Basic Image Processing and Linear Operators

2004 ◽  
pp. 19-45 ◽  
Author(s):  
George Stetten ◽  
Bill Lorensen
Keyword(s):  
Image Processing ◽  
Linear Operators ◽  
Basic Image

A μc-Si:H P-I-N Imager For 2-D Pattern Recognition

1999 ◽  
Vol 558 ◽  
Author(s):  
J. Martins ◽  
M. Fernandes ◽  
F. Sousa ◽  
P. Louro ◽  
A. MaçArico ◽  
...  
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Enhancement ◽  
Image Representation ◽  
Rectangular Grid ◽  
Sensing Element ◽  
Coplanar Electrodes ◽  
Basic Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms

ABSTRACTA TCO/ μc-p-i-n Si:H/AI imager is presented and analyzed. The μc-p-i-n Si:H photodiode acts as a sensing element. Contacts are used as an electrical interface. The image is acquired by a scan-out process. Sampling is performed on a rectangular grid, and the read-out of the photogenerated charges is achieved by measuring simultaneously both transverse photovoltages at the coplanar electrodes. The image representation in gray-tones is obtained by using low level processing algorithms. Basic image processing algorithms are developed for image enhancement and restoration.

Detecting Thread Features Based on Digital Image Processing Techniques

2014 ◽  
Vol 889-890 ◽  
pp. 1107-1110
Author(s):  
Han Ming Cai ◽  
Pei Yao Wang ◽  
Xiao Mei Song
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Measurement Accuracy ◽  
Measuring Method ◽  
Image Processing Techniques ◽  
Basic Image ◽  
The Cost ◽  
Processing Techniques ◽  
Gauge Measurement

Thread features of the traditional measuring method mainly adopts working gauge measurement, due to limitations in the traditional thread features measurement accuracy is relatively low, the efficiency is low, the cost is high. The thread features detection method based on digital image processing techniques using CCD to obtain basic image of thread, processing the thread image, extracting thread outline, calculating thread features through the computer, improves the efficiency, saves the cost.

scholarly journals New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Safia Abdelmounaime ◽  
He Dong-Chen
Keyword(s):  
Image Processing ◽  
Texture Analysis ◽  
Image Databases ◽  
Spectral Information ◽  
Spectral Content ◽  
New Class ◽  
Texture Information ◽  
Basic Image ◽  
Informative Content ◽  
Texture Characterization

Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.

Proposal of a Method to Extract Arbitrary FiguresUsing One-Dimensional Histograms

2009 ◽  
Vol 13 (4) ◽  
pp. 380-385 ◽  
Author(s):  
Shota Nakashima ◽  
◽  
Makoto Miyauchi ◽  
Seiichi Serikawa ◽  
Keyword(s):  
Image Processing ◽  
Hough Transform ◽  
Processing Time ◽  
Memory Space ◽  
One Dimensional ◽  
Processing Problem ◽  
Generalized Hough Transform ◽  
Aspect Ratios ◽  
Optimum Parameters ◽  
Basic Image

Arbitrary figure extraction, a basic image processing problem, is done typically using the generalized Hough transform (GHT). GHT and its successors tend, however, to consume humongous amounts of processing time and memory space. The arbitrary figure extraction we propose using one-dimensional histograms takes advantage of the Polytope method, which features: (1) The histogram distribution changes if parameters representing figures change. (2) Optimum parameters are obtained, if the value of the highest-frequency histogram becomes maximum. This approach makes memory space very small, processing time very short, effective by extracts arbitrary curves with different aspect ratios, and the algorithm is simple.

scholarly journals Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang Eigen

2008 ◽  
Vol 2 (1) ◽  
pp. 69
Author(s):  
Is Mardianto ◽  
Dian Pratiwi
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Reduction Process ◽  
X Ray ◽  
Important Processing ◽  
Basic Image ◽  
Artificial Neural ◽  
Principal Componentanalysis ◽  
Learning Data

There are various ways to detect osteoporosis disease (bone loss). One of them is by observing the osteoporosisimage through rontgen picture or X-ray. Then, it is analyzed manually by Rheumatology experts. Article present the creationof a system which could detect osteoporosis disease on human, by implementing the Rheumatology principles. The main areasidentified were between wrist and hand fingers. The working system in this software included 3 important processing, whichwere process of basic image processing, pixel reduction process, pixel reduction, and artificial neural networks. Initially, thecolor of digital X-ray image (30 x 30 pixels) was converted from RGB to grayscale. Then, it was threshold and its gray levelvalue was taken. These values then were normalized to an interval [0.1, 0.9], then reduced using a PCA (Principal ComponentAnalysis) method. The results were used as input on the process of Backpropagation artificial neural networks to detect thedisease analysis of X-ray being inputted. It can be concluded that from the testing result, with a learning rate of 0.7 andmomentum of 0.4, this system had a success rate of 73 to 100 percent for the non-learning data testing, and 100 percent forlearning data.Keywords: osteoporosis, image processing, PCA, artificial neural networks

scholarly journals Identification of Tumour from Ct Images using Contour Plot and Gui

2019 ◽  
Vol 8 (4) ◽  
pp. 4306-4309
Keyword(s):  
Image Processing ◽  
Healthcare Sector ◽  
Contour Plot ◽  
Age Group ◽  
Image Processing Techniques ◽  
Basic Image ◽  
Processing Techniques ◽  
Human Eyes ◽  
Different Levels

The healthcare sector in terms of medical imaging is picking up significance with people preferring automation which is eventually fast and effective in determination of the problem which can give understanding to the picture way superior than to the human eyes. Brain tumour is a condition where it ranks second in terms of cancer related deaths for men and ranks at fifth place for women over the age group of 20 to 39.Brain tumours are extremely agonizing and it ends up being significant reasons for various ailments if not cured properly. Analysis of the tumour and its type is a very significant part in its treatment. Tumours are of two types benign and malignant, Distinguishing the type of tumour place an important role in its treatment .The principal reason for the rise in the number of malignancy patients is due to numbness towards its treatment at early stages. The whole idea of this paper is to create an algorithm that could educate the patient about the tumour with the help of image processing techniques. The basic image processing techniques are used to obtain the background by the sharpening the image, reduction of noise together with morphological functions such as erosion and dilation. To obtain the tumour images we are intended to subtract the background of the image and their negatives from the various set of images. Plotting contour and c-label of the tumour and its boundary provides us with information related to the tumour that can help in a better visualization in diagnosing cases. This procedure helps in recognizing the size, shape and location of the tumour. This in turn helps the doctors as well as the patient to comprehend the complexity of tumour with colour labelling for different levels of elevation. A graphical user interface would help the medicinal staff to access the reports and also find the background and contour plot of tumour within their finger tips.

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