scholarly journals A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface

2021 ◽  
Vol 2083 (3) ◽  
pp. 032090
Author(s):  
Changli Mai ◽  
Bijian Jian ◽  
Yongfa Ling
Keyword(s):  
Water Surface ◽  
Center Of Mass ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Random Fluctuation ◽  
False Detection ◽  
Detection Algorithms ◽  
Regional Center ◽  
Underwater Environment ◽  
Active Imaging

Abstract Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.

scholarly journals An Improvement to a Class of Intensity based Spatial Domain Corner Detection Algorithms using Image Fission and Fusion

2019 ◽  
Vol 9 (2) ◽  
pp. 924-930
Keyword(s):  
Information Gain ◽  
Binary Classification ◽  
Real Life ◽  
Detection Algorithm ◽  
Spatial Domain ◽  
Corner Detection ◽  
Computational Time ◽  
Detection Algorithms ◽  
Bit Plane ◽  
Bit Planes

Corners of an object are important as features for the representation and analysis of its shape in computer vision. Corner detection, particularly in real scenes, is still a challenge. Most of the corner detectors found in the literature generate a number of false corners, which is not acceptable in real-life applications. In this paper, an improvement to a class of corner detection algorithms is presented using image fission/fusion. In this approach, a grayscale image is first divided into several bit-planes. A corner detector is applied on all the bit-planes simultaneously and a threshold (bitplane) is obtained using the concept of information gain. Finally, all the higher bit-plane corners are recombined (up to some thresholded bit-plane) to obtain the final set of corners. Here the corner detection algorithm is considered as a binary classification problem. Experimental results show that this improved approach reduces the number of erroneous corner detection relative to existing spatial domain corner detection algorithms. The improvements are established with the help of a number of performance measures proposed by various researchers. The proposed approach works better with respect to computational time also. This approach can easily be utilized in different low-level image processing applications.

Intelligent crack damage detection system in shield tunnel using combination of retinanet and optimal adaptive selection

2021 ◽  
pp. 1-17
Author(s):  
Xin Wen Gao ◽  
ShuaiQing Li ◽  
Bang Yang Jin ◽  
Min Hu ◽  
Wei Ding
Keyword(s):  
Large Scale ◽  
Crack Detection ◽  
Detection System ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Shield Tunnel ◽  
False Detection ◽  
Adaptive Selection ◽  
Detection Algorithms ◽  
Tunnel Crack

With the large-scale construction of urban subways, the detection of tunnel cracks becomes particularly important. Due to the complexity of the tunnel environment, it is difficult for traditional tunnel crack detection algorithms to detect and segment such cracks quickly and accurately. The article presents an optimal adaptive selection model (RetinaNet-AOS) based on deep learning RetinaNet for semantic segmentation on tunnel crack images quickly and accurately. The algorithm uses the ROI merge mask to obtain a minimum detection area of the crack in the field of view. A scorer is designed to measure the effect of ROI region segmentation to achieve optimal results, and further optimized with a multi-dimensional classifier. The algorithm is compared with the standard detection based on RetinaNet algorithm with an optimal adaptive selection based on RetinaNet algorithm for different crack types. The results show that our crack detection algorithm not only addresses interference due to mash cracks, slender cracks, and water stains but also the false detection rate decreases from 25.5–35.5% to about 3.6%. Meanwhile, the experimental results focus on the execution time to be calculated on the algorithm, FCN, PSPNet, UNet. The algorithm gives better performance in terms of time complexity.

scholarly journals 3D-modeling of Augmented Reality objects using Shi-Tomasi corner detection algorithms

2021 ◽  
Vol 2091 (1) ◽  
pp. 012058
Author(s):  
O M Demidenko ◽  
N A Aksionova ◽  
A V Varuyeu ◽  
A I Kucharav
Keyword(s):  
Augmented Reality ◽  
3D Modeling ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Accurate Data ◽  
Software Module ◽  
Detection Algorithms ◽  
Construction Documents

Abstract This article covers development of the Python-based software module for Blender 3D, as well as it covers research of Shi-Tomasi corner detection algorithm using the developer’s construction documents. The corners detected may be used for further three - dimensional modelling, replanting not requiring adjustment of the construction documents, or may be used for retrieval of the accurate data.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaheen Syed ◽  
Bente Morseth ◽  
Laila A. Hopstock ◽  
Alexander Horsch
Keyword(s):  
Threshold Value ◽  
Detection Algorithm ◽  
Classification Model ◽  
Type I ◽  
Accelerometer Data ◽  
Deep Convolutional Neural Networks ◽  
Major Drawback ◽  
Wear Time ◽  
Detection Algorithms ◽  
Type Ii Errors

AbstractTo date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.

scholarly journals An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4116
Author(s):  
Nighat Usman ◽  
Omar Alfandi ◽  
Saeeda Usman ◽  
Asad Masood Khattak ◽  
Muhammad Awais ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Water Surface ◽  
Power Transmission ◽  
Smart Cities ◽  
Cluster Head ◽  
Three Dimensional ◽  
Energy Efficient Routing ◽  
Underwater Environment ◽  
Ocean Environment ◽  
Smart Technologies

Nowadays, there is a growing trend in smart cities. Therefore, Terrestrial and Internet of Things (IoT) enabled Underwater Wireless Sensor Networks (TWSNs and IoT-UWSNs) are mostly used for observing and communicating via smart technologies. For the sake of collecting the desired information from the underwater environment, multiple acoustic sensors are deployed with limited resources, such as memory, battery, processing power, transmission range, etc. The replacement of resources for a particular node is not feasible due to the harsh underwater environment. Thus, the resources held by the node needs to be used efficiently to improve the lifetime of a network. In this paper, to support smart city vision, a terrestrial based “Away Cluster Head with Adaptive Clustering Habit” (ACH) 2 is examined in the specified three dimensional (3-D) region inside the water. Three different cases are considered, which are: single sink at the water surface, multiple sinks at water surface,, and sinks at both water surface and inside water. “Underwater (ACH) 2 ” (U-(ACH) 2 ) is evaluated in each case. We have used depth in our proposed U-(ACH) 2 to examine the performance of (ACH) 2 in the ocean environment. Moreover, a comparative analysis is performed with state of the art routing protocols, including: Depth-based Routing (DBR) and Energy Efficient Depth-based Routing (EEDBR) protocol. Among all of the scenarios followed by case 1 and case 3, the number of packets sent and received at sink node are maximum using DEEC-(ACH) 2 protocol. The packets drop ratio using TEEN-(ACH) 2 protocol is less when compared to other algorithms in all scenarios. Whereas, for dead nodes DEEC-(ACH) 2 , LEACH-(ACH) 2 , and SEP-(ACH) 2 protocols’ performance is different for every considered scenario. The simulation results shows that the proposed protocols outperform the existing ones.

An Improved Corner Detection Algorithm Based on Harris

2018 ◽  
Author(s):  
Songqi Han ◽  
Weibo Yu ◽  
Hongtao Yang ◽  
Shicheng Wan
Keyword(s):  
Detection Algorithm ◽  
Corner Detection

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

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