Assessment Of Image Quality Of Photographic Lenses By Edge Image Analysis

1984 ◽  
Author(s):  
Harald Thomas
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Edge Image

scholarly journals Challenges in Curating 2D Multimedia Data in the Application of Machine Learning in Biodiversity Image Analysis

2021 ◽  
Vol 5 ◽  
Author(s):  
Yasin Bakış ◽  
Xiaojun Wang ◽  
Hank Bart
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Image Quality ◽  
Multimedia Data ◽  
Computational Techniques ◽  
Search Results ◽  
Quality Issues

Over 1 billion biodiversity collection specimens ranging from fungi to fish to fossils are housed in more than 1,600 natural history collections across the United States. The digitization of these specimens has risen significantly within the last few decades and this is only likely to increase, as the use of digitized data gains more importance every day. Numerous experiments with automated image analysis have proven the practicality and usefulness of digitized biodiversity images by computational techniques such as neural networks and image processing. However, most of the computational techniques to analyze images of biodiversity collection specimens require a good curation of this data. One of the challenges in curating multimedia data of biodiversity collection specimens is the quality of the multimedia objects—in our case, two dimensional images. To tackle the image quality problem, multimedia needs to be captured in a specific format and presented with appropriate descriptors. In this study we present an analysis of two image repositories each consisting of 2D images of fish specimens from several institutions—the Integrated Digitized Biocollections (iDigBio) and the Great Lakes Invasives Network (GLIN). Approximately 70 thousand images have been processed from the GLIN repository and 450 thousand images have been processed from the iDigBio repository and their suitability assessed for use in neural network-based species identification and trait extraction applications. Our findings showed that images that came from the GLIN dataset were more successful for image processing and machine learning purposes. Almost 40% of the species have been represented with less than 10 images while only 20% have more than 100 images per species. We identified and captured 20 metadata descriptors that define quality and usability of the image. According to the captured metadata information, 70% of the GLIN dataset images were found to be useful for further analysis according to the overall image quality score. Quality issues with the remaining images included: curved specimens, non-fish objects in the images such as tags, labels and rocks that obstructed the view of the specimen; color, focus and brightness issues; folded or overlapping parts as well as missing parts. We used both the web interface and the API (Application Programming Interface) for downloading images from iDigBio. We searched for all fish genera, families and classes in three different searches with the images-only option selected. Then we combined all of the search results and removed duplicates. Our search on the iDigBio database for fish taxa returned approximately 450 thousand records with images. We narrowed this down to 90 thousand fish images aided by the multimedia metadata with the downloaded search results, excluding some non-fish images, fossil samples, X-ray and CT (computed tomography) scans and several others. Only 44% of these 90 thousand images were found to be suitable for further analysis. In this study, we discovered some of the limitations of biodiversity image datasets and built an infrastructure for assessing the quality of biodiversity images for neural network analysis. Our experience with the fish images gathered from two different image repositories has enabled describing image quality metadata features. With the help of these metadata descriptors, one can simply create a dataset for a desired image quality for the purpose of analysis. Likewise, the availability of the metadata descriptors will help advance our understanding of quality issues, while helping data technicians, curators and the other digitization staff be more aware of multimedia.

Image Quality in Chest Radiography: Abstract

2003 ◽  
Vol 3 (2) ◽  
pp. 13-13 ◽  
Author(s):  
C. Vyborny ◽  
P. Bunch ◽  
H. Chotas ◽  
J. Dobbins ◽  
L. Niklason ◽  
...  
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Chest Radiography ◽  
Careful Consideration ◽  
Extensive Literature ◽  
Radiographic Images ◽  
Radiographic Technique ◽  
Chest Disease ◽  
Complex Subject

Image quality in chest radiography is an important, but complex, subject. The complicated anatomy of the chest, as well as the various ways that chest disease may manifest itself, require careful consideration of radiographic technique. The manner in which human observers deal with the complexity of chest images adds further dimensions to image analysis that are not found in other radiography examinations. This report describes many issues that are related to the quality of chest radiographic images. In so doing, it relies upon the very extensive literature on this topic, a topic that has been one of the most thoroughly studied in all of radiography. Strategies that are generally agreed to improve the quality of chest radiographs are described, as are approaches to the assessment of image quality.

