Multi Orientation Text Detection in Natural Imagery

2018 ◽  
Vol 8 (4) ◽  
pp. 41-56
Author(s):  
Deepak Kumar ◽  
Ramandeep Singh
Keyword(s):  
Performance Metrics ◽  
Research Work ◽  
Text Detection ◽  
Natural Images ◽  
Hard Copy ◽  
Future Perspective ◽  
Depth Study ◽  
Benchmark Datasets ◽  
And Performance ◽  
Curved Text

Constant advancement and growth in digital technology is swiftly changing the scenario of text detection from hard copy images to natural images. An in-depth study of the previous research work reveals that though a lot of research work has been done on text detection and recognition in natural scene images, but most of the researchers have concluded their survey either on a horizontal or near to horizontal texts. Their survey somewhat speaks about multi-orientation text detection, but the curved text detection in natural images escaped their attention. It has necessitated exploration on the vital aspect of text detection field where detailed study of horizontal, near to horizontal, multi-orientation, and curved text finds a place in a single cover. To achieve this goal, the present study will focus on fundamental understanding, existing challenges, and the proven algorithms for text detection in natural images. The authors discuss the future perspective of recent advances in text detection in natural images with various benchmark datasets and performance metrics.

scholarly journals An Efficient Machine Learning based Model for Classification of Wearable Clothing

2021 ◽  
Vol 3 (4) ◽  
pp. 317-329
Author(s):  
Judy Simon
Keyword(s):  
High Efficiency ◽  
Performance Metrics ◽  
Research Work ◽  
Evaluation Procedure ◽  
Classification Error ◽  
Fashion Industry ◽  
Starting Point ◽  
Benchmark Datasets ◽  
And Performance ◽  
Traditional Approaches

Computer vision research and its applications in the fashion industry have grown popular due to the rapid growth of information technology. Fashion detection is increasingly popular because most fashion goods need detection before they could be worn. Early detection of the human body component of the input picture is necessary to determine where the garment area is and then synthesize it. For this reason, detection is the starting point for most of the in-depth research. The cloth detection of landmarks is retrieved through many feature items that emphasis on fashionate things. The feature extraction can be done for better accuracy, pose and scale transmission. These convolution filters extract the features through many epochs and max-pooling layers in the neural networks. The optimized classification has been done using SVM in this study, for attaining overall high efficiency. This proposed CNN approach fashionate things prediction is combined with SVM for better classification. Furthermore, the classification error is minimized through the evaluation procedure for obtaining better accuracy. Finally, this research work has attained good accuracy and other performance metrics than the different traditional approaches. The benchmark datasets, current methodologies, and performance comparisons are all reorganized for each piece.

Multiscale Nanoporous Structures for Sensing and Diagnostics

2009 ◽  
Vol 1236 ◽  
Author(s):  
Shalini Prasad
Keyword(s):  
Performance Metrics ◽  
Dynamic Range ◽  
Research Work ◽  
Serum Samples ◽  
Biomolecule Detection ◽  
Confined Spaces ◽  
And Performance ◽  
Hybrid Devices ◽  
Design And Manufacture ◽  
Analytical Tools

AbstractCurrent trends in sensing and diagnostics is towards developing hybrid devices that incorporate nanomaterial for enhancing device performance. These devices and systems have a broad impact ranging from personalized medicine in health care, environmental sensing and building multifunctional sensors for military applications. The overarching objective of the research work is to develop a new class of portable, bio-analytical tools with improved functionality and performance capabilities by utilizing the electrical effects on cellular and sub cellular species in micro and nanoscale domains.There are two key ideas underlying this research work. The first is to design and manufacture structures comprising of nanoscale-confined spaces integrated on to multi-scale architecture platforms. This model architecture has been engineered to harness the principle of macromolecular crowding for biomolecule binding and detection by monitoring perturbations in the electrical bi-layer in tailored nanoscale confined spaces. Enhanced performance metrics in biomolecule detection have been demonstrated in developing electrical immunoassays. We have demonstrated picogram/ml sensitivity in detection of specific cardiovascular disease biomarkers, cancer biomarkers from human serum samples with a dynamic range of response varying from pg/ml to g/ml and response time within 120 seconds.

An Analytical Transient Tire Model

2001 ◽  
Vol 29 (2) ◽  
pp. 108-132 ◽  
Author(s):  
A. Ghazi Zadeh ◽  
A. Fahim
Keyword(s):  
Transient Behavior ◽  
Stability Control ◽  
Vehicle Stability ◽  
Tire Model ◽  
Steering Angle ◽  
First Order ◽  
Vehicle Stability Control ◽  
Proposed Model ◽  
Depth Study ◽  
And Performance

Abstract The dynamics of a vehicle's tires is a major contributor to the vehicle stability, control, and performance. A better understanding of the handling performance and lateral stability of the vehicle can be achieved by an in-depth study of the transient behavior of the tire. In this article, the transient response of the tire to a steering angle input is examined and an analytical second order tire model is proposed. This model provides a means for a better understanding of the transient behavior of the tire. The proposed model is also applied to a vehicle model and its performance is compared with a first order tire model.

