scholarly journals Study of Feature Extraction of Retinal Scans

2019 ◽  
Vol 24 (1) ◽  
pp. 5-13
Author(s):  
Mohanad Abdulhamid ◽  
Gitonga Muthomi
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Image Enhancement ◽  
Computer System ◽  
Image Acquisition ◽  
Poor Quality ◽  
Design System ◽  
Security Systems ◽  
Matching Process ◽  
High Resolution Images

Abstract In this paper, the retina is discussed as part of the feature of extraction of retinal scans for use in security systems as a means of identification. The design system contains a method of image acquisition and processing of the image. A computer system is also incorporated for matching and verifying the image captured to an already present representation of unique features of the retina that are stored as templates for matching and identification. It should then either allow or deny the user depending on the results of the matching process. This paper shows the development of the step undertaken to process the image to the extraction of the features. The high resolution images are taken through a series of image enhancement process before feature extraction technics are applied and before templates are created for future referencing. The main limitation of this process is attributed to capturing the image from the retina. The image obtained may be of poor quality thus making the unique features of the retina unclear.

Transputer Based Parallel Processing Applied to Machine Vision Inspection

1991 ◽  
Author(s):  
Richard A. Carey ◽  
Wayne D. R. Daley ◽  
Jon S. Lindberg
Keyword(s):  
Feature Extraction ◽  
Machine Vision ◽  
Real Time ◽  
Image Enhancement ◽  
Image Acquisition ◽  
Image Interpretation ◽  
Real Time Processing ◽  
Flexible System ◽  
Vision Inspection ◽  
Machine Vision Inspection

Abstract The use of Machine vision systems has become more widespread in manufacturing processes for the purposes of quality control inspection, and product identification and sorting. Typical Machine Vision applications need to run in real time (30 frames per second), and as a result most of the existing systems are built from hardware to meet this speed requirement. There is currently no single processor that is reasonably priced and fast enough to provide real time performance on Machine Vision applications. This paper describes a Transputer based system that employs different architectures and algorithms to achieve real time processing speeds for some Machine Vision applications. The paper discusses the differences between sequential and parallel architectures, and the way the unique abilities of the Transputers are utilized to create a flexible system that provides the best performance for a variety of applications. The areas of Machine Vision discussed are Image Acquisition, Image Enhancement, Feature Extraction and Image Interpretation. Image Acquisition and interpretation are discussed briefly, with an in depth discussion of the algorithms and architecture needed to optimize Image Enhancement and Feature Extraction on a Transputer based system.

scholarly journals IMAGE RECONSTRUCTION WITH IMPROVED SUPER-RESOLUTION ALGORITHM

2004 ◽  
Vol 18 (08) ◽  
pp. 1513-1527
Author(s):  
CHIEN-YU CHEN ◽  
YU-CHUAN KUO ◽  
CHIOU-SHANN FUH
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Image Registration ◽  
Iterative Method ◽  
Image Enhancement ◽  
Super Resolution ◽  
Post Processing ◽  
High Resolution Images ◽  
Robust Image ◽  
Resolution Algorithm

In this paper we propose a technique that reconstructs high-resolution images with improved super-resolution algorithms, based on Irani and Peleg iterative method, and employs our suggested initial interpolation, robust image registration, automatic image selection and image enhancement post-processing. When the target of reconstruction is a moving object with respect to a stationary camera, high-resolution images can still be reconstructed, whereas previous systems only work well when we move the camera and the displacement of the whole scene is the same.

scholarly journals AN EFFICIENT METHOD TO FEED HIGH RESOLUTION IMAGES TO FACIAL ANALYSIS SYSTEMS

2013 ◽  
pp. 68-71
Author(s):  
ROOPA R ◽  
MRS. VANI.K. S ◽  
MRS. NAGAVENI. V
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
High Resolution ◽  
Super Resolution ◽  
Poor Quality ◽  
Low Resolution ◽  
Resolution Method ◽  
Facial Analysis ◽  
Face Images ◽  
High Resolution Images

Image Processing is any form of signal processing for which the image is an input such as a photograph or video frame. The output of image processing may be either an image or a set of characteristics or parameters related to the image. In many facial analysis systems like Face Recognition face is used as an important biometric. Facial analysis systems need High Resolution images for their processing. The video obtained from inexpensive surveillance cameras are of poor quality. Processing of poor quality images leads to unexpected results. To detect face images from a video captured by inexpensive surveillance cameras, we will use AdaBoost algorithm. If we feed those detected face images having low resolution and low quality to face recognition systems they will produce some unstable and erroneous results. Because these systems have problem working with low resolution images. Hence we need a method to bridge the gap between on one hand low- resolution and low-quality images and on the other hand facial analysis systems. Our approach is to use a Reconstruction Based Super Resolution method. In Reconstruction Based Super Resolution method we will generate a face-log containing images of similar frontal faces of the highest possible quality using head pose estimation technique. Then, we use a Learning Based Super-Resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. Hence the total system quality factor will be improved by four.

