209 research outputs found
"A Margarita Debayle", Sin Datar
No full textabstract: Handwritten poem composed by Rubén Darío.The original Rubén Darío Papers 1882-1945 (MSS-339) are located at ASU Libraries Archives & Special Collections. For more information about visiting the collection see http://hdl.handle.net/2286/L.A.0.The first page has the title "A Margarita Debayle" and Ruben Dario's name. Likewise the odd numbers have written the title as well as Ruben Dario's name.Margarita Debayle (July 4, 1900 - December 19, 1983) was Luis H. Debayle's daughter, a friend of Rubén Darío.All pages are numerated in roman numerals
Image Processing, Analysis and Modeling of Particle Populations
No full textConférence invitée de Johan Debayle, centre SPIN, LGF UMR CNRS 5307, en qualité de “Plenary Speaker“.International audienceParticle populations are widely used in many industrial applications and fields of science from physics to biology or agronomy. In chemical engineering, in particular, it is generally desired to extract information on geometrical characteristics and on spatial distribution from 2D images of the population of particles involved in the process. For example in pharmaceutics, the size and the shape of crystals of active ingredients are known to have a considerable impact on the final quality of products, such as drugs. Hence, it is of main importance to be able to control in real time the granulometry (size and shape) of the crystals during the process. The purpose of this talk is then to show different ways (deterministic and stochastic methods) of image processing, analysis and modeling to geometrically characterize the particles from a sequence of 2-D images acquired by a camera (visualizing the particles during a particular process). The developed methods will be presented by addressing different issues: the perspective projection of the 3-D particle shape onto the image plane, the blurred appearance of unfocused particles, the degree of agglomeration or overlapping, and the random variation in size/shape of the observed particles. The methods are mainly based on image enhancement, restoration, segmentation, tracking, modeling, feature detection, stereology, stochastic geometry, pattern analysis and recognition. The methods will be particularly illustrated on real applications of crystallization processes (for pharmaceutics industry) and multiphase flow processes (for nuclear industry)
General Adaptive Neighborhood Image Processing (GANIP)
No full textConférence invitée de Johan Debayle, centre SPIN, LGF UMR CNRS 5307, en qualité de “Invited Talk“.International audienceThe framework entitled General Adaptive Neighborhood Image Processing (GANIP) has been introduced in order to propose an original local image representation and mathematical structure for adaptive non-linear processing and analysis of gray-tone images. In this talk, the GANIP framework is first presented and particularly studied in the context of image filtering. The central idea is based on the key notion of adaptivity which is simultaneously associated with the analyzing scales, the spatial structures and the intensity values of the image to be addressed. Several adaptive image transforms are then defined in the context of convolution analysis, order filtering or mathematical morphology. Such operators are no longer spatially invariant, but vary over the whole image with General Adaptive Neighborhoods (GANs) as adaptive operational windows, taking intrinsically into account the local image features. The GANIP framework allows efficient adaptive image filters to be built and opens new pathways that promise large prospects for non-linear image filtering
Recibo de Rubén Darío para L. H. Debayle, 1908 Octubre 10
No full textabstract: The receipt is an acknowledgment for an amount of 2,000 Pesetas to Luis H. Debayle. At the time of this receipt, Debayle was the Mexican Consul in France. Rubén Darío was in Madrid when this receipt was written.The original Rubén Darío Papers 1882-1945 (MSS-339) are located at ASU Libraries Archives & Special Collections. For more information about visiting the collection see http://hdl.handle.net/2286/L.A.0.Luis H. Debayle (1865 - 1938) was a recognized and prestigious Nicaraguan doctor. He had a close friendship with Rubén Darío
General Adaptive Neighborhood Image Processing and Analysis (GANIPA)
