New Silhouette Detection Algorithm to Create Real-Time Volume Shadow

2011 ◽  
Author(s):  
Hoshang Kolivand ◽  
Mohd Shahrizal bin Sunar
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Silhouette Detection

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

Real-Time Fire Detection Algorithm Based on Support Vector Machine with Dynamic Time Warping Kernel Function

2021 ◽  
Author(s):  
Jaeseung Baek ◽  
Taha J. Alhindi ◽  
Young-Seon Jeong ◽  
Myong K. Jeong ◽  
Seongho Seo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Kernel Function ◽  
Dynamic Time Warping ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Time Warping ◽  
Dynamic Time

An Octree Method for Interference Detection in Computer Aided 3-D Packing

1994 ◽  
Author(s):  
Zuo Dai ◽  
Jianzhong Cha
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Time Response ◽  
Interference Detection ◽  
Computer Memory ◽  
Computational Accuracy ◽  
Data Space ◽  
Computer Aided ◽  
Packing Process

Abstract In simulating the three dimensional packing process with arbitrary shaped objects, the task of detecting interference between objects is important and very difficult. This paper, representing the three dimensional packing space and objects with an octree, presents an effective interference detection algorithm, which can overcome the performance shortcomings that the conventional methods have in terms of real-time response, computer memory and computational accuracy. By recording the distribution status of packing space in the “bits” of short integers, the data space can be compressed to 1/16 of that used by conventional algorithms.

An automatic seismic signal detection algorithm based on the Walsh transform

1981 ◽  
Vol 71 (4) ◽  
pp. 1351-1360
Author(s):  
Tom Goforth ◽  
Eugene Herrin
Keyword(s):  
Signal Detection ◽  
Real Time ◽  
Seismic Signal ◽  
Detection Algorithm ◽  
Machine Language ◽  
Walsh Transform ◽  
Time Signal ◽  
Data Systems ◽  
Detection Capability ◽  
Short Period

abstract An automatic seismic signal detection algorithm based on the Walsh transform has been developed for short-period data sampled at 20 samples/sec. Since the amplitude of Walsh function is either +1 or −1, the Walsh transform can be accomplished in a computer with a series of shifts and fixed-point additions. The savings in computation time makes it possible to compute the Walsh transform and to perform prewhitening and band-pass filtering in the Walsh domain with a microcomputer for use in real-time signal detection. The algorithm was initially programmed in FORTRAN on a Raytheon Data Systems 500 minicomputer. Tests utilizing seismic data recorded in Dallas, Albuquerque, and Norway indicate that the algorithm has a detection capability comparable to a human analyst. Programming of the detection algorithm in machine language on a Z80 microprocessor-based computer has been accomplished; run time on the microcomputer is approximately 110 real time. The detection capability of the Z80 version of the algorithm is not degraded relative to the FORTRAN version.

Research of Magnetic Grid Rail Splicing Technology Based on Phase Difference Detection Method

2013 ◽  
Vol 278-280 ◽  
pp. 905-914
Author(s):  
Wen Tao Gu ◽  
Shao Kun Lei ◽  
Fang Li ◽  
Shao Wei Zhou
Keyword(s):  
Real Time ◽  
Phase Difference ◽  
Detection Algorithm ◽  
Initial Position ◽  
Signal Frequency ◽  
Simulation Test ◽  
Signal Phase ◽  
Difference Detection ◽  
Phase Errors ◽  
New Type

To meet the high real-time performance and high accuracy requirements of signal detection in the rail splicing process, this paper proposes a new type of magnetic grid rail splicing method based on accurately zeroing output signal phase difference of two magnetic grid reading heads, thus establishing two reading heads “shift” rules. The accurate zero setting technology of phase difference is based on Nuttall window algorithm, which doesn’t need to give the exact signal frequency beforehand and sample periodically, and can effectively eliminate phase errors. So this algorithm is suitable for detecting signal phase difference when reading heads go over buff joints with any speed at any initial position. Additionally, simulation test and experimental verification were performed on this detection algorithm and “shift” rules. The results show that, the method mentioned in this paper can real-time detect the phase difference by “shift” rules, when reading heads go over buff joints with any speed or any acceleration.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

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