Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

MULTI-SCALE SEMI-TRANSPARENT BLOTCH REMOVAL ON ARCHIVED PHOTOGRAPHS USING BAYESIAN MATTING TECHNIQUES AND VISIBILITY LAWS

ABSTRACT This paper presents an automatic technique to remove semitransparent blotches (due to moisture) from archived photographs and documents. Blotches are processed in the HSV space. While chroma components are processed using a simple texture synthesis method, the intensity component is split into an over-complete wavelet representation. In the approximation band, the blotch is modelled as an alpha matte which reduces the intensity of the image in a non-uniform yet smooth manner. The alpha matte is estimated using a Bayesian approach and its effect reversed. Wavelet details are left unchanged in the case of perfect semi-transparency or attenuated using visibility laws whenever dirt and dust cause spurious edges. Experimental results achieved on many historical photographs show the effectiveness of the proposed approach

Sur la Restauration et l'Edition de Vidéo : Détection de Rayures et Inpainting de Scènes Complexes

The inevitable degradation of visual content such as images and films leads to the goal ofimage and video restoration. In this thesis, we look at two specific restoration problems : the detection ofline scratches in old films and the automatic completion of videos, or video inpainting as it is also known.Line scratches are caused when the film physically rubs against a mechanical part. This origin resultsin the specific characteristics of the defect, such as verticality and temporal persistence. We propose adetection algorithm based on the statistical approach known as a contrario methods. We also proposea temporal filtering step to remove false alarms present in the first detection step. Comparisons withprevious work show improved recall and precision, and robustness with respect to the presence of noiseand clutter in the film.The second part of the thesis concerns video inpainting. We propose an algorithm based on theminimisation of a patch-based functional of the video content. In this framework, we address the followingproblems : extremely high execution times, the correct handling of textures in the video and inpaintingwith moving cameras. We also address some convergence issues in a very simplified inpainting context.La degradation inévitable des contenus visuels (images, films) conduit nécessairementà la tâche de la restauration des images et des vidéos. Dans cetre thèse, nous nous intéresserons àdeux sous-problèmes de restauration : la détection des rayures dans les vieux films, et le remplissageautomatique des vidéos (“inpainting vidéo en anglais).En général, les rayures sont dues aux frottements de la pellicule du film avec un objet lors de laprojection du film. Les origines physiques de ce défaut lui donnent des caractéristiques très particuliers.Les rayures sont des lignes plus ou moins verticales qui peuvent être blanches ou noires (ou parfois encouleur) et qui sont temporellement persistantes, c’est-à-dire qu’elles ont une position qui est continuedans le temps. Afin de détecter ces défauts, nous proposons d’abord un algorithme de détection basésur un ensemble d’approches statistiques appelées les méthodes a contrario. Cet algorithme fournitune détection précise et robuste aux bruits et aux textures présentes dans l’image. Nous proposonségalement une étape de filtrage temporel afin d’écarter les fausses alarmes de la première étape dedétection. Celle-ci améliore la précision de l’algorithme en analysant le mouvement des détections spatiales.L’ensemble de l’algorithme (détection spatiale et filtrage temporel) est comparé à des approchesde la littérature et montre un rappel et une précision grandement améliorés.La deuxième partie de cette thèse est consacrée à l’inpainting vidéo. Le but ici est de remplirune région d’une vidéo avec un contenu qui semble visuellement cohérent et convaincant. Il existeune pléthore de méthodes qui traite ce problème dans le cas des images. La littérature dans le casdes vidéos est plus restreinte, notamment car le temps d’exécution représente un véritable obstacle.Nous proposons un algorithme d’inpainting vidéo qui vise l’optimisation d’une fonctionnelle d’énergiequi intègre la notion de patchs, c’est-à-dire des petits cubes de contenu vidéo. Nous traitons d’abord leprobl’‘eme du temps d’exécution avant d’attaquer celui de l’inpainting satisfaisant des textures dans lesvidéos. Nous traitons également le cas des vidéos dont le fond est en mouvement ou qui ont été prisesavec des caméras en mouvement. Enfin, nous nous intéressons à certaines questions de convergencede l’algorithme dans des cas très simplifiés

Deep Incremental Learning for Object Recognition

In recent years, deep learning techniques received great attention in the field of information technology. These techniques proved to be particularly useful and effective in domains like natural language processing, speech recognition and computer vision. In several real world applications deep learning approaches improved the state-of-the-art. In the field of machine learning, deep learning was a real revolution and a number of effective techniques have been proposed for both supervised and unsupervised learning and for representation learning. This thesis focuses on deep learning for object recognition, and in particular, it addresses incremental learning techniques. With incremental learning we denote approaches able to create an initial model from a small training set and to improve the model as new data are available. Using temporal coherent sequences proved to be useful for incremental learning since temporal coherence also allows to operate in unsupervised manners. A critical point of incremental learning is called forgetting which is the risk to forget previously learned patterns as new data are presented. In the first chapters of this work we introduce the basic theory on neural networks, Convolutional Neural Networks and incremental learning. CNN is today one of the most effective approaches for supervised object recognition; it is well accepted by the scientific community and largely used by ICT big players like Google and Facebook: relevant applications are Facebook face recognition and Google image search. The scientific community has several (large) datasets (e.g., ImageNet) for the development and evaluation of object recognition approaches. However very few temporally coherent datasets are available to study incremental approaches. For this reason we decided to collect a new dataset named TCD4R (Temporal Coherent Dataset For Robotics)

