scholarly journals MATCHING REAL AND SYNTHETIC PANORAMIC IMAGES USING A VARIANT OF GEOMETRIC HASHING

2017 ◽  
Vol IV-1/W1 ◽  
pp. 199-206 ◽  
Author(s):  
J. Li-Chee-Ming ◽  
C. Armenakis
Keyword(s):  
Detection Algorithm ◽  
Geometric Hashing ◽  
Detection Techniques ◽  
3D Cad ◽  
Visual Model ◽  
Model Based ◽  
Image Registration Algorithm ◽  
Camera Pose ◽  
Panoramic Images ◽  
Tracking By Detection

This work demonstrates an approach to automatically initialize a visual model-based tracker, and recover from lost tracking, without prior camera pose information. These approaches are commonly referred to as <i>tracking-by-detection</i>. Previous tracking-by-detection techniques used either fiducials (i.e. landmarks or markers) or the object’s texture. The main contribution of this work is the development of a tracking-by-detection algorithm that is based solely on natural geometric features. A variant of geometric hashing, a model-to-image registration algorithm, is proposed that searches for a matching panoramic image from a database of synthetic panoramic images captured in a 3D virtual environment. The approach identifies corresponding features between the matched panoramic images. The corresponding features are to be used in a photogrammetric space resection to estimate the camera pose. The experiments apply this algorithm to initialize a model-based tracker in an indoor environment using the 3D CAD model of the building.

scholarly journals A Robust Tracking-by-Detection Algorithm Using Adaptive Accumulated Frame Differencing and Corner Features

2020 ◽  
Vol 6 (4) ◽  
pp. 25
Author(s):  
Nahlah Algethami ◽  
Sam Redfern
Keyword(s):  
Detection Algorithm ◽  
Image Features ◽  
Corner Detection ◽  
Wide Angle ◽  
Tracking Accuracy ◽  
Range Of Movement ◽  
Illumination Variation ◽  
Detection Techniques ◽  
Wide Range ◽  
Tracking By Detection

We propose a tracking-by-detection algorithm to track the movements of meeting participants from an overhead camera. An advantage of using overhead cameras is that all objects can typically be seen clearly, with little occlusion; however, detecting people from a wide-angle overhead view also poses challenges such as people’s appearance significantly changing due to their position in the wide-angle image, and generally from a lack of strong image features. Our experimental datasets do not include empty meeting rooms, and this means that standard motion based detection techniques (e.g., background subtraction or consecutive frame differencing) struggle since there is no prior knowledge for a background model. Additionally, standard techniques may perform poorly when there is a wide range of movement behaviours (e.g. periods of no movement and periods of fast movement), as is often the case in meetings. Our algorithm uses a novel coarse-to-fine detection and tracking approach, combining motion detection using adaptive accumulated frame differencing (AAFD) with Shi-Tomasi corner detection. We present quantitative and qualitative evaluation which demonstrates the robustness of our method to track people in environments where object features are not clear and have similar colour to the background. We show that our approach achieves excellent performance in terms of the multiple object tracking accuracy (MOTA) metrics, and that it is particularly robust to initialisation differences when compared with baseline and state of the art trackers. Using the Online Tracking Benchmark (OTB) videos we also demonstrate that our tracker is very strong in the presence of background clutter, deformation and illumination variation.

Plagiarism Detection Algorithm Model Based on NLP Technology

2021 ◽  
pp. 43-61
Author(s):  
Ahmed A. Elngar ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Negative Consequences ◽  
Plagiarism Detection ◽  
Detection Techniques ◽  
Lexical Semantic ◽  
Model Based ◽  
Music Software ◽  
Grammar Analysis ◽  
Phd Thesis

