scholarly journals A Vertex Concavity-Convexity Detection Method for Three-Dimensional Spatial Objects Based on Geometric Algebra

2020 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Pengcheng Yin ◽  
Jiyi Zhang ◽  
Xiying Sun ◽  
Di Hu ◽  
Zhifeng Shi ◽  
...  
Keyword(s):  
Geometric Algebra ◽  
Detection Method ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Basic Algorithm ◽  
Outer Product ◽  
Spatial Objects ◽  
Detection Algorithms ◽  
Product Operation ◽  
Object Based

Vertex concavity-convexity detection for spatial objects is a basic algorithm of computer graphics, as well as the foundation for the implementation of other graphics algorithms. In recent years, the importance of the vertex concavity-convexity detection algorithm for three-dimensional (3D) spatial objects has been increasingly highlighted, with the development of 3D modeling, artificial intelligence, and other graphics technologies. Nonetheless, the currently available vertex concavity-convexity detection algorithms mostly use two-dimensional (2D) polygons, with limited research on vertex concavity-convexity detection algorithms for 3D polyhedrons. This study investigates the correlation between the outer product and the topology of the spatial object based on the unique characteristic that the outer product operation in the geometric algebra has unified and definitive geometric implications in space, and with varied dimensionality. Moreover, a multi-dimensional unified vertex concavity-convexity detection algorithm framework for spatial objects is proposed, and this framework is capable of detecting vertex concavity-convexity for both 2D simple polygons and 3D simple polyhedrons.

scholarly journals A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Liying Wang ◽  
Lei Shi ◽  
Liancheng Xu ◽  
Peiyu Liu ◽  
Lindong Zhang ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Computational Cost ◽  
Optimization Method ◽  
Detection Algorithm ◽  
Threshold Function ◽  
Detection Algorithms ◽  
Original Dataset ◽  
Data Clusters ◽  
High Computational Cost

Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough room for further improvements. To solve the above problems, our paper proposes a parameter-free outlier detection algorithm based on dataset optimization method. Firstly, we propose a dataset optimization method (DOM), which initializes the original dataset in which density is greater than a specific threshold. In this method, we propose the concepts of partition function (P) and threshold function (T). Secondly, we establish a parameter-free outlier detection method. Similarly, we propose the concept of the number of residual neighbors, as the number of residual neighbors and the size of data clusters are used as the basis of outlier detection to obtain a more accurate outlier set. Finally, extensive experiments are carried out on a variety of datasets and experimental results show that our method performs well in terms of the efficiency of outlier detection and time complexity.

An Effective Method for Defects Detection in Radiographic Images of Welds Based on Edge Detection and Morphology

2013 ◽  
Vol 290 ◽  
pp. 71-77
Author(s):  
Wen Ming Guo ◽  
Yan Qin Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Radiographic Images ◽  
X Ray ◽  
Weld Defects ◽  
Detection Algorithms ◽  
Detection Quality

In the current industrial production, as steel weld X-ray images are low contrasted and noisy, the efficiency and precision can’t be both ensured. This paper has studied three different edge detection algorithms and found the most suitable one to detect weld defects. Combined with this edge detection algorithm, we proposed a new weld defects detection method. This method uses defect features to find the defects in edge images with morphological processing. Compared to the traditional methods, the method has ensured detection quality of weld defects detection.

scholarly journals A New Semantic-Based Tool Detection Method for Robots

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Wenbai Chen ◽  
Chao He ◽  
Chen W.Z. ◽  
Chen Q.L. ◽  
Wu P.L.
Keyword(s):  
Detection Method ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Recognition Ability ◽  
Home Helper ◽  
Point Detection ◽  
Functional Components ◽  
Instance Segmentation ◽  
Better Than

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms.

scholarly journals A Framework to Test Resistency of Detection Algorithms for Stepping-Stone Intrusion on Time-Jittering Manipulation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lixin Wang ◽  
Jianhua Yang ◽  
Michael Workman ◽  
Peng-Jun Wan
Keyword(s):  
Data Stream ◽  
Detection Method ◽  
Detection Algorithm ◽  
Efficient Algorithms ◽  
Detection Methods ◽  
The Internet ◽  
Stepping Stones ◽  
Stepping Stone ◽  
Detection Algorithms ◽  
A Chain

Hackers on the Internet usually send attacking packets using compromised hosts, called stepping-stones, in order to avoid being detected and caught. With stepping-stone attacks, an intruder remotely logins these stepping-stones using programs like SSH or telnet, uses a chain of Internet hosts as relay machines, and then sends the attacking packets. A great number of detection approaches have been developed for stepping-stone intrusion (SSI) in the literature. Many of these existing detection methods worked effectively only when session manipulation by intruders is not present. When the session is manipulated by attackers, there are few known effective detection methods for SSI. It is important to know whether a detection algorithm for SSI is resistant on session manipulation by attackers. For session manipulation with chaff perturbation, software tools such as Scapy can be used to inject meaningless packets into a data stream. However, to the best of our knowledge, there are no existing effective tools or efficient algorithms to produce time-jittered network traffic that can be used to test whether an SSI detection method is resistant on intruders’ time-jittering manipulation. In this paper, we propose a framework to test resistency of detection algorithms for SSI on time-jittering manipulation. Our proposed framework can be used to test whether an existing or new SSI detection method is resistant on session manipulation by intruders with time-jittering.

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.

