Robust Feature Points Extraction Based on Harris and SIFT

2013 ◽  
Vol 347-350 ◽  
pp. 3500-3504
Author(s):  
Xiao Ran Guo ◽  
Shao Hui Cui ◽  
Fang Dan
Keyword(s):  
Video Sequence ◽  
State Of The Art ◽  
Image Motion ◽  
Video Sequences ◽  
Stabilization System ◽  
Feature Points ◽  
Image Stabilization ◽  
Current State ◽  
Novel Approach ◽  
Key Points

This article presents a novel approach to extract robust local feature points of video sequence in digital image stabilization system. Robust Harris-SIFT detector is proposed to select the most stable SIFT key points in the video sequence where image motion is happened due to vehicle or platform vibration. Experimental results show that the proposed scheme is robust to various transformations of video sequences, such as translation, rotation and scaling, as well as blurring. Compared with the current state-of-the-art schemes, the proposed scheme yields better performances.

scholarly journals Knocking on IPs: Identifying HTTPS Websites for Zero-Rated Traffic

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Mariano Di Martino ◽  
Peter Quax ◽  
Wim Lamotte
Keyword(s):  
Experimental Analysis ◽  
Service Providers ◽  
State Of The Art ◽  
Domain Name ◽  
Internet Service ◽  
Ip Address ◽  
Identification Methods ◽  
Current State ◽  
Novel Approach ◽  
Current Zero

Zero-rating is a technique where internet service providers (ISPs) allow consumers to utilize a specific website without charging their internet data plan. Implementing zero-rating requires an accurate website identification method that is also efficient and reliable to be applied on live network traffic. In this paper, we examine existing website identification methods with the objective of applying zero-rating. Furthermore, we demonstrate the ineffectiveness of these methods against modern encryption protocols such as Encrypted SNI and DNS over HTTPS and therefore show that ISPs are not able to maintain the current zero-rating approaches in the forthcoming future. To address this concern, we present “Open-Knock,” a novel approach that is capable of accurately identifying a zero-rated website, thwarts free-riding attacks, and is sustainable on the increasingly encrypted web. In addition, our approach does not require plaintext protocols or preprocessed fingerprints upfront. Finally, our experimental analysis unveils that we are able to convert each IP address to the correct domain name for each website in the Tranco top 6000 websites list with an accuracy of 50.5% and therefore outperform the current state-of-the-art approaches.

scholarly journals LEO: Liquid Exploration Online

2020 ◽  
Vol 2 (1) ◽  
pp. 58-80
Author(s):  
Frank Hoeller
Keyword(s):  
Path Planning ◽  
State Of The Art ◽  
Simulation Experiments ◽  
Coverage Path Planning ◽  
Current State ◽  
Extensive Evaluation ◽  
Novel Approach ◽  
Local Navigation ◽  
Local Decision ◽  
Real Robot

This article introduces a novel approach to the online complete- coverage path planning (CCPP) problem that is specically tailored to the needs of skid-steer tracked robots. In contrast to most of the current state-of-the-art algorithms for this task, the proposed algorithm reduces the number of turning maneuvers, which are responsible for a large part of the robot's energy consumption. Nevertheless, the approach still keeps the total distance traveled at a competitive level. The algorithm operates on a grid-based environment representation and uses a 3x3 prioritization matrix for local navigation decisions. This matrix prioritizes cardinal di- rections leading to a preference for straight motions. In case no progress can be achieved based on a local decision, global path planning is used to choose a path to the closest known unvisited cell, thereby guaranteeing completeness of the approach. In an extensive evaluation using simulation experiments, we show that the new algorithm indeed generates competi- tively short paths with largely reduced turning costs, compared to other state-of-the-art CCPP algorithms. We also illustrate its performance on a real robot.

Sentence Processing

2019 ◽  
pp. 675-709
Author(s):  
Ina Bornkessel-Schlesewsky ◽  
Matthias Schlesewsky
Keyword(s):  
Empirical Evidence ◽  
Sentence Processing ◽  
State Of The Art ◽  
Current State ◽  
Key Points ◽  
The Brain

Neurolinguistic approaches to sentence processing have recently begun to focus on neurobiological plausibility. Thus, rather than seeking primarily to establish mappings between linguistic and cognitive concepts and the brain, the question of how sentence processing is implemented by the brain’s unique biological hardware has become increasingly important. This chapter reviews the current state of the art with respect to the neurobiology of sentence processing, adopting both a neuroanatomical and a timing-based perspective. For both of these domains, the chapter provides an overview of current models and frameworks, as well as the empirical evidence supporting them. In each case, it highlights areas of consensus, as well as key points of difference between approaches where no consensus has yet been reached.

scholarly journals A System for Extracting Study Design Parameters from Nutritional Genomics Abstracts

