Algorithm to Develop A Smart System for Detecting Phishing Attack Based on Image Analysis Through Image Quality Parameter and Threshold value

2019 ◽  
Author(s):  
Debarshita Biswas ◽  
Diptendu Bhattacharya ◽  
Parthasarathi De
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Threshold Value ◽  
Quality Parameter ◽  
Smart System

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.

Application of image analysis in activated sludge to evaluate correlations between settleability and features of flocs and filamentous species

2009 ◽  
Vol 59 (10) ◽  
pp. 2029-2036 ◽  
Author(s):  
A. Arelli ◽  
L. Luccarini ◽  
P. Madoni
Keyword(s):  
Image Analysis ◽  
Activated Sludge ◽  
Nutrient Deficiency ◽  
Domestic Wastewater ◽  
Threshold Value ◽  
Pilot Scale ◽  
Volume Index ◽  
Morphological Parameters ◽  
Treatment Processes ◽  
Sludge Properties

Digital image analysis is a useful tool to estimate some morphological parameters of flocs and filamentous species in activated sludge wastewater treatment processes. In this work we found the correlation between some morphological parameters and sludge volume index (SVI). The sludge was taken from a pilot—scale activated sludge plant, owned by ENEA, located side stream to the Trebbo di Reno (Bologna, Italy) municipal WWTP and fed by domestic wastewater. In order to use image analysis, we developed a correct method to acquire digital microbiological observations and to obtain images altogether representative of the sludge properties. We identified and assessed the parameters needed to estimate the settleability of the sludge and evaluated the morphological filamentous features. It is known that several conditions (i.e. low F/M, nutrient deficiency, low dissolved oxygen) select specific filamentous species and their excessive growth decrease floc-forming/filaments ratio, correspond to the worse settleability properties; we found a relationship between the relative abundance of filamentous species and SVI. We also evaluated the fractal dimension parameter (FD) and determined a threshold value useful to distinguish between the “weak” and “firm” floc and we found a correlation between FD and SVI.

scholarly journals Identifying Vegetation in Arid Regions Using Object-Based Image Analysis with RGB-Only Aerial Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2308 ◽  
Author(s):  
Micha Silver ◽  
Arti Tiwari ◽  
Arnon Karnieli
Keyword(s):  
Image Analysis ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Roc Curves ◽  
Threshold Value ◽  
Aerial Photographs ◽  
Object Based Image Analysis ◽  
Object Based ◽  
True Tree ◽  
Vegetation Patches

Vegetation state is usually assessed by calculating vegetation indices (VIs) derived from remote sensing systems where the near infrared (NIR) band is used to enhance the vegetation signal. However VIs are pixel-based and require both visible and NIR bands. Yet, most archived photographs were obtained with cameras that record only the three visible bands. Attempts to construct VIs with the visible bands alone have shown only limited success, especially in drylands. The current study identifies vegetation patches in the hyperarid Israeli desert using only the visible bands from aerial photographs by adapting an alternative geospatial object-based image analysis (GEOBIA) routine, together with recent improvements in preprocessing. The preprocessing step selects a balanced threshold value for image segmentation using unsupervised parameter optimization. Then the images undergo two processes: segmentation and classification. After tallying modeled vegetation patches that overlap true tree locations, both true positive and false positive rates are obtained from the classification and receiver operating characteristic (ROC) curves are plotted. The results show successful identification of vegetation patches in multiple zones from each study area, with area under the ROC curve values between 0.72 and 0.83.

scholarly journals A Semiautomated Method for Measuring Brain Infarct Volume

1990 ◽  
Vol 10 (2) ◽  
pp. 290-293 ◽  
Author(s):  
Raymond A. Swanson ◽  
Matthew T. Morton ◽  
George Tsao-Wu ◽  
Robert A. Savalos ◽  
Charisse Davidson ◽  
...  
Keyword(s):  
Image Analysis ◽  
White Matter ◽  
Gray Matter ◽  
Infarct Volume ◽  
Threshold Value ◽  
Brain Infarct ◽  
Potential Error ◽  
Reproducible Method ◽  
Infarcted Tissue ◽  
Semiautomated Method

An accurate, reproducible method for determining the infarct volumes of gray matter structures is presented for use with presently available image analysis systems. Areas of stained sections with optical densities above that of a threshold value are automatically recognized and measured. This eliminates the potential error and bias inherent in manually delineating infarcted regions. Moreover, the volume of surviving normal gray matter is determined rather than that of the infarct. This approach minimizes the error that is introduced by edema, which distorts and enlarges the infarcted tissue and surrounding white matter.

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.

The Key Technology Research for the Microscopic Particles Image Analysis

2012 ◽  
Vol 461 ◽  
pp. 677-681 ◽  
Author(s):  
Hui Sun ◽  
Yu Zeng Wang ◽  
Ya Lin Li
Keyword(s):  
Image Analysis ◽  
Particle Image ◽  
Threshold Value ◽  
Correction Method ◽  
Background Correction ◽  
Technology Research ◽  
Key Technology ◽  
Ideal Image ◽  
Global Threshold ◽  
Background Brightness

According to the uneven illumination or the very complex background cases, global threshold value method can't correctly to binary particle image; this paper puts forward a kind of background correction method and the OTSU for particle image binary method. It's used background correction method to eliminate the influence of uneven illumination, and the OTSU for binary particle image. Combining with the above methods are tested, and the result shows that, after the background is corrected, we segment the background brightness particle image; the OTSU can obtain ideal image segmentation effect.

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.

Sign in / Sign up

Export Citation Format

Share Document