A Hybrid fuzzy based cross neighbor filtering (HF-CNF) for image enhancement of fine and coarse powder scanned electron microscopy (SEM) images

2021 ◽  
pp. 1-12
Author(s):  
Samuel Manoharan Jayaseelan ◽  
Sakthivel Thirumalai Gopal ◽  
Sangeetha Muthu ◽  
Sivamani Selvaraju ◽  
Md Saad Patel
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Dominant Role ◽  
Random Noise ◽  
Research Paper ◽  
Superior Performance ◽  
Electron Microscopic ◽  
Sem Images ◽  
Coarse Powder ◽  
Processing Application

Image enhancement is one of the most critical stages towards any image processing application. The outcome of image enhancement determines the accuracy and precise nature of the overall output from the image processing under interest. This research paper has shown specific interests towards enhancement of Scanned Electron Microscopic (SEM) images which are primarily concerned with projection of fine details exist in internal details of surfaces, metals, fine powders, fibers etc. These fine details play a dominant role in detection of minute cracks, artifacts, progressing faults, texture of powders, their coarseness or fineness, internal details of fibers in forensics. However, due to the image capturing process which is through conventional camera-based models, noise tends to be a major source in degrading or blurring the underlying vital information. A cross neighbor fuzzy filter is a hybrid combination called Hybrid Fuzzy Based Cross Neighbor Filtering (HF-CNF) which is proposed in this research paper in order to minimize impulse and random noise to a great extent also to fine tune the further processing stages. The proposed method has been subjected to extensive analysis by comparison with state of art and recent benchmark methods and superior performance justified in terms of several validation metrics.

A Novel Approach in Adopting Finite State Automata for Image Processing Applications

2018 ◽  
Vol 8 (1) ◽  
pp. 59-74
Author(s):  
R. Obulakonda Reddy ◽  
Kashyap D. Dhruve ◽  
R. Nagarjuna Reddy ◽  
M. Radha ◽  
N. Sree Vani
Keyword(s):  
Image Processing ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Texture Classification ◽  
Superior Performance ◽  
Finite State Automata ◽  
Processing Application ◽  
Novel Approach ◽  
Comparison Results ◽  
Finite State

This article describes how robust image processing application rely heavily on image descriptors extracted. Limited work is carried out in adopting probabilistic finite state automata (PFSA) models for image processing. A finite state automata for image processing (FSAFIP) method is presented here. Texture classification and content based image retrieval (CBIR) is considered. In FSAFIP, foreground and background regions of an image are identified and later split into patches. Using a tristate PFSA model, feature descriptors corresponding to background/foreground regions are constructed. A distance based large margin nearest neighbor (LMNN) classifier is considered in FSAFIP to impart intelligence. A performance and experimental study to evaluate performance of FSAFIP for CBIR and texture classification is presented. Comparison results in CBIR obtained prove superior performance of FSAFIP over existing methods on Corel-1K dataset. High texture classification accuracy of 99.2% is reported using FSAFIP on KHT-TIPS dataset. An improved texture classification accuracy is achieved using FSAFIP in comparison to former methods.

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

Implications of Digital Image Processing in the Paraclinical Assessment of the Partially Edentated Patient

2018 ◽  
Vol 69 (2) ◽  
pp. 521-524
Author(s):  
Magda Ecaterina Antohe ◽  
Doriana Agop Forna ◽  
Cristina Gena Dascalu ◽  
Norina Consuela Forna
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Treatment Plan ◽  
Digital Processing ◽  
Contour Detection ◽  
Anatomical Structures ◽  
Imaging Processing ◽  
Point To Point ◽  
Radiological Image ◽  
Processing Techniques

The application of certain digital processing techniques offers the possibility of extra accuracy in the interpretation of paraclinical examinations of this type, with profound implications in the diagnosis as well as in the hierarchy of the treatment plan. The purpose of this study is to identify the type of imaging processing for the identification of pathological elements from orthopantomographies and articular tomographies. A number of 20 orthopantomographies and 15 temporo-mandibular joint tomography have undergone through various image enhancement techniques. Various methods of image enhancement (enhancement) have been used for those procedures whereby it becomes more useful in the following aspects: specific details are highlighted; noise is eliminated; the image becomes more visually attractive. The workings were done in Corel PhotoPaint 7.0, using the automatic procedures available.The choice of a particular type of image enhancement technique has been selected for each type of pathology found in orthopantomographies or articular tomography, providing the best accuracy for an optimal imaging interpretation that underpins a precision diagnosis.Of the most useful imaging processing in the optimization of the orthopantomographic image accuracy the point-to-point transformations are to be noted. The image processing proposed in this article focused primarily on improving the radiological image attributes to highlight specific anatomical structures, and secondly, the contour detection, where it was necessary for the diagnostic purposes as well.

DFOG-Image Processing Application for Real-Time Defogging

2020 ◽  
Author(s):  
Indiketiya I.H.O.H ◽  
Kulasekara K.M.R.A ◽  
J.M. Thomas ◽  
Ishara Gamage ◽  
Thusithanjana Thilakarathna
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Application ◽  
Image Processing Application

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.

scholarly journals Image Processing: Capturing Student Attendance Data

2017 ◽  
Vol 16 (7) ◽  
pp. 7002-7009
Author(s):  
Hendra - Kurniawan ◽  
Melda Agarina ◽  
Suhendro Yusuf Irianto
Keyword(s):  
Image Processing ◽  
Learning Process ◽  
Teaching And Learning ◽  
Process Model ◽  
Tertiary Education ◽  
Recognition System ◽  
Student Attendance ◽  
Processing Application ◽  
Student Activities ◽  
Facial Recognition System

Role of the student attendance record is very important in the primary, secondary, and tertiary education. The purpose of this record is monitoring student activity in the teaching and learning process and regarded as one of the important learning assessments. Moreover, a data processing for recording the student attendance is currently done in various ways such as fingerprint, radio frequent identification (RFID), facial recognition system, android-based application, and others. However, many conventional ways (i.e., using paper-based system) are still used, especially in Indonesia. This is because several universities still rarely have enough funds for developing innovative systems. In this research, the image processing application for capturing student attendance data was built. The objective of this research was to provide an efficient alternative to monitor student activities in teaching and learning process. The image processing produced the information related to student attendance by scanning the attendance file through jpg/jpeg using learning vector quantization (LVG) as the process model.

Sign in / Sign up

Export Citation Format

Share Document