2013 ◽  
Vol 10 (2) ◽  
pp. 82-93 ◽  
Author(s):  
Cassidy Kelly ◽  
Hui Yang
Keyword(s):  
Language Processing ◽  
Study Design ◽  
State Of The Art ◽  
Design Parameters ◽  
Regular Expressions ◽  
Journal Articles ◽  
Current State ◽  
Novel Approach ◽  
Algorithmic Framework ◽  
Nutritional Genomics

Summary The extraction of study design parameters from biomedical journal articles is an important problem in natural language processing (NLP). Such parameters define the characteristics of a study, such as the duration, the number of subjects, and their profile. Here we present a system for extracting study design parameters from sentences in article abstracts. This system will be used as a component of a larger system for creating nutrigenomics networks from articles in the nutritional genomics domain. The algorithms presented consist of manually designed rules expressed either as regular expressions or in terms of sentence parse structure. A number of filters and NLP tools are also utilized within a pipelined algorithmic framework. Using this novel approach, our system performs extraction at a finer level of granularity than comparable systems, while generating results that surpass the current state of the art.

scholarly journals HYBRID APPROACH FOR KEY FRAME EXTRACTION FROM VIDEO SEQUENCE

2017 ◽  
Vol 5 (4RACSIT) ◽  
pp. 97-104
Author(s):  
Satish Kumar
Keyword(s):  
Background Subtraction ◽  
Video Sequence ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Considerable Improvement ◽  
Video Sequences ◽  
Key Frame ◽  
Key Frames ◽  
Mixture Of Gaussian ◽  
Fine Tune

This paper proposed and developed hybrid approach for extraction of key-frames from video sequences from stationary camera. This method first uses histogram difference to extract the candidate key frames from the video sequences, later using Background subtraction algorithm (Mixture of Gaussian) was used to fine tune the final key frames from the video sequences. This developed approach show considerable improvement over the state-of-the art techniques and same is reported in this paper.

scholarly journals FUTUREGAN: ANTICIPATING THE FUTURE FRAMES OF VIDEO SEQUENCES USING SPATIO-TEMPORAL 3D CONVOLUTIONS IN PROGRESSIVELY GROWING GANS

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 3-11 ◽  
Author(s):  
S. Aigner ◽  
M. Körner
Keyword(s):  
Video Sequence ◽  
State Of The Art ◽  
Input Sequence ◽  
Video Sequences ◽  
Complex Task ◽  
High Quality ◽  
Spatio Temporal ◽  
Resolution Single ◽  
Additional Constraints ◽  
Video Prediction

<p><strong>Abstract.</strong> We introduce a new <i>encoder-decoder GAN</i> model, <i>FutureGAN</i>, that predicts future frames of a video sequence conditioned on a sequence of past frames. During training, the networks solely receive the raw pixel values as an input, without relying on additional constraints or dataset specific conditions. To capture both the spatial and temporal components of a video sequence, spatio-temporal 3d convolutions are used in all encoder and decoder modules. Further, we utilize concepts of the existing <i>progressively growing GAN (PGGAN)</i> that achieves high-quality results on generating high-resolution single images. The FutureGAN model extends this concept to the complex task of video prediction. We conducted experiments on three different datasets, <i>MovingMNIST</i>, <i>KTH Action</i>, and <i>Cityscapes</i>. Our results show that the model learned representations to transform the information of an input sequence into a plausible future sequence effectively for all three datasets. The main advantage of the FutureGAN framework is that it is applicable to various different datasets without additional changes, whilst achieving stable results that are competitive to the state-of-the-art in video prediction. The code to reproduce the results of this paper is publicly available at https://github.com/TUM-LMF/FutureGAN.</p>

scholarly journals Contiguous Loss for Motion-Based, Non-Aligned Image Deblurring

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 630
Author(s):  
Wenjia Niu ◽  
Kewen Xia ◽  
Yongke Pan
Keyword(s):  
Video Sequence ◽  
State Of The Art ◽  
Video Sequences ◽  
Dynamic Scenes ◽  
Multiple Objects ◽  
Proposed Model ◽  
Ill Posed ◽  
Spatio Temporal ◽  
Better Than ◽  
General Dynamic

