Fuzzy Lattice-Based Orthogonal Image Transformation Technique for Natural Image Analysis

2020 ◽  
pp. 295-306
Author(s):  
S. Jagatheswari ◽  
R. Viswanathan
Keyword(s):  
Image Analysis ◽  
Natural Image ◽  
Image Transformation ◽  
Transformation Technique ◽  
Fuzzy Lattice

scholarly journals Review And Analysis Of Computer Vision Algorithms

2021 ◽  
Vol 03 (05) ◽  
pp. 245-250
Author(s):  
Bakhtiyar Saidovich Rakhimov ◽  
◽  
Feroza Bakhtiyarovna Rakhimova ◽  
Sabokhat Kabulovna Sobirova ◽  
Furkat Odilbekovich Kuryazov ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Noise Suppression ◽  
Color Correction ◽  
Scientific Discipline ◽  
Specific Information ◽  
Two Dimensional ◽  
Image Transformation ◽  
Almost All

Computer vision as a scientific discipline refers to the theories and technologies for creating artificial systems that receive information from an image. Despite the fact that this discipline is quite young, its results have penetrated almost all areas of life. Computer vision is closely related to other practical fields like image processing, the input of which is two-dimensional images obtained from a camera or artificially created. This form of image transformation is aimed at noise suppression, filtering, color correction and image analysis, which allows you to directly obtain specific information from the processed image. This information may include searching for objects, keypoints, segments, and annexes;

scholarly journals Image Transformation Technique Using Steganography Methods Using LWT Technique

2019 ◽  
Vol 36 (3) ◽  
pp. 233-237
Author(s):  
Singamaneni Kumar ◽  
Pallela Reddy ◽  
Gajula Ramesh ◽  
Venkata Maddumala
Keyword(s):  
Image Transformation ◽  
Transformation Technique

scholarly journals Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

2016 ◽  
Author(s):  
Wentao Zhu ◽  
Qi Lou ◽  
Yeeleng Scott Vang ◽  
Xiaohui Xie
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Computational Models ◽  
Training Data ◽  
Natural Image ◽  
Great Effort ◽  
Computer Aided ◽  
Deep Networks ◽  
Mass Classification ◽  
Aided Diagnosis

AbstractMammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multiinstance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.

Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

1977 ◽  
Vol 35 ◽  
pp. 220-221
Author(s):  
S.F. Stinson ◽  
J.C. Lilga ◽  
M.B. Sporn
Keyword(s):  
Image Analysis ◽  
Pattern Analysis ◽  
Relative Proportion ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Cross Sectional ◽  
Exfoliated Cells ◽  
Neoplastic Lesions ◽  
Analysis System ◽  
Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

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.

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