scholarly journals A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2155
Author(s):  
Julia Shemiakina ◽  
Elena Limonova ◽  
Natalya Skoryukina ◽  
Vladimir V. Arlazarov ◽  
Dmitry P. Nikolaev
Keyword(s):  
Quality Assessment ◽  
Predictive Value ◽  
Image Quality Assessment ◽  
Maximum Level ◽  
Poor Quality ◽  
Image Point ◽  
Image Size ◽  
Predictive Values ◽  
Document Recognition ◽  
Identity Document

In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of an evaluated field are assumed to be predefined in the document template. This information is used to estimate the maximum level of distortion acceptable for recognition. The method was tested on a dataset of synthetically distorted field images. Synthetic images were created based on document template images from the publicly available dataset MIDV-2019. In the experiments, the method shows stable predictive values for different strings of one font and height. When used as a pre-recognition rejection method, it demonstrates a positive predictive value of 86.7% and a negative predictive value of 64.1% on the synthetic dataset. A comparison with other geometric quality assessment methods shows the superiority of our approach.

Image Quality Assessment Based on Invariant Moments Similarity

2012 ◽  
Vol 546-547 ◽  
pp. 565-569
Author(s):  
Mei Wang ◽  
E Ye Wang ◽  
Guo Hua Pan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Experimental Result ◽  
Original Image ◽  
Image Size ◽  
Invariant Moment ◽  
Invariant Moments ◽  
Computing Complexity ◽  
Better Than

To resolve the problems of the image quality assessment issue and the algorithm adaptability for different image size and deformation, this paper proposes a image quality assessment algorithm based on Invariant Moments Similarity. Firstly, Hu invariant moments values of original image and evaluated image are computed. Secondly the invariant moments distance is completed between original image and evaluated image. At last, the method assess the restoration image quality depend on the invariant moment distance. The experimental result shows that the algorithm result is better than MSE, PSNR, SSIM for the same-size images. And the algorithm based on invariant moment similarity can evaluate different image-size and deformation images with low computing-complexity. The assessment experimental result for difference actual images certifies the algorithm effectiveness.

scholarly journals Multi-Pooled Inception Features for No-Reference Image Quality Assessment

2020 ◽  
Vol 10 (6) ◽  
pp. 2186
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Input Image ◽  
Reference Image ◽  
Parameter Study ◽  
Image Size ◽  
In The Wild ◽  
Display Technology ◽  
Multi Level

Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal—called MultiGAP-NRIQA—is able to outperform the state-of-the-art on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.

scholarly journals PREDICTIVE VALUE OF THE GENERAL MOVEMENTS ASSESSMENT IN PRETERM INFANTS: A META-ANALYSIS

2020 ◽  
Vol 38 ◽  
Author(s):  
Camila da Silva Pires ◽  
Sérgio Tadeu Martins Marba ◽  
Jamil Pedro de Siqueira Caldas ◽  
Mônica de Carvalho Sanchez Stopiglia
Keyword(s):  
Cerebral Palsy ◽  
Diagnostic Accuracy ◽  
Quality Assessment ◽  
Predictive Value ◽  
Calculated Data ◽  
Negative Likelihood Ratio ◽  
Qualitative Assessment ◽  
Predictive Values ◽  
General Movements ◽  
Neurodevelopment Disorders

