scholarly journals Efficient Filtering for Edge Extraction under Perspective Effect

2021 ◽  
Vol 11 (18) ◽  
pp. 8558
Author(s):  
Jian Li ◽  
Xiangjing An
Keyword(s):  
Prior Information ◽  
Real Data ◽  
Linear Filter ◽  
Edge Extraction ◽  
Scale Parameters ◽  
Lane Marking ◽  
Novel Approach ◽  
Perspective Effect ◽  
Speed Up ◽  
Computer Vision Applications

Though it is generally believed that edges should be extracted at different scales when using a linear filter, it is still difficult to determine the optimal scale for each filter. In this paper, we propose a novel approach called orientation and scale tuned difference of boxes (osDoB) to solve this problem. For certain computer vision applications, such as lane marking detection, the prior information about the concerned target can facilitate edge extraction in a top-down manner. Based on the perspective effect, we associate the scale of the edge in an image with the target size in the real world and assign orientation and scale parameters for filtering each pixel. Considering the fact that it is very time-consuming to naïvely perform filters with different orientations and scales, we further design an extended integration map technology to speed up filtering. Our method is validated on synthetic and real data. The experimental results show that assigning appropriate orientation and scale parameters for filters is effective and can be realized efficiently.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

scholarly journals Decision Support System for Evaluating Suitable Job Applicants

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1773
Author(s):  
Bogdan Walek ◽  
Ondrej Pektor ◽  
Radim Farana
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Real Data ◽  
Job Applicants ◽  
Inference System ◽  
Selection Procedures ◽  
Novel Approach ◽  
Fuzzy Expert Systems ◽  
Takagi Sugeno

This paper describes a novel approach in the area of evaluating suitable job applicants for various job positions, and specifies typical areas of requirement and their usage. Requirements for this decision-support system are defined in order to be used in middle-size companies. Suitable tools chosen were fuzzy expert systems, primarily the inference system Takagi-Sugeno type, which were then supplied with implementation of methods of variant multi-criteria analysis. The resulting system is a variable tool with the possibility to simply set the importance of individual selection criteria so that it can be used in various situations, primarily in repeated selection procedures for similar job positions. A strong emphasis is devoted to the explanatory module, which enables the results of the expert system to be used easily. Verification of the system on real data in cooperation with a collaborating company has proved that the system is easily usable.

A Novel Approach for Crawling the Opinions from World Wide Web

2016 ◽  
Vol 6 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Surbhi Bhatia ◽  
Manisha Sharma ◽  
Komal Kumar Bhatia
Keyword(s):  
World Wide ◽  
Opinion Mining ◽  
Real Data ◽  
User Generated Content ◽  
Decision Making Process ◽  
Web Pages ◽  
Data Sets ◽  
Web Technologies ◽  
Design And Implementation ◽  
Novel Approach

Due to the sudden and explosive increase in web technologies, huge quantity of user generated content is available online. The experiences of people and their opinions play an important role in the decision making process. Although facts provide the ease of searching information on a topic but retrieving opinions is still a crucial task. Many studies on opinion mining have to be undertaken efficiently in order to extract constructive opinionated information from these reviews. The present work focuses on the design and implementation of an Opinion Crawler which downloads the opinions from various sites thereby, ignoring rest of the web. Besides, it also detects web pages which frequently undergo updation by calculating the timestamp for its revisit in order to extract relevant opinions. The performance of the Opinion Crawler is justified by taking real data sets that prove to be much more accurate in terms of precision and recall quality attributes.

scholarly journals A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Ibrahim Selim ◽  
Hatem Awad A. Khater
Keyword(s):  
Feature Extraction ◽  
Virus Disease ◽  
Real Data ◽  
Ct Images ◽  
Learning Tools ◽  
Corona Virus ◽  
Novel Approach ◽  
Hybrid Classification ◽  
Hybrid Feature Extraction ◽  
First Time

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

scholarly journals Sparse Signal Recovery from Modulo Observations

2020 ◽  
Author(s):  
Viraj Shah ◽  
Chinmay Hegde
Keyword(s):  
Phase Retrieval ◽  
Dynamic Range ◽  
Signal Reconstruction ◽  
Real Data ◽  
Superior Performance ◽  
Signal Recovery ◽  
Reconstruction Methods ◽  
Novel Approach ◽  
Sparsity Constraints ◽  
Improved Performance

Abstract We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.

Clustering Mashups by Integrating Structural and Semantic Similarities Using Fuzzy AHP

2021 ◽  
Vol 18 (1) ◽  
pp. 34-57
Author(s):  
Weifeng Pan ◽  
Xinxin Xu ◽  
Hua Ming ◽  
Carl K. Chang
Keyword(s):  
Semantic Similarity ◽  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Fuzzy Ahp ◽  
Real Data ◽  
Structural Similarity ◽  
Analytic Hierarchy ◽  
Data Set ◽  
Novel Approach ◽  
Hierarchy Process