In general dynamic scenes, blurring is the result of the motion of multiple objects, camera shaking or scene depth variations. As an inverse process, deblurring extracts a sharp video sequence from the information contained in one single blurry image—it is itself an ill-posed computer vision problem. To reconstruct these sharp frames, traditional methods aim to build several convolutional neural networks (CNN) to generate different frames, resulting in expensive computation. To vanquish this problem, an innovative framework which can generate several sharp frames based on one CNN model is proposed. The motion-based image is put into our framework and the spatio-temporal information is encoded via several convolutional and pooling layers, and the output of our model is several sharp frames. Moreover, a blurry image does not have one-to-one correspondence with any sharp video sequence, since different video sequences can create similar blurry images, so neither the traditional pixel2pixel nor perceptual loss is suitable for focusing on non-aligned data. To alleviate this problem and model the blurring process, a novel contiguous blurry loss function is proposed which focuses on measuring the loss of non-aligned data. Experimental results show that the proposed model combined with the contiguous blurry loss can generate sharp video sequences efficiently and perform better than state-of-the-art methods.

scholarly journals Robust Non-Rigid Feature Matching for Image Registration Using Geometry Preserving

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2729 ◽  
Author(s):  
Hao Zhu ◽  
Ke Zou ◽  
Yongfu Li ◽  
Ming Cen ◽  
Lyudmila Mihaylova
Keyword(s):  
Image Registration ◽  
Expectation Maximization ◽  
Feature Matching ◽  
State Of The Art ◽  
Gaussian Mixture ◽  
Connectivity Matrix ◽  
Local Geometry ◽  
Feature Points ◽  
Ml Estimation ◽  
Current State

In this paper, a robust non-rigid feature matching approach for image registration with geometry constraints is proposed. The non-rigid feature matching approach is formulated as a maximum likelihood (ML) estimation problem. The feature points of one image are represented by Gaussian mixture model (GMM) centroids, and are fitted to the feature points of the other image by moving coherently to encode the global structure. To preserve the local geometry of these feature points, two local structure descriptors of the connectivity matrix and Laplacian coordinate are constructed. The expectation maximization (EM) algorithm is applied to solve this ML problem. Experimental results demonstrate that the proposed approach has better performance than current state-of-the-art methods.

Dynamic Shadow Detection and Removal for Vehicle Tracking System

2021 ◽  
Author(s):  
Kalpesh R. Jadav ◽  
Arvind R. Yadav
Keyword(s):  
Detection Rate ◽  
Video Sequence ◽  
State Of The Art ◽  
Tracking System ◽  
Shadow Detection ◽  
Vehicle Tracking ◽  
Video Sequences ◽  
Frame Difference ◽  
Discrimination Rate ◽  
High Discrimination

Shadow leads to failure of moving target positioning, segmentation, tracking, and classification in the video surveillance system thus shadow detection and removal is essential for further computer vision process. The existing state-of-the-art methods for dynamic shadow detection have produced a high discrimination rate but a poor detection rate (foreground pixels are classified as shadow pixels). This paper proposes an effective method for dynamic shadow detection and removal based on intensity ratio along with frame difference, gamma correction, and morphology operations. The performance of the proposed method has been tested on two outdoor ATON datasets, namely, highway-I and highway-III for vehicle tracking systems. The proposed method has produced a discrimination rate of 89.07% and a detection rate of 80.79% for highway-I video sequences. Similarly, for a highway-III video sequence, the discrimination rate of 85.60% and detection rate of 84.05% have been obtained. Investigational outcomes show that the proposed method is the simple, steadiest, and robust for dynamic shadow detection on the dataset used in this work.

Interactions Between Ketamine and Magnesium for the Treatment of Pain: Current State of the Art

2021 ◽  
Vol 20 ◽  
Author(s):  
Katarina Savić Vujović ◽  
Sonja Vučković ◽  
Radan Stojanović ◽  
Nevena Divac ◽  
Branislava Medić ◽  
...  
Keyword(s):  
State Of The Art ◽  
New Drugs ◽  
Nmda Receptor Antagonists ◽  
Dosing Regimen ◽  
Chronological Order ◽  
The Past ◽  
Current State ◽  
Novel Approach ◽  
Dosage Administration ◽  
Novel Therapeutic

Background: Over the past three decades, NMDA-receptor antagonists have been shown to be efficient drugs for treating pain and particularly pain that is resistant to conventional analgesics. Emphasis will be on the old-new drugs, ketamine and magnesium and their combination as a novel approach for treating chronic pain. Methods: The MEDLINE database was searched via PubMed for articles which were published up to March 1, 2020 with the key words ‘ketamine’, ‘magnesium’ and ‘pain’ (in the title/abstract). Results: Studies in animals, as well as humans have shown that interactions of ketamine and magnesium can be additive, antagonistic and synergistic. These discrepancies might be due to differences in magnesium and ketamine dosage, administration times and the chronological order of drugs administration. Different kinds of pain can also be the source of divergent results. Conclusion: This review explains why studies performed with a combination of ketamine and magnesium have given inconsistent results. Because of the lack of efficacy of drugs available for pain, ketamine and magnesium in combination provide a novel therapeutic approach that needs to be standardized with a suitable dosing regimen, including the chronological order of drug administration.

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