No full textConférence invitée de Johan Debayle, centre SPIN, LGF UMR CNRS 5307, en qualité de “Keynote Talk“.Conférence: Mobile Multimedia/Image Processing, Security, and Applications 2019 - Session 1: Innovative Image Processing TechniquesInternational audienceThe framework entitled General Adaptive Neighborhood Image Processing and Analysis (GANIPA) has been introduced in order to propose an original local image representation and mathematical structure for adaptive non-linear processing and analysis of gray-tone images and further extended to color images. The central idea is based on the key notion of adaptivity which is simultaneously associated with the analyzing scales, the spatial structures and the intensity values of the image to be addressed. Several adaptive image operators are then defined in the context of image filtering, image segmentation, image measurements and image registration by the use of convolution analysis, order filtering, mathematical morphology, integral geometrical or similarly measures. Such operators are no longer spatially invariant, but vary over the whole image with General Adaptive Neighborhoods (GANs) as adaptive operational windows, taking intrinsically into account the local image features. The first part of my talk will be focused on the context and the definitions and properties of the GANs. Once these adaptive neighborhoods are defined, it is possible to build different operators for image processing (filtering such as enhancement/restoration, segmentation, registration...) but also for image analysis providing tools for local image measurements (integral geometry, shape diagrams). The second part of my talk will be focused on these new operators and will be illustrated on real applications in different areas (biomedical, material, process engineering...). Finally, some conclusions and prospects will be given. In conclusion, the GANIPA framework allows efficient adaptive image operators to be built (using local adaptive operational woindows) and opens new pathways that promise large prospects for nonlinear image processing and analysis
General Adaptive Neighborhood Image Processing for biomedical applications
No full textIn biomedical imaging, the image processing techniques using spatially invariant transformations, with fixed operational windows, give efficient and compact computing structures, with the conventional separation between data and operations. Nevertheless, these operators have several strong drawbacks, such as removing significant details, changing some meaningful parts of large objects, and creating artificial patterns. This kind of approaches is generally not sufficiently relevant for helping the biomedical professionals to perform accurate diagnosis and therapy by using image processing techniques. Alternative approaches addressing context-dependent processing have been proposed with the introduction of spatially-adaptive operators (Bouannaya and Schonfeld, 2008; Ciuc et al., 2000; Gordon and Rangayyan, 1984;Maragos and Vachier, 2009; Roerdink, 2009; Salembier, 1992), where the adaptive concept results from the spatial adjustment of the sliding operational window. A spatially-adaptive image processing approach implies that operators will no longer be spatially invariant, but must vary over the whole image with adaptive windows, taking locally into account the image context by involving the geometrical, morphological or radiometric aspects. Nevertheless, most of the adaptive approaches require a priori or extrinsic informations on the image for efficient processing and analysis. An original approach, called General Adaptive Neighborhood Image Processing (GANIP), has been introduced and applied in the past few years by Debayle & Pinoli (2006a;b); Pinoli and Debayle (2007). This approach allows the building of multiscale and spatially adaptive image processing transforms using context-dependent intrinsic operational windows. With the help of a specified analyzing criterion (such as luminance, contrast, ...) and of the General Linear Image Processing (GLIP) (Oppenheim, 1967; Pinoli, 1997a), such transforms perform a more significant spatial and radiometric analysis. Indeed, they take intrinsically into account the local radiometric, morphological or geometrical characteristics of an image, and are consistent with the physical (transmitted or reflected light or electromagnetic radiation) and/or physiological (human visual perception) settings underlying the image formation processes. The proposed GAN-based transforms are very useful and outperforms several classical or modern techniques (Gonzalez and Woods, 2008) - such as linear spatial transforms, frequency noise filtering, anisotropic diffusion, thresholding, region-based transforms - used for image filtering and segmentation (Debayle and Pinoli, 2006b; 2009a; Pinoli and Debayle, 2007). This book chapter aims to first expose the fundamentals of the GANIP approach (Section 2) by introducing the GLIP frameworks, the General Adaptive Neighborhood (GAN) sets and two kinds of GAN-based image transforms: the GAN morphological filters and the GAN Choquet filters. Thereafter in Section 3, several GANIP processes are illustrated in the fields of image restoration, image enhancement and image segmentation on practical biomedical application examples. Finally, Section 4 gives some conclusions and prospects of the proposed GANIP approach