Mashup technology has become a promising way to develop and deliver applications on the web. Automatically organizing Mashups into functionally similar clusters helps improve the performance of Mashup discovery. Although there are many approaches aiming to cluster Mashups, they solely focus on utilizing semantic similarities to guide the Mashup clustering process and are unable to utilize both the structural and semantic information in Mashup profiles. In this paper, a novel approach to cluster Mashups into groups is proposed, which integrates structural similarity and semantic similarity using fuzzy AHP (fuzzy analytic hierarchy process). The structural similarity is computed from usage histories between Mashups and Web APIs using SimRank algorithm. The semantic similarity is computed from the descriptions and tags of Mashups using LDA (latent dirichlet allocation). A clustering algorithm based on the genetic algorithm is employed to cluster Mashups. Comprehensive experiments are performed on a real data set collected from ProgrammableWeb. The results show the effectiveness of the approach when compared with two kinds of conventional approaches.

scholarly journals A Robust Localization System for Inspection Robots in Sewer Networks

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4946 ◽  
Author(s):  
David Alejo ◽  
Fernando Caballero ◽  
Luis Merino
Keyword(s):  
Cross Sections ◽  
Prior Information ◽  
Real Data ◽  
Localization System ◽  
Sewer Network ◽  
Localization And Mapping ◽  
Localization Errors ◽  
Robust Localization ◽  
Monte Carlo Localization ◽  
Inspection Robots

Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network. It is capable of sensing gas concentrations and detecting failures in the network such as cracks and holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely geo-localized to allow the operators performing the required correcting measures. To this end, this paper presents a robust localization system for global pose estimation on sewers. It makes use of prior information of the sewer network, including its topology, the different cross sections traversed and the position of some elements such as manholes. The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Additionally, the localization is further refined with novel updating steps proposed in this paper which are activated whenever a discrete element in the sewer network is detected or the relative orientation of the robot over the sewer gallery could be estimated. Each part of the system has been validated with real data obtained from the sewers of Barcelona. The whole system is able to obtain median localization errors in the order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the approach.

scholarly journals Different Approaches to Estimation of the Gompertz Distribution under the Progressive Type-II Censoring Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Kyeongjun Lee ◽  
Jung-In Seo
Keyword(s):  
Nuisance Parameter ◽  
Estimation Method ◽  
Real Data ◽  
Type Ii ◽  
Gompertz Distribution ◽  
Type Ii Censoring ◽  
Scale Parameters ◽  
Progressive Type ◽  
Censoring Scheme ◽  
Progressive Type Ii Censoring

This paper provides an estimation method for an unknown parameter by extending weighted least-squared and pivot-based methods to the Gompertz distribution with the shape and scale parameters under the progressive Type-II censoring scheme, which induces a consistent estimator and an unbiased estimator of the scale parameter. In addition, a way to deal with a nuisance parameter is provided in the pivot-based approach. For evaluation and comparison, the Monte Carlo simulations are conducted, and real data are analyzed.

An Unsupervised Approach for Determining Link Specifications

2018 ◽  
Vol 13 (4) ◽  
pp. 104-123
Author(s):  
Khayra Bencherif ◽  
Mimoun Malki ◽  
Djamel Amar Bensaber
Keyword(s):  
Linked Data ◽  
Open Data ◽  
Real Data ◽  
Knowledge Bases ◽  
Structured Data ◽  
Data Sets ◽  
Novel Approach ◽  
Link Discovery ◽  
Unsupervised Approach

This article describes how the Linked Open Data Cloud project allows data providers to publish structured data on the web according to the Linked Data principles. In this context, several link discovery frameworks have been developed for connecting entities contained in knowledge bases. In order to achieve a high effectiveness for the link discovery task, a suitable link configuration is required to specify the similarity conditions. Unfortunately, such configurations are specified manually; which makes the link discovery task tedious and more difficult for the users. In this article, the authors address this drawback by proposing a novel approach for the automatic determination of link specifications. The proposed approach is based on a neural network model to combine a set of existing metrics into a compound one. The authors evaluate the effectiveness of the proposed approach in three experiments using real data sets from the LOD Cloud. In addition, the proposed approach is compared against link specifications approaches to show that it outperforms them in most experiments.

Shape Optimisation of Multi-Chamber Acoustical Plenums Using BEM, Neural Networks, and GA Method

2016 ◽  
Vol 41 (1) ◽  
pp. 43-53 ◽  
Author(s):  
Ying-Chun Chang ◽  
Ho-Chih Cheng ◽  
Min-Chie Chiu ◽  
Yuan-Hung Chien
Keyword(s):  
Noise Reduction ◽  
Boundary Element ◽  
Transmission Loss ◽  
Real Data ◽  
Element Model ◽  
Shape Optimisation ◽  
Design Data ◽  
Input Design ◽  
Speed Up ◽  
Fixed Space

Abstract Research on plenums partitioned with multiple baffles in the industrial field has been exhaustive. Most researchers have explored noise reduction effects based on the transfer matrix method and the boundary element method. However, maximum noise reduction of a plenum within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of multi-chamber plenums becomes essential. In this paper, two kinds of multi-chamber plenums (Case I: a two-chamber plenum that is partitioned with a centre-opening baffle; Case II: a three-chamber plenum that is partitioned with two centre-opening baffles) within a fixed space are assessed. In order to speed up the assessment of optimal plenums hybridized with multiple partitioned baffles, a simplified objective function (OBJ) is established by linking the boundary element model (BEM, developed using SYSNOISE) with a polynomial neural network fit with a series of real data – input design data (baffle dimensions) and output data approximated by BEM data in advance. To assess optimal plenums, a genetic algorithm (GA) is applied. The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.

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