Directional Hinge Features for Writer Identification: The Importance of the Skeleton and the Effects of Character Size and Pixel Intensity

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Paraskevas Diamantatos ◽  
Ergina Kavallieratou ◽  
Stefanos Gritzalis
Keyword(s):  
Writer Identification ◽  
Pixel Intensity ◽  
Character Size

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

A Set of Features for Text-Independent Writer Identification

2016 ◽  
Vol 11 (10) ◽  
pp. 898 ◽  
Author(s):  
Tayeb Bahram ◽  
Abdelkader Benyettou ◽  
Djelloul Ziadi
Keyword(s):  
Writer Identification

scholarly journals Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristi Powers ◽  
Raymond Chang ◽  
Justin Torello ◽  
Rhonda Silva ◽  
Yannick Cadoret ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Analysis ◽  
Structure And Function ◽  
Ultrasound Images ◽  
Cardiac Structure ◽  
Short Axis ◽  
Pixel Intensity ◽  
Cardiac Structure And Function ◽  
And Function

AbstractEchocardiography is a widely used and clinically translatable imaging modality for the evaluation of cardiac structure and function in preclinical drug discovery and development. Echocardiograms are among the first in vivo diagnostic tools utilized to evaluate the heart due to its relatively low cost, high throughput acquisition, and non-invasive nature; however lengthy manual image analysis, intra- and inter-operator variability, and subjective image analysis presents a challenge for reproducible data generation in preclinical research. To combat the image-processing bottleneck and address both variability and reproducibly challenges, we developed a semi-automated analysis algorithm workflow to analyze long- and short-axis murine left ventricle (LV) ultrasound images. The long-axis B-mode algorithm executes a script protocol that is trained using a reference library of 322 manually segmented LV ultrasound images. The short-axis script was engineered to analyze M-mode ultrasound images in a semi-automated fashion using a pixel intensity evaluation approach, allowing analysts to place two seed-points to triangulate the local maxima of LV wall boundary annotations. Blinded operator evaluation of the semi-automated analysis tool was performed and compared to the current manual segmentation methodology for testing inter- and intra-operator reproducibility at baseline and after a pharmacologic challenge. Comparisons between manual and semi-automatic derivation of LV ejection fraction resulted in a relative difference of 1% for long-axis (B-mode) images and 2.7% for short-axis (M-mode) images. Our semi-automatic workflow approach reduces image analysis time and subjective bias, as well as decreases inter- and intra-operator variability, thereby enhancing throughput and improving data quality for pre-clinical in vivo studies that incorporate cardiac structure and function endpoints.

scholarly journals Defining indocyanine green fluorescence to assess anastomotic perfusion during gastrointestinal surgery: systematic review

BJS Open ◽  
2021 ◽  
Vol 5 (2) ◽  
Author(s):  
M D Slooter ◽  
M S E Mansvelders ◽  
P R Bloemen ◽  
S S Gisbertz ◽  
W A Bemelman ◽  
...  
Keyword(s):  
Systematic Review ◽  
Indocyanine Green ◽  
Patient Outcomes ◽  
Gastrointestinal Surgery ◽  
Fluorescence Angiography ◽  
Future Research ◽  
Fluorescence Signal ◽  
Pixel Intensity ◽  
Indocyanine Green Fluorescence ◽  
Quantitative Parameters

Abstract Background The aim of this systematic review was to identify all methods to quantify intraoperative fluorescence angiography (FA) of the gastrointestinal anastomosis, and to find potential thresholds to predict patient outcomes, including anastomotic leakage and necrosis. Methods This systematic review adhered to the PRISMA guidelines. A PubMed and Embase literature search was performed. Articles were included when FA with indocyanine green was performed to assess gastrointestinal perfusion in human or animals, and the fluorescence signal was analysed using quantitative parameters. A parameter was defined as quantitative when a diagnostic numeral threshold for patient outcomes could potentially be produced. Results Some 1317 articles were identified, of which 23 were included. Fourteen studies were done in patients and nine in animals. Eight studies applied FA during upper and 15 during lower gastrointestinal surgery. The quantitative parameters were divided into four categories: time to fluorescence (20 studies); contrast-to-background ratio (3); pixel intensity (2); and numeric classification score (2). The first category was subdivided into manually assessed time (7 studies) and software-derived fluorescence–time curves (13). Cut-off values were derived for manually assessed time (speed in gastric conduit wall) and derivatives of the fluorescence–time curves (Fmax, T1/2, TR and slope) to predict patient outcomes. Conclusion Time to fluorescence seems the most promising category for quantitation of FA. Future research might focus on fluorescence–time curves, as many different parameters can be derived and the fluorescence intensity can be bypassed. However, consensus on study set-up, calibration of fluorescence imaging systems, and validation of software programs is mandatory to allow future data comparison.

Writer identification system for pre-segmented offline handwritten Devanagari characters using k-NN and SVM

2019 ◽  
Vol 24 (13) ◽  
pp. 10111-10122 ◽  
Author(s):  
Shaveta Dargan ◽  
Munish Kumar ◽  
Anupam Garg ◽  
Kutub Thakur
Keyword(s):  
Identification System ◽  
Writer Identification

scholarly journals Fuzzy enhancement and deep hash layer based neural network to detect Covid-19

2021 ◽  
pp. 1-11
Author(s):  
Amita Nandal ◽  
Marija Blagojevic ◽  
Danijela Milosevic ◽  
Arvind Dhaka ◽  
Lakshmi Narayan Mishra
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Performance Metrics ◽  
Regularization Parameter ◽  
Fuzzy Membership Function ◽  
Kappa Statistics ◽  
Pixel Intensity ◽  
Testing Sample ◽  
X Ray ◽  
Conventional Methods

This paper proposes a deep learning framework for Covid-19 detection by using chest X-ray images. The proposed method first enhances the image by using fuzzy logic which improvises the pixel intensity and suppresses background noise. This improvement enhances the X-ray image quality which is generally not performed in conventional methods. The pre-processing image enhancement is achieved by modeling the fuzzy membership function in terms of intensity and noise threshold. After this enhancement we use a block based method which divides the image into smooth and detailed regions which forms a feature set for feature extraction. After feature extraction we insert a hashing layer after fully connected layer in the neural network. This hash layer is advantageous in terms of improving the overall accuracy by computing the feature distances effectively. We have used a regularization parameter which minimizes the feature distance between similar samples and maximizes the feature distance between dissimilar samples. Finally, classification is done for detection of Covid-19 infection. The simulation results present a comparison of proposed model with existing methods in terms of some well-known performance indices. Various performance metrics have been analysed such as Overall Accuracy, F-measure, specificity, sensitivity and kappa statistics with values 93.53%, 93.23%, 92.74%, 92.02% and 88.70% respectively for 20:80 training to testing sample ratios; 93.84%, 93.53%, 93.04%, 92.33%, and 91.01% respectively for 50:50 training to testing sample ratios; 95.68%, 95.37%, 94.87%, 94.14%, and 90.74% respectively for 80:20 training to testing sample ratios have been obtained using proposed method and it is observed that the results using proposed method are promising as compared to the conventional methods.

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