scholarly journals THE ANALYSIS OF THE IMAGE QUALITY OF CHEST RADIOGRAPHS : THE IMAGE ANALYSIS FOR THE PRACTICAL DENSITY REGION

1988 ◽  
Vol 44 (1) ◽  
pp. 10-17 ◽  
Author(s):  
HIROHARU OKADA ◽  
KIMIO USAMI ◽  
YUICHI KURAMATA ◽  
KAEKO MIYATA ◽  
YOSHIHIKO MURANO ◽  
...  
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Chest Radiographs ◽  
Density Region

Progress In The Practical Application Of Automated Image Analysis To Tem Negatives

1977 ◽  
Vol 35 ◽  
pp. 214-217
Author(s):  
F. A. Heckman ◽  
E. Redman ◽  
J.E. Connolly
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Exposure Time ◽  
Image Contrast ◽  
Automated Image Analysis ◽  
Practical Application ◽  
Factors Affecting ◽  
High Image Quality ◽  
Qualitative Evidence ◽  
Comprehensive Study

In our initial publication on this subject1) we reported results demonstrating that contrast is the most important factor in producing the high image quality required for reliable image analysis. We also listed the factors which enhance contrast in order of the experimentally determined magnitude of their effect. The two most powerful factors affecting image contrast attainable with sheet film are beam intensity and KV. At that time we had only qualitative evidence for the ranking of enhancing factors. Later we carried out the densitometric measurements which led to the results outlined below.Meaningful evaluations of the cause-effect relationships among the considerable number of variables in preparing EM negatives depend on doing things in a systematic way, varying only one parameter at a time. Unless otherwise noted, we adhered to the following procedure evolved during our comprehensive study:Philips EM-300; 30μ objective aperature; magnification 7000- 12000X, exposure time 1 second, anti-contamination device operating.

Observability of Very Thick Specimen by Using Transmission Scanning Electron Microscope

1971 ◽  
Vol 29 ◽  
pp. 26-27
Author(s):  
K. Shibatomi ◽  
T. Yamanoto ◽  
H. Koike
Keyword(s):  
Electron Microscope ◽  
Image Quality ◽  
Scanning Electron Microscope ◽  
Chromatic Aberration ◽  
Image Contrast ◽  
Objective Lens ◽  
Thick Specimen ◽  
Transmission Electron ◽  
Scanning Electron

In the observation of a thick specimen by means of a transmission electron microscope, the intensity of electrons passing through the objective lens aperture is greatly reduced. So that the image is almost invisible. In addition to this fact, it have been reported that a chromatic aberration causes the deterioration of the image contrast rather than that of the resolution. The scanning electron microscope is, however, capable of electrically amplifying the signal of the decreasing intensity, and also free from a chromatic aberration so that the deterioration of the image contrast due to the aberration can be prevented. The electrical improvement of the image quality can be carried out by using the fascionating features of the SEM, that is, the amplification of a weak in-put signal forming the image and the descriminating action of the heigh level signal of the background. This paper reports some of the experimental results about the thickness dependence of the observability and quality of the image in the case of the transmission SEM.

Organ doses and subjective image quality of indirect digital panoramic radiography

2001 ◽  
Vol 30 (6) ◽  
pp. 308-313 ◽  
Author(s):  
F Gijbels ◽  
G Sanderink ◽  
C Bou Serhal ◽  
H Pauwels ◽  
R Jacobs
Keyword(s):  
Image Quality ◽  
Panoramic Radiography ◽  
Organ Doses ◽  
Subjective Image Quality ◽  
Digital Panoramic Radiography

An image analysis based expert system for assessing the quality of freeze-dried protein formulations

2012 ◽  
Author(s):  
Jukka Rantanen ◽  
Hjalte Trnka ◽  
Jian Wu ◽  
Marco van de Weert ◽  
Holger Grohganz
Keyword(s):  
Image Analysis ◽  
Expert System ◽  
Protein Formulations ◽  
Freeze Dried
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