ABSTRACT Objective: To discuss the predictive value of the General Movements Assessment for the diagnosis of neurodevelopment disorders in preterm newborns. Data source: We conducted a systematic literature review using the following databases: Scientific Electronic Library Online (SciELO), National Library of Medicine, National Institutes of Health (PubMed), and Excerpta Medica Database (EMBASE). The articles were filtered by language, year of publication, population of interest, use of Prechtl’s Method on the Qualitative Assessment of General Movements, and presence of variables related to the predictive value. The Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess the methodology of the included studies. Sensitivity, specificity, Diagnostic Odds Ratio, positive and negative likelihood ratio, and parameter of accuracy were calculated. Data synthesis: Six of 342 articles were included. The evaluation of Writhing Movements is a good indicator for recognizing cerebral palsy, as it has high values for the sensitivity and accuracy parameters. The evaluation of Fidgety Movements has the strongest predictive validity for cerebral palsy, as it has high values in all measures of diagnostic accuracy. The quality assessment shows high risk of bias for patient selection and flow and timing of the evaluation. Therefore, the scale has potential to detect individuals with neurodevelopment disorders. However, the studies presented limitations regarding the selection of subjects and the assessment of neurological outcomes. Conclusions: Despite the high predictive values of the tool to identify neurological disorders, research on the subject is required due to the heterogeneity of the current studies.

scholarly journals Tongue Image Quality Assessment Based on Deep Convolutional Neural Network

2020 ◽  
Author(s):  
Tao Jiang ◽  
Xiao-juan Hu ◽  
Xing-hua Yao ◽  
Li-ping Tu ◽  
Jing-bin Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Model Performance ◽  
Poor Quality ◽  
Deep Convolutional Neural Network ◽  
Deep Cnn

Abstract Background: With the wide application of digital tongue diagnosis instrument, massive tongue images will be produced. Adequate image quality is the prerequisite to ensure accurate tongue image analysis. In the process of tongue image collection, improper operation may lead to many poor-quality images (fogging, underexposure, overexposure, blurred focus, wrong tongue posture, etc.), which seriously affect the image processing and the accuracy of image analysis. However traditional pattern recognition is difficult to evaluate the quality of tongue images by extracting features and manual removal of tongue images with bad quality consumes a lot of labor and has a high error rate. In this research, we utilized a deep convolutional neural network to automatically select bad quality tongue images.Methods: The present study was conducted to identify the most appropriate CNN model for Tongue Image Quality Assessment based on deep CNN. The CNN model was evaluated by using Residual neural network and compared with VGGNet and DenseNet. Evaluation metrics such as accuracy, precision, recall, and F1-score were used for CNN model performance.Results: A detection model is established for tongue image quality control based on deep residual network, with an average accuracy of 99.04%, accuracy of 99.05%, recall of 99.04%, and F1-score of 99.05%, which can be used for quality screening of massive tongue images.Conclusions: Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and prove that applying deep learning methods, specifically deep CNN, to evaluate bad quality tongue images is feasible.

Predictive Value of Decreasing Antithrombin III in Deep-Vein Thrombosis

1980 ◽  
Vol 44 (03) ◽  
pp. 135-137 ◽  
Author(s):  
Thorkild Lund Andreasen
Keyword(s):  
Deep Vein Thrombosis ◽  
Predictive Value ◽  
Coronary Care Unit ◽  
Antithrombin Iii ◽  
Predictive Values ◽  
Vein Thrombosis ◽  
At Iii ◽  
Coronary Care ◽  
Time Of Admission ◽  
Deep Vein

SummaryAntithrombin III (At-III) was measured at the time of admission and two days later in 131 patients laid up in a coronary care unit. The patients were examined for deep-vein thrombosis (DVT) clinically and by means of 125I-fibrinogen scanning. 19 patients developed DVT. In 11 subjects with and 25 without DVT At-III decreased more than 10%. And in 7 with and 17 without DVT At-III decreased more than 15%. One person with DVT had subnormal At-III. By using decrease of At-III or subnormal initial At-III to predict DVT the following predictive value (PV) were found. Decrease ≤ 10%, PV pos.= 0.32 and PV neg. = 0.93. Decrease ≤ 15%, PV pos. = 0.32 and PV neg. = 0.90. The positive predictive values obtained were too low to let decreasing At-III give occasion for prophylactic anticoagulant treatment.

A Novel Hybrid Image Quality Assessment Algorithm

2011 ◽  
Vol 4 (4) ◽  
pp. 107-108
Author(s):  
Deepa Maria Thomas ◽  
◽  
S. John Livingston
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

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