scholarly journals A Parametric Logarithmic Image Processing Framework Based on Fuzzy Graylevel Accumulation by the Hamacher T-Conorm

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4857
Author(s):  
Constantin Vertan ◽  
Corneliu Florea ◽  
Laura Florea
Keyword(s):  
Image Processing ◽  
Real World ◽  
Digital Images ◽  
Gray Level ◽  
Accumulation Process ◽  
Fuzzy Modelling ◽  
Parameterized Family ◽  
Logarithmic Image Processing ◽  
Suitable Framework ◽  
Processing Framework

It has been proven that Logarithmic Image Processing (LIP) models provide a suitable framework for visualizing and enhancing digital images acquired by various sources. The most visible (although simplified) result of using such a model is that LIP allows the computation of graylevel addition, subtraction and multiplication with scalars within a fixed graylevel range without the use of clipping. It is claimed that a generalized LIP framework (i.e., a parameterized family of LIP models) can be constructed on the basis of the fuzzy modelling of gray level addition as an accumulation process described by the Hamacher conorm. All the existing LIP and LIP-like models are obtained as particular cases of the proposed framework in the range corresponding to real-world digital images.

scholarly journals CHARACTERIZATION OF DIESEL SPRAY IMAGES USING THE LOGARITHMIC IMAGE PROCESSING FRAMEWORK

2011 ◽  
Vol 26 (3) ◽  
pp. 145 ◽  
Author(s):  
Cecile Petit ◽  
Michel Jourlin ◽  
Wolfgang Reckers
Keyword(s):  
Image Processing ◽  
Monochromatic Light ◽  
Internal Symmetry ◽  
Diesel Spray ◽  
Diesel Sprays ◽  
Spray Plume ◽  
Logarithmic Image Processing ◽  
Logarithmic Distance ◽  
Processing Framework ◽  
Spray Characterization

The increasing levels of emission standards in Diesel Engines require a detailed understanding, combustion and after treatment. This paper focuses on the spray development as one key parameter in the mixture preparation. The presentation of a methodology to differentiate the internal symmetry of spray images taken under different environmental conditions is presented. In a first step, a preprocessing is performed, then an image re-centering is made using the logarithmic average, afterwards different symmetry axes based on grey levels or on the plume boundary are calculated and, finally, the logarithmic distance characterizing the spray plume internal symmetry is computed. This distance gives more importance to the high grey level pixels, so using our optical setup, it characterizes the liquid continuous core symmetry. The methodology relies on the logarithmic image processing framework, providing a set of specific algebraic and functional operations to analyze images. This paper is an application of the logarithmic image processing framework on Diesel spray characterization. This is a step further in the quantitative diesel spray characterization by means of image analysis. The presented method can be applied to Diesel sprays illuminated with polychromatic or monochromatic light, under atmospheric or pressurized conditions.

scholarly journals REGION HOMOGENEITY IN THE LOGARITHMIC IMAGE PROCESSING FRAMEWORK: APPLICATION TO REGION GROWING ALGORITHMS

2019 ◽  
Vol 38 (1) ◽  
pp. 43
Author(s):  
Guillaume Noyel ◽  
Michel Jourlin
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
State Of The Art ◽  
Region Growing ◽  
Segmentation Method ◽  
Image Region ◽  
Logarithmic Image Processing ◽  
Image Component ◽  
Processing Framework ◽  
Component Tree

In order to create an image segmentation method robust to lighting changes, two novel homogeneity criteria of an image region were studied. Both were defined using the Logarithmic Image Processing (LIP) framework whose laws model lighting changes. The first criterion estimates the LIP-additive homogeneity and is based on the LIP-additive law. It is theoretically insensitive to lighting changes caused by variations of the camera exposure-time or source intensity. The second, the LIP-multiplicative homogeneity criterion, is based on the LIP-multiplicative law and is insensitive to changes due to variations of the object thickness or opacity. Each criterion is then applied in Revol and Jourlin’s (1997) region growing method which is based on the homogeneity of an image region. The region growing method becomes therefore robust to the lighting changes specific to each criterion. Experiments on simulated and on real images presenting lighting variations prove the robustness of the criteria to those variations. Compared to a state-of the art method based on the image component-tree, ours is more robust. These results open the way to numerous applications where the lighting is uncontrolled or partially controlled.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Improving focus measurements using logarithmic image processing

2010 ◽  
Vol 242 (3) ◽  
pp. 228-241 ◽  
Author(s):  
M. FERNANDES ◽  
Y. GAVET ◽  
J.-C. PINOLI
Keyword(s):  
Image Processing ◽  
Logarithmic Image Processing

Image Segmentation Using Rough Set Theory

2014 ◽  
Vol 1 (2) ◽  
pp. 62-74 ◽  
Author(s):  
Payel Roy ◽  
Srijan Goswami ◽  
Sayan Chakraborty ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Gray Level ◽  
Rule Generation ◽  
Local Minima ◽  
Fuzzy Correlation ◽  
Segmentation Methods

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

scholarly journals Using Relion software within Scipion framework

2020 ◽  
Author(s):  
Grigory Sharov ◽  
Dustin R. Morado ◽  
Marta Carroni ◽  
José Miguel de la Rosa-Trevín
Keyword(s):  
Image Processing ◽  
User Interfaces ◽  
Published Data ◽  
New Developments ◽  
Processing Pipeline ◽  
Software Packages ◽  
File Formats ◽  
Processing Framework ◽  
Taking Care

Scipion is a modular image processing framework integrating several software packages under a unified interface while taking care of file formats and conversions. Here new developments and capabilities of the Scipion plugin for the Relion software are presented and illustrated with the image processing pipeline of published data. The user interfaces of Scipion and Relion are compared and the key differences highlighted, allowing this manuscript to be used as a guide for both new and experienced users of these software. Different streaming image processing options are also discussed demonstrating the flexibility of the Scipion framework.SynopsisAn overview of the Scipion plugin for the Relion software is presented and various capabilities of image processing within Scipion framework are discussed.

scholarly journals Modified KNN-LVQ for Stairs Down Detection Based on Digital Image

2021 ◽  
Vol 12 (3) ◽  
pp. 141
Author(s):  
Ahmad Wali Satria Bahari Johan ◽  
Sekar Widyasari Putri ◽  
Granita Hajar ◽  
Ardian Yusuf Wicaksono
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Hybrid Method ◽  
Visually Impaired ◽  
Blind People ◽  
Gray Level ◽  
Computational Speed ◽  
Occurrence Matrix ◽  
Computing Speed

Persons with visual impairments need a tool that can detect obstacles around them. The obstacles that exist can endanger their activities. The obstacle that is quite dangerous for the visually impaired is the stairs down. The stairs down can cause accidents for blind people if they are not aware of their existence. Therefore we need a system that can identify the presence of stairs down. This study uses digital image processing technology in recognizing the stairs down. Digital images are used as input objects which will be extracted using the Gray Level Co-occurrence Matrix method and then classified using the KNN-LVQ hybrid method. The proposed algorithm is tested to determine the accuracy and computational speed obtained. Hybrid KNN-LVQ gets an accuracy of 95%. While the average computing speed obtained is 0.07248 (s).

Sign in / Sign up

Export Citation Format

Share Document