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

Discriminant random field