ANTECEDENTS AND CONSEQUENCES OF ORGANIZATIONAL JUSTICE

2016 ◽  
Vol 12 (02) ◽  
Author(s):  
Reeta Yadav
Keyword(s):  
Job Satisfaction ◽  
Organizational Justice ◽  
Organizational Citizenship ◽  
Organizational Citizenship Behavior ◽  
Turnover Intentions ◽  
Research Work ◽  
Citizenship Behavior ◽  
Future Research ◽  
Antecedents And Consequences ◽  
And Performance

Employee’s perception regarding fairness in the organization is termed as organizational justice. The objective of this paper is to study the antecedents and consequences of organizational justice on the basis of earlier relevant studies from the period ranging from 1964 to 2015. Previous research identified employee participation, communication, justice climate as the antecedents and trust, job satisfaction, commitment, turnover intentions, organizational citizenship behavior and performance as the consequences of organizational justice. Finding reveals the gaps existing in the literature and gives suggestions for future research work.

Assessment methods and performance metrics for redox flow batteries

Nature Energy ◽  
2021 ◽  
Author(s):  
Yanxin Yao ◽  
Jiafeng Lei ◽  
Yang Shi ◽  
Fei Ai ◽  
Yi-Chun Lu
Keyword(s):  
Performance Metrics ◽  
Assessment Methods ◽  
Redox Flow Batteries ◽  
Flow Batteries ◽  
And Performance

Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts

2021 ◽  
Author(s):  
Givanna H Putri ◽  
Irena Koprinska ◽  
Thomas M Ashhurst ◽  
Nicholas J C King ◽  
Mark N Read
Keyword(s):  
Single Cell ◽  
Performance Metrics ◽  
Clustering Algorithms ◽  
Latin Hypercube Sampling ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Evaluation Protocol ◽  
Benchmark Datasets ◽  
Pareto Fronts ◽  
Parameter Values

Abstract Motivation Many ‘automated gating’ algorithms now exist to cluster cytometry and single-cell sequencing data into discrete populations. Comparative algorithm evaluations on benchmark datasets rely either on a single performance metric, or a few metrics considered independently of one another. However, single metrics emphasize different aspects of clustering performance and do not rank clustering solutions in the same order. This underlies the lack of consensus between comparative studies regarding optimal clustering algorithms and undermines the translatability of results onto other non-benchmark datasets. Results We propose the Pareto fronts framework as an integrative evaluation protocol, wherein individual metrics are instead leveraged as complementary perspectives. Judged superior are algorithms that provide the best trade-off between the multiple metrics considered simultaneously. This yields a more comprehensive and complete view of clustering performance. Moreover, by broadly and systematically sampling algorithm parameter values using the Latin Hypercube sampling method, our evaluation protocol minimizes (un)fortunate parameter value selections as confounding factors. Furthermore, it reveals how meticulously each algorithm must be tuned in order to obtain good results, vital knowledge for users with novel data. We exemplify the protocol by conducting a comparative study between three clustering algorithms (ChronoClust, FlowSOM and Phenograph) using four common performance metrics applied across four cytometry benchmark datasets. To our knowledge, this is the first time Pareto fronts have been used to evaluate the performance of clustering algorithms in any application domain. Availability and implementation Implementation of our Pareto front methodology and all scripts and datasets to reproduce this article are available at https://github.com/ghar1821/ParetoBench. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Extended haplotype-phasing of long-read de novo genome assemblies using Hi-C

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zev N. Kronenberg ◽  
Arang Rhie ◽  
Sergey Koren ◽  
Gregory T. Concepcion ◽  
Paul Peluso ◽  
...  
Keyword(s):  
Zebra Finch ◽  
Cultured Cells ◽  
De Novo ◽  
Single Cells ◽  
Variant Calling ◽  
Chromatin Interaction ◽  
Extended Haplotype ◽  
Benchmark Datasets ◽  
And Performance ◽  
Genome Assemblies

AbstractHaplotype-resolved genome assemblies are important for understanding how combinations of variants impact phenotypes. To date, these assemblies have been best created with complex protocols, such as cultured cells that contain a single-haplotype (haploid) genome, single cells where haplotypes are separated, or co-sequencing of parental genomes in a trio-based approach. These approaches are impractical in most situations. To address this issue, we present FALCON-Phase, a phasing tool that uses ultra-long-range Hi-C chromatin interaction data to extend phase blocks of partially-phased diploid assembles to chromosome or scaffold scale. FALCON-Phase uses the inherent phasing information in Hi-C reads, skipping variant calling, and reduces the computational complexity of phasing. Our method is validated on three benchmark datasets generated as part of the Vertebrate Genomes Project (VGP), including human, cow, and zebra finch, for which high-quality, fully haplotype-resolved assemblies are available using the trio-based approach. FALCON-Phase is accurate without having parental data and performance is better in samples with higher heterozygosity. For cow and zebra finch the accuracy is 97% compared to 80–91% for human. FALCON-Phase is applicable to any draft assembly that contains long primary contigs and phased associate contigs.

Sign in / Sign up

Export Citation Format

Share Document