QUALITY ASSESSMENT IN COLONOSCOPY - New Challenges Through Computer Vision-based Systems

2009 ◽  
Keyword(s):  
Computer Vision ◽  
Quality Assessment ◽  
New Challenges

scholarly journals QUALITY ASSESSMENT OF BISCUITS USING COMPUTER VISION

2016 ◽  
Vol 7 (1) ◽  
pp. 1285-1289 ◽  
Author(s):  
Archana A. Bade ◽  
◽  
Manisha P. Dale ◽  
Jemshid K K ◽  
◽  
...  
Keyword(s):  
Computer Vision ◽  
Quality Assessment

scholarly journals Quality assessment for register-based statistics - Results for the Austrian census 2011

2016 ◽  
Vol 45 (2) ◽  
pp. 3-14 ◽  
Author(s):  
Eva-Maria Asamer ◽  
Franz Astleithner ◽  
Predrag Cetkovic ◽  
Stefan Humer ◽  
Manuela Lenk ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Quality Assessment ◽  
Quality Indicator ◽  
Quality Evaluation ◽  
External Source ◽  
Quality Framework ◽  
New Challenges ◽  
Available Information

In 2011, Statistics Austria carried out the first register-based census. The use of administrative data for statistical purposes is accompanied by various advantages like a reduced burden for the respondents and less costs for the NSI. However, new challenges, like the quality assessment of this kind of data, arise. Therefore, Statistics Austria developed a comprehensive standardized framework for the evaluation of the data quality for registerbased statistics.In this paper, we present the principle of the quality framework and detailed results from the quality evaluation of the 2011 Austrian census. For each attribute in the census a quality measure is derived from four hyperdimensions. The first three hyperdimensions focus on the documentation of data, the usability of the records and the comparison of data to an external source. The fourth hyperdimension assesses the quality of the imputations. In the framework all the available information on each attribute can be combined to form one final quality indicator. This procedure allows to track changes in quality during data processing and to compare the quality of different census generations.

scholarly journals Quality assessment of coffee beans through computer vision and machine learning algorithms

2020 ◽  
Vol 15 ◽  
pp. 1-9
Author(s):  
Fernando Ferreira Lima dos Santos ◽  
Jorge Rosas ◽  
Rodrigo Martins ◽  
Guilherme Araújo ◽  
Lucas Viana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Quality Assessment ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Coffee Beans

scholarly journals Deep Learning in Image Quality Assessment: Past, Present, and What Lies Ahead

2021 ◽  
Vol 2021 (1) ◽  
pp. 1-4
Author(s):  
Seyed Ali Amirshahi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Machine Learning Techniques ◽  
Short Paper ◽  
Subjective Quality Assessment ◽  
Learning Techniques ◽  
Objective Quality

Quality assessment of images plays an important role in different applications in image processing and computer vision. While subjective quality assessment of images is the most accurate approach due to issues objective quality metrics have been the go to approach. Until recently most such metrics have taken advantage of different handcrafted features. Similar (but with a slower speed) to other applications in image processing and computer vision, different machine learning techniques, more specifically Convolutional Neural Networks (CNNs) have been introduced in different tasks related to image quality assessment. In this short paper which is a supplement to a focal talk given with the same title at the London Imaging Meeting (LIM) 2021 we aim to provide a short timeline on how CNNs have been used in the field of image quality assessment so far, how the field could take advantage of CNNs to evaluate the image quality, and what we expect will happen in the near future.

scholarly journals On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

2018 ◽  
Vol 4 (10) ◽  
pp. 114 ◽  
Author(s):  
Pedro Garcia Freitas ◽  
Luísa da Eira ◽  
Samuel Santos ◽  
Mylene Farias
Keyword(s):  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Local Binary Patterns ◽  
Reference Image ◽  
Fundamental Interest ◽  
Wide Range ◽  
Estimate Quality

Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.

Sign in / Sign up

Export Citation Format

Share Document