scholarly journals Double-Sided Probing by Map of Asplund’s Distances Using Logarithmic Image Processing in the Framework of Mathematical Morphology

2017 ◽  
pp. 408-420 ◽  
Author(s):  
Guillaume Noyel ◽  
Michel Jourlin
Keyword(s):  
Image Processing ◽  
Mathematical Morphology ◽  
Logarithmic Image Processing

scholarly journals Functional Asplund metrics for pattern matching, robust to variable lighting conditions

2020 ◽  
Author(s):  
Guillaume Noyel ◽  
Michel Jourlin
Keyword(s):  
Image Processing ◽  
Pattern Matching ◽  
Mathematical Morphology ◽  
Exposure Time ◽  
Low Contrast ◽  
Lighting Conditions ◽  
Logarithmic Image Processing ◽  
Robust To Noise ◽  
Complete Framework ◽  
Functional Metrics

In this paper, we propose a complete framework to process images captured under uncontrolled lighting and especially under low lighting. By taking advantage of the Logarithmic Image Processing (LIP) context, we study two novel functional metrics: i) the LIP-multiplicative Asplund metric which is robust to object absorption variations and ii) the LIP-additive Asplund metric which is robust to variations of source intensity or camera exposure-time. We introduce robust to noise versions of these metrics. We demonstrate that the maps of their corresponding distances between an image and a reference template are linked to Mathematical Morphology. This facilitates their implementation. We assess  them in various situations with different lightings and movement. Results show that those maps of distances are robust to lighting variations. Importantly, they are efficient to detect patterns in low-contrast images with a template acquired under a different lighting.

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

Mathematical Morphology and Fourier Analysis on Time Scales

2009 ◽  
Author(s):  
Robert J Marks II
Keyword(s):  
Image Processing ◽  
Fourier Analysis ◽  
Mathematical Morphology ◽  
Time Scales ◽  
Time Scale ◽  
Time Signal ◽  
Scale Theory ◽  
Black And White ◽  
Introductory Material ◽  
Special Cases

Mathematical morphology, used extensively in image processing, tracks the support domains for the operation of convolution and deconvolution. Morphology is also important in the modelling of signals on time scales. Time scale theory provides a generalization tent under which the operations of discrete and continuous time signal and Fourier analysis rest as special cases. The time scale paradigm provides modelling under which a rich class of hybrid signals and systems can be analyzed. We begin with introductory material on mathematical morphology which is foundational to the development of time scale theory. The support of convolution is related to the operation of dilation in mathematical morphology. Mathematical morphology is most commonly associated with image processing. Applications of morphology was initially applied to binary black and white images by Matheron [966]. The field is richly developed [506, 578]. Here, we outline the fundamentals. In N dimensions, let X and H denote a set of vectors or, in the degenerate case of one dimension, a set of real numbers.

Formulation of Fractional Derivative-Based De-Hazing Algorithm and Implementation on Mobile-Embedded Devices

2019 ◽  
Vol 19 (01) ◽  
pp. 1950003
Author(s):  
Uche A. Nnolim
Keyword(s):  
Image Processing ◽  
Fractional Derivative ◽  
Mobile Devices ◽  
Fractional Order ◽  
Spatial Filter ◽  
Embedded Devices ◽  
Processing Application ◽  
Fractional Order Calculus ◽  
Image Processing Application ◽  
Logarithmic Image Processing

This paper presents the modification of a previously developed algorithm using fractional order calculus and its implementation on mobile-embedded devices such as smartphones. The system performs enhancement on three categories of images such as those exhibiting uneven illumination, faded features/colors and hazy appearance. The key contributions include the simplified scheme for illumination correction, contrast enhancement and de-hazing using fractional derivative-based spatial filter kernels. These are achieved without resorting to logarithmic image processing, histogram-based statistics and complex de-hazing techniques employed by conventional algorithms. The simplified structure enables ease of implementation of the algorithm on mobile devices as an image processing application. Results indicate that the fractional order version of the algorithm yields good results relative to the integer order version and other algorithms from the literature.

Morphological Filtering Principles

2011 ◽  
pp. 1102-1111
Author(s):  
Jose Crespo
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Mathematical Morphology ◽  
Digital Form ◽  
Practical Applications ◽  
Theoretical Investigations ◽  
Morphological Filtering ◽  
The 1960S

In the last fifty years, approximately, advances in computers and the availability of images in digital form have made it possible to process and to analyze them in automatic (or semi-automatic) ways. Alongside with general signal processing, the discipline of image processing has acquired a great importance for practical applications as well as for theoretical investigations. Some general image processing references are (Castleman, 1979) (Rosenfeld & Kak, 1982) (Jain, 1989) (Pratt, 1991) (Haralick & Shapiro, 1992) (Russ, 2002) (Gonzalez & Woods, 2006). Mathematical Morphology, which was founded by Serra and Matheron in the 1960s, has distinguished itself from other types of image processing in the sense that, among other aspects, has focused on the importance of shapes. The principles of Mathematical Morphology can be found in numerous references such as (Serra, 1982) (Serra, 1988) (Giardina & Dougherty, 1988) (Schmitt & Mattioli, 1993) (Maragos & Schafer, 1990) (Heijmans, 1994) (Soille, 2003) (Dougherty & Lotufo, 2003) (Ronse, 2005).

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