Sensors Application in Agriculture

Novel technologies are playing an important role in the development of crop and livestock farming and have the potential to be the key drivers of sustainable intensification of agricultural systems. In particular, new sensors are now available with reduced dimensions, reduced costs, and increased performances, which can be implemented and integrated in production systems, providing more data and eventually an increase in information. It is of great importance to support the digital transformation, precision agriculture, and smart farming, and to eventually allow a revolution in the way food is produced. In order to exploit these results, authoritative studies from the research world are still needed to support the development and implementation of new solutions and best practices. This Special Issue is aimed at bringing together recent developments related to novel sensors and their proved or potential applications in agriculture

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Science-based restoration monitoring of coastal habitats, Volume Two: Tools for monitoring coastal habitats

Healthy coastal habitats are not only important ecologically; they also support healthy coastal communities and improve the quality of people’s lives. Despite their many benefits and values, coastal habitats have been systematically modified, degraded, and destroyed throughout the United States and its protectorates beginning with European colonization in the 1600’s (Dahl 1990). As a result, many coastal habitats around the United States are in desperate need of restoration. The monitoring of restoration projects, the focus of this document, is necessary to ensure that restoration efforts are successful, to further the science, and to increase the efficiency of future restoration efforts

Ornamental plants: annual reports and research reviews, 2002

Ohio State University Extension Nursery, Landscape, and Turf Team directory: 2003 / Jack Kerrigan -- Floriculture Industry Roundtable of Ohio: 2003 / Charles Behnke -- Ohio State University Extension 2002 Buckeye Yard and Garden Line evaluation survey / Amy K. Stone and James A. Chatfield -- Weather, environmental, and cultural problems of ornamental plants in Ohio: 2002 / Pamela J. Bennett -- Infectious disease problems of ornamental plants in Ohio: 2002 / James A. Chatfield, Nancy A. Taylor, Erik A. Draper, and Joseph F. Boggs -- A biological calendar for predicting pest activity: six years of plant and insect phenology in Secrest Arboretum / Daniel A. Herms -- Biological suppression of foliar diseases of ornamental plants with composted manures, biosolids, and Trichoderma hamatum 382 / Harry A. J. Hoitink, Carol A. Musselman, Terry L. Moore, Leona E. Horst, Charles R. Krause, Randy A. Zondag, and Hannah Mathers -- Growth and water use by four leguminous tree species in containers on a gravel surface or embedded in mulch / Michael Knee, Daniel K. Struve, Michael H. Bridgewater, and Joseph W. Phillips -- The effects of sprayer configuration on efficacy for the control of scab on crabapple / Charles R. Krause, Richard C. Derksen, Leona E. Horst, Randall Zondag, Ross D. Brazee, Michael G. Klein, and Michael E. Reding -- Update on honeylocust knot / Pierluigi Bonello, Maria Bellizzi, and Harry A. J. Hoitink -- Control of phytophthora and other major diseases of Ericaceous plants / Harry A. J. Hoitink, Steven T. Nameth, and James C. Locke -- Is your landscape mulch going up in smoke? / Larry G. Steward, T. Davis Sydnor, and Bert Bishop -- IR-4 ornamental trials conducted by USDA-ARS in Ohio: 2002 / Betsy A. Anderson, Michael E. Reding, Michael G. Klein, and Charles R. Krause -- Research on black vine weevil and white grubs in ornamental nurseries-in Ohio by USDA-ARS / Michael E. Reding, Michael G. Klein, Ross D. Brazee, and Charles R. Krause -- Herbaceous ornamental field trial results in Clark County, Ohio – 2002 / Pamela J. Bennett -- Results of annual trial gardens at the Cincinnati Zoo and Botanical Garden for 2002 / Dave Dyke -- Ohio State University Learning Garden annual cultivar trials / Monica M. Kmetz-Gonzalez and Claudio C. Pasian -- A collection of crabapple knowledge from Secrest Arboretum: 1993-2002 / Erik A. Draper, James A. Chatfield, and Kenneth D. Cochran -- Key results of the 2001 Ohio Green Industry Survey / Gary Y. Gao, John J. Smith, James A. Chatfield, Joseph F. Boggs, Erik A. Draper, and Hannah Mathers -- The USDA/Agricultural Research Service research weather network in Lake County, Ohio - 2002 update / R. D. Brazee, R. C. Derksen, C. R. Krause, K. A. Williams, D. Lohnes, M. G. Klein, M. Reding, R. Lyons, W. Hendricks, R. Zondag, R. D. Fox, and D. Herms -- The OSU Chadwick Arboretum Learning Gardens / Dr. Steven Still and Annette Duetz -- Choosing soil testing labs / Gary Y, Gao, Maurice E. Watson, Joseph F. Boggs, and James A. Chatfield -- Top horticultural references for a green industry professional's library / Gary Y. Gao and Pamela J. Bennett -- The maples of Secrest Arboretum / Gary W. Graham, James A. Chatfield, and Kenneth D. Cochran -- Deck the halls with boughs from Ollie! / Kenneth D. Cochran and James A. Chatfiel