scholarly journals K-MEANS CLUSTERING FOR EGG EMBRYO'S DETECTION BASED-ON STATISTICAL FEATURE EXTRACTION APPROACH OF CANDLING EGGS IMAGE

SINERGI ◽  
2020 ◽  
Vol 25 (1) ◽  
pp. 43
Author(s):  
Shoffan Saifullah
Keyword(s):  
Feature Extraction ◽  
Image Enhancement ◽  
Test Data ◽  
Euclidean Distance ◽  
Image Acquisition ◽  
Image Data ◽  
Gray Level ◽  
Clustering Method ◽  
Statistical Feature ◽  
Occurrence Matrix

This research discusses the detection of embryonic eggs using the k-means clustering method based on statistical feature extraction. The processes that occur in detection are image acquisition, image enhancement, feature extraction, and identification/detection. The data used consisted of 200 egg image data, consisting of 100 test data and 100 new test data. The acquisition process uses a smartphone camera by capturing candled egg objects. The results of image acquisition become a reference in the process of image enhancement and feature extraction using Statistical Feature Extraction. The statistical feature extraction applied is the Gray Level Co-occurrence Matrix (GLCM) method, which consists of 6 features, namely Energy, Contrast, Entropy, Variance, Correlation, and Homogeneity. The results of feature extraction (6 features) are grouped by the K-means Clustering method. The clustering process uses Euclidean distance calculations to determine the proximity of features. The results of grouping and testing give the best average results with an accuracy of ≈ 74% from several test samples.

scholarly journals Image Enhancement by Histogram Specification Using Multiple Target Images

2011 ◽  
pp. 29-37
Author(s):  
Pavithra P ◽  
Ramyashree N ◽  
Shruthi T.V ◽  
Dr. Jharna Majumdar
Keyword(s):  
High Resolution ◽  
Image Enhancement ◽  
Input Image ◽  
Multiple Target ◽  
Target Image ◽  
Image Histogram ◽  
Histogram Specification ◽  
High Resolution Images

Shape and characteristics of the histogram plays a major role in finding the quality of an image. Histogram Specification is an image enhancement technique, where the histogram of the input image is transformed to a pre-specified histogram derived from a high resolution image, called target image. In this paper, the classical histogram specification technique is extended by using a target image which is obtained by fusing multiple high resolution images. A set of Quality Metrics were identified to assess the quality of the output enhanced image. The paper addresses the following issues: a) Effect of varying the number of target images on the quality of the output enhanced image b) Role of using different methods of fusion on the quality of the output enhanced image c) Category of the target image on the quality of the output enhanced image. If the input image is from a forest, whether in order to obtain an enhanced image, all target images has to be selected from the forest category d) Effect of preprocessing of target image on the quality of the output enhanced image.

scholarly journals Resolution-invariant Person Re-Identification

2019 ◽  
Author(s):  
Shunan Mao ◽  
Shiliang Zhang ◽  
Ming Yang
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Large Range ◽  
State Of The Art ◽  
Super Resolution ◽  
The State ◽  
Invariant Representation ◽  
Invariant Feature ◽  
Feature Extractor ◽  
High Resolution Images

Exploiting resolution invariant representation is critical for person Re-Identification (ReID) in real applications, where the resolutions of captured person images may vary dramatically. This paper learns person representations robust to resolution variance through jointly training a Foreground-Focus Super-Resolution (FFSR) module and a Resolution-Invariant Feature Extractor (RIFE) by end-to-end CNN learning. FFSR upscales the person foreground using a fully convolutional auto-encoder with skip connections learned with a foreground focus training loss. RIFE adopts two feature extraction streams weighted by a dual-attention block to learn features for low and high resolution images, respectively. These two complementary modules are jointly trained, leading to a strong resolution invariant representation. We evaluate our methods on five datasets containing person images at a large range of resolutions, where our methods show substantial superiority to existing solutions. For instance, we achieve Rank-1 accuracy of 36.4% and 73.3% on CAVIAR and MLR-CUHK03, outperforming the state-of-the art by 2.9% and 2.6%, respectively.

FeXIV Line Emission Polarization of the July 11, 1991 Solar Corona

1994 ◽  
Vol 144 ◽  
pp. 541-547
Author(s):  
J. Sýkora ◽  
J. Rybák ◽  
P. Ambrož
Keyword(s):  
High Resolution ◽  
Solar Corona ◽  
Emission Line ◽  
Solar Eclipse ◽  
White Light ◽  
Preliminary Analysis ◽  
Line Emission ◽  
Total Solar Eclipse ◽  
Degree Of Polarization ◽  
High Resolution Images

AbstractHigh resolution images, obtained during July 11, 1991 total solar eclipse, allowed us to estimate the degree of solar corona polarization in the light of FeXIV 530.3 nm emission line and in the white light, as well. Very preliminary analysis reveals remarkable differences in the degree of polarization for both sets of data, particularly as for level of polarization and its distribution around the Sun’s limb.

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