  
Imaging Results ◽  
Noise Ratio

Three-dimensional (3D) imaging under the condition of weak light and low signal-to-noise ratio is a challenging task. In this paper, a 3D imaging scheme based on time-correlated single-photon counting technology is proposed and demonstrated. The 3D imaging scheme, which is composed of a pulsed laser, a scanning mirror, single-photon detectors, and a time-correlated single-photon counting module, employs time-correlated single-photon counting technology for 3D LiDAR (Light Detection and Ranging). Aided by the range-gated technology, experiments show that the proposed scheme can image the object when the signal-to-noise ratio is decreased to −13 dB and improve the structural similarity index of imaging results by 10 times. Then we prove the proposed scheme can image the object in three dimensions with a lateral imaging resolution of 512 × 512 and an axial resolution of 4.2 mm in 6.7 s. At last, a high-resolution 3D reconstruction of an object is also achieved by using the photometric stereo algorithm.

scholarly journals Underwater-Sonar-Image-Based 3D Point Cloud Reconstruction for High Data Utilization and Object Classification Using a Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1763
Author(s):  
Minsung Sung ◽  
Jason Kim ◽  
Hyeonwoo Cho ◽  
Meungsuk Lee ◽  
Son-Cheol Yu
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
Field Experiments ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Object Classification ◽  
High Data ◽  
3D Geometry ◽  
Underwater Object ◽  
3D Point Cloud Reconstruction

This paper proposes a sonar-based underwater object classification method for autonomous underwater vehicles (AUVs) by reconstructing an object’s three-dimensional (3D) geometry. The point cloud of underwater objects can be generated from sonar images captured while the AUV passes over the object. Then, a neural network can predict the class given the generated point cloud. By reconstructing the 3D shape of the object, the proposed method can classify the object accurately through a straightforward training process. We verified the proposed method by performing simulations and field experiments.

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