We can bear in mind that each of us has plagiarized a text without realizing that it was plagiarism, Plagiarism can happen in Articles, Papers, Researches, literature, music, software, scientific, newspapers, websites, Master and PHD Thesis and many other fields, So plagiarism has become serious major problem to teachers, researchers and publishers, There are divergent opinions about how to define plagiarism and what makes plagiarism serious. So, the detecting plagiarism is very important, so in this survey we explicate the concept of ;plagiarism ; and provide an overview of different plagiarism software and tools to solve the plagiarism problem, and will discuss the plagiarism process, types and detection methodologies. We can define that plagiarism is the brief and the description of this sentence ;someone used someone else’s mental product (such as its texts, ideas, or privacy). We suggest that what makes plagiarism so reprehensible is that it distorts scientific credit. In addition, intentional plagiarism indicates dishonesty. Moreover, there are a number of possible negative consequences of plagiarism. So we just create a framework for external plagiarism detection in which a some NLP processes are applied to process a set of suspicious and original documents, we have classified the different plagiarism detection techniques based on Lexical, Semantic, Syntactic and grammar analysis algorithms, And all of these algorithms precedes it NLP processing.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

Adaptive Edge Detection Method Towards Features Extraction From Diverse Medical Imaging Technologies

2018 ◽  
pp. 1245-1278
Author(s):  
Indra Kanta Maitra ◽  
Samir Kumar Bandhyopadhyaay
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Techniques ◽  
Robust Segmentation ◽  
Image Characteristics ◽  
Edge Detection Method ◽  
Medical Image Enhancement

The CAD is a relatively young interdisciplinary technology, has had a tremendous impact on medical diagnosis specifically cancer detection. The accuracy of CAD to detect abnormalities on medical image analysis requires a robust segmentation algorithm. To achieve accurate segmentation, an efficient edge-detection algorithm is essential. Medical images like USG, X-Ray, CT and MRI exhibit diverse image characteristics but are essentially collection of intensity variations from which specific abnormalities are needed to be isolated. In this chapter a robust medical image enhancement and edge detection algorithm is proposed, using tree-based adaptive thresholding technique. It has been compared with different classical edge-detection techniques using one sample two tail t-test to exam whether the null hypothesis can be supported. The proposed edge-detection algorithm showing 0.07 p-values and 2.411 t-stat where α = 0.025. Moreover the proposed edge is single pixeled and connected which is very significant for medical edge detection.

scholarly journals 3D Object Detection Algorithm for Panoramic Images With Multi-Scale Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171461-171470
Author(s):  
Dianwei Wang ◽  
Yanhui He ◽  
Ying Liu ◽  
Daxiang Li ◽  
Shiqian Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
3D Object ◽  
Multi Scale ◽  
Panoramic Images ◽  
3D Object Detection

scholarly journals Application of machine learning to microseismic event detection in distributed acoustic sensing data

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. KS149-KS160 ◽  
Author(s):  
Anna L. Stork ◽  
Alan F. Baird ◽  
Steve A. Horne ◽  
Garth Naldrett ◽  
Sacha Lapins ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Acoustic Sensing ◽  
Detection Techniques ◽  
Real Noise ◽  
Temporal Sampling ◽  
Manual Inspection ◽  
Microseismic Events ◽  
Distributed Acoustic Sensing

This study presents the first demonstration of the transferability of a convolutional neural network (CNN) trained to detect microseismic events in one fiber-optic distributed acoustic sensing (DAS) data set to other data sets. DAS increasingly is being used for microseismic monitoring in industrial settings, and the dense spatial and temporal sampling provided by these systems produces large data volumes (approximately 650 GB/day for a 2 km long cable sampling at 2000 Hz with a spatial sampling of 1 m), requiring new processing techniques for near-real-time microseismic analysis. We have trained the CNN known as YOLOv3, an object detection algorithm, to detect microseismic events using synthetically generated waveforms with real noise superimposed. The performance of the CNN network is compared to the number of events detected using filtering and amplitude threshold (short-term average/long-term average) detection techniques. In the data set from which the real noise is taken, the network is able to detect >80% of the events identified by manual inspection and 14% more than detected by standard frequency-wavenumber filtering techniques. The false detection rate is approximately 2% or one event every 20 s. In other data sets, with monitoring geometries and conditions previously unseen by the network, >50% of events identified by manual inspection are detected by the CNN.

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