Accuracy Analysis of Centroid Detection Method Used to Star Sensor

2012 ◽  
Vol 468-471 ◽  
pp. 401-404 ◽  
Author(s):  
Qi Li ◽  
Wei Xu ◽  
Zhi Hai Xu ◽  
Hua Jun Feng
Keyword(s):  
Simulation Experiment ◽  
Detection Method ◽  
Detection Algorithm ◽  
Motion Blur ◽  
Detection Accuracy ◽  
Star Sensor ◽  
Detection Algorithms ◽  
Satellite Attitude ◽  
Centroid Detection ◽  
Acceptable Parameter

Star sensor is important equipment for measuring satellite attitude and motion, and star centroid detection accuracy is the basis of the overall accuracy of star sensor. In star sensors, slightly- defocus method is often adopted to acquire dispersive light spots so as to facilitate centroid detection, and certain motion blur can also be introduced because of the motion of satellites. In this paper, we analyzed several commonly-used centroid detection algorithms by using simulation experiment to study the influence of defocus and motion parameters on the accuracy of centroid detection algorithm and provided acceptable parameter value ranges.

scholarly journals Application of Smart Sensor in Underwater Weak Object Detection and Positioning

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yangmei Zhang
Keyword(s):  
Object Detection ◽  
Magnetic Induction ◽  
Detection Method ◽  
Negative Impact ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Smart Sensor ◽  
Scene Segmentation ◽  
Underwater Positioning ◽  
Underwater Object

This paper is aimed at studying underwater object detection and positioning. Objects are detected and positioned through an underwater scene segmentation-based weak object detection algorithm and underwater positioning technology based on the three-dimensional (3D) omnidirectional magnetic induction smart sensor. The proposed weak object detection involves a predesigned U-shaped network- (U-Net-) architectured image segmentation network, which has been improved before application. The key factor of underwater positioning technology based on 3D omnidirectional magnetic induction is the magnetic induction intensity. The results show that the image-enhanced object detection method improves the accuracy of Yellow Croaker, Goldfish, and Mandarin Fish by 3.2%, 1.5%, and 1.6%, respectively. In terms of sensor positioning technology, under the positioning Signal-to-Noise Ratio (SNR) of 15 dB and 20 dB, the curve trends of actual distance and positioning distance are consistent, while SNR = 10   dB , the two curves deviate greatly. The research conclusions read as follows: an underwater scene segmentation-based weak object detection method is proposed for invalid underwater object samples from poor labeling, which can effectively segment the background from underwater objects, remove the negative impact of invalid samples, and improve the precision of weak object detection. The positioning model based on a 3D coil magnetic induction sensor can obtain more accurate positioning coordinates. The effectiveness of 3D omnidirectional magnetic induction coil underwater positioning technology is verified by simulation experiments.

scholarly journals Defect Detection in Tire X-Ray Images Using Weighted Texture Dissimilarity

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Qiang Guo ◽  
Caiming Zhang ◽  
Hui Liu ◽  
Xiaofeng Zhang
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Weighted Averaging ◽  
X Ray ◽  
Feature Vectors ◽  
Detection Algorithms ◽  
Quantitative Metrics ◽  
Feature Similarity ◽  
Anomaly Map

Automatic defect detection is an important and challenging problem in industrial quality inspection. This paper proposes an efficient defect detection method for tire quality assurance, which takes advantage of the feature similarity of tire images to capture the anomalies. The proposed detection algorithm mainly consists of three steps. Firstly, the local kernel regression descriptor is exploited to derive a set of feature vectors of an inspected tire image. These feature vectors are used to evaluate the feature dissimilarity of pixels. Next, the texture distortion degree of each pixel is estimated by weighted averaging of the dissimilarity between one pixel and its neighbors, which results in an anomaly map of the inspected image. Finally, the defects are located by segmenting this anomaly map with a simple thresholding process. Different from some existing detection algorithms that fail to work for tire tread images, the proposed detection algorithm works well not only for sidewall images but also for tread images. Experimental results demonstrate that the proposed algorithm can accurately locate the defects of tire images and outperforms the traditional defect detection algorithms in terms of various quantitative metrics.

scholarly journals Fast Overlap Detection between Hard-Core Colloidal Cuboids and Spheres. The OCSI Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 72
Author(s):  
Luca Tonti ◽  
Alessandro Patti
Keyword(s):  
Colloidal Particles ◽  
Dynamic Properties ◽  
Wide Spectrum ◽  
Three Dimensional ◽  
Hard Core ◽  
Detection Algorithm ◽  
Three Dimensional Objects ◽  
Aggregation Behaviour ◽  
Sphere Radius ◽  
User Friendly

Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are commonly modelled as hard-core objects, whose collision test is key to correctly determine their phase and aggregation behaviour. In this work, we propose the Oriented Cuboid Sphere Intersection (OCSI) algorithm to detect collisions between prolate or oblate cuboids and spheres. We investigate OCSI’s performance by bench-marking it against a number of algorithms commonly employed in computer graphics and colloidal science: Quick Rejection First (QRI), Quick Rejection Intertwined (QRF) and a vectorized version of the OBB-sphere collision detection algorithm that explicitly uses SIMD Streaming Extension (SSE) intrinsics, here referred to as SSE-intr. We observed that QRI and QRF significantly depend on the specific cuboid anisotropy and sphere radius, while SSE-intr and OCSI maintain their speed independently of the objects’ geometry. While OCSI and SSE-intr, both based on SIMD parallelization, show excellent and very similar performance, the former provides a more accessible coding and user-friendly implementation as it exploits OpenMP directives for automatic vectorization.

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