Image Processing, Analysis and Modeling of Particle Populations
No full textConférence invitée de Johan Debayle, centre SPIN, LGF UMR CNRS 5307, en qualité de “Invited Talk“.International audienceParticle populations are widely used in many industrial applications and fields of science from physics to biology or agronomy. In chemical engineering, in particular, it is generally desired to extract information on geometrical characteristics and on spatial distribution from 2D images of the population of particles involved in the process. For example in pharmaceutics, the size and the shape of crystals of active ingredients are known to have a considerable impact on the final quality of products, such as drugs. Hence, it is of main importance to be able to control in real time the granulometry (size and shape) of the crystals during the process. The first part of this talk will be focused on specific geometrical and morphometrical descriptors giving information on the size, shape and spatial distribution of the particles. They have a compact representation with good mathematical properties and are easy to compute. They are based on integral geometry, shape diagrams and computational geometry. The second part of this talk will show different ways (deterministic and stochastic methods) of image processing, analysis and modeling to geometrically characterize the particles from a sequence of 2-D images acquired by a camera (visualizing the particles during a particular process). The developed methods will be presented by addressing different issues: the perspective projection of the 3-D particle shape onto the image plane, the blurred appearance of unfocused particles, the degree of agglomeration or overlapping, and the random variation in size/shape of the observed particles. The methods are mainly based on image enhancement, restoration, segmentation, tracking, modeling, feature detection, stereology, stochastic geometry, pattern analysis and recognition. The methods will be particularly illustrated on real applications of crystallization processes (for pharmaceutics industry) and multiphase flow processes (for nuclear industry). Some conclusions and prospects will be finally given
General Adaptive Neighborhood Image Processing for biomedical applications
No full textIn biomedical imaging, the image processing techniques using spatially invariant transformations, with fixed operational windows, give efficient and compact computing structures, with the conventional separation between data and operations. Nevertheless, these operators have several strong drawbacks, such as removing significant details, changing some meaningful parts of large objects, and creating artificial patterns. This kind of approaches is generally not sufficiently relevant for helping the biomedical professionals to perform accurate diagnosis and therapy by using image processing techniques. Alternative approaches addressing context-dependent processing have been proposed with the introduction of spatially-adaptive operators (Bouannaya and Schonfeld, 2008; Ciuc et al., 2000; Gordon and Rangayyan, 1984;Maragos and Vachier, 2009; Roerdink, 2009; Salembier, 1992), where the adaptive concept results from the spatial adjustment of the sliding operational window. A spatially-adaptive image processing approach implies that operators will no longer be spatially invariant, but must vary over the whole image with adaptive windows, taking locally into account the image context by involving the geometrical, morphological or radiometric aspects. Nevertheless, most of the adaptive approaches require a priori or extrinsic informations on the image for efficient processing and analysis. An original approach, called General Adaptive Neighborhood Image Processing (GANIP), has been introduced and applied in the past few years by Debayle & Pinoli (2006a;b); Pinoli and Debayle (2007). This approach allows the building of multiscale and spatially adaptive image processing transforms using context-dependent intrinsic operational windows. With the help of a specified analyzing criterion (such as luminance, contrast, ...) and of the General Linear Image Processing (GLIP) (Oppenheim, 1967; Pinoli, 1997a), such transforms perform a more significant spatial and radiometric analysis. Indeed, they take intrinsically into account the local radiometric, morphological or geometrical characteristics of an image, and are consistent with the physical (transmitted or reflected light or electromagnetic radiation) and/or physiological (human visual perception) settings underlying the image formation processes. The proposed GAN-based transforms are very useful and outperforms several classical or modern techniques (Gonzalez and Woods, 2008) - such as linear spatial transforms, frequency noise filtering, anisotropic diffusion, thresholding, region-based transforms - used for image filtering and segmentation (Debayle and Pinoli, 2006b; 2009a; Pinoli and Debayle, 2007). This book chapter aims to first expose the fundamentals of the GANIP approach (Section 2) by introducing the GLIP frameworks, the General Adaptive Neighborhood (GAN) sets and two kinds of GAN-based image transforms: the GAN morphological filters and the GAN Choquet filters. Thereafter in Section 3, several GANIP processes are illustrated in the fields of image restoration, image enhancement and image segmentation on practical biomedical application examples. Finally, Section 4 gives some conclusions and prospects of the proposed GANIP approach
Special Section Guest Editorial: Machine Vision—Systems, Methods, and Applications
No full textSpecial section editors Johan Debayle, Wolfgang Osten, and Dmitry Nikolaev introduce the Special Section on Machine Vision: Systems, Methods, and Applications.The emergence of machine vision as a ubiquitous platform for innovations has laid the foundation for the rapid growth of information. Side by side, the use of mobile and wireless devices such as PDA, laptop, and cell phones for accessing the Internet has paved the way for relatedtechnologies to flourish through recent developments. In addition, machine vision technology is promoting better integration of the digital world with the physical environment. This special section focuses primarily on research in the field of machine vision. The purpose is to review the progress and achievements of the research work to date. Topics of interest for this special section include: machine vision systems and components (hardware and software, sensor fusion), machine vision applications (industrial inspection, navigation, optical metrology, autonomous vehicles, remote sensing, astronomy and astronautics, bio-medical imaging, face and gesture recognition, data compression, security and coding, document processing), computer vision (scene reconstruction, video tracking, 3D pose estimation, action recognition), active vision (autonomous cameras, wearable and assistive computing, realtime 3D scene segmentation and reconstruction), 3D vision (stereovision, laser triangulation, multi-cameras), machine learning (artificial intelligence, neural networks, deep learning, big data, and data mining), image processing (analog, digital, electronic, optical, acoustical, hybrid), image processing methods (pre-processing, image analysis, feature extraction, segmentation, classification, pattern recognition, coding, understanding, modeling, color, texture, shape, geometry, topology, SIMD, MIMD), and computational imaging
Exploration of Vigorous Privacy Preserving Models using Deep Learning in Digital Media
No full textLead Editor : Ashwani Kumar ; Guest Editor : Johan Debayle - Justin ZhangIn this era, a massive amount of digital media data is created and transferred every second, in formats including text, photo, audio, and video. Multimedia has been employed in many areas of human society: music, film, and video games make up the majority of our daily enjoyment; medical visuals assist doctors in making more accurate diagnoses; and fingerprints and face photographs are used to identify persons for a variety of purposes. Machine learning (ML)-based technologies significantly improve our capacity to analyze, process, and use multimedia, however, both multimedia processing and ML technologies require a significant amount of computing and storage. Recent advancements in machine learning have led to several safe and resilient applications, such as digital watermarking, digital image processing, speech recognition, and natural language processing. Machine learning algorithms have overcome numerous obstacles; particularly, trained ML models have made it easier for researchers to deliver cutting-edge results. Digital watermark data is scrambled, and a transform domain-based hybrid watermarking approach can be employed to embed the watermark into the transform coefficients. Machine learning-based digital watermarking solutions have recently received a lot of attention, as machine learning-based embedding approaches for digital watermarking allow the watermark to be injected through learning, allowing the extraction algorithm to quickly retrieve the watermark while maintaining invisibility. The goal of this Special Issue is to focus on advances in machine learning-based digital watermarking, as well as future research possibilities. For this Special Issue, we are seeking high-quality original research and review articles on privacy protection and current advances in machine learning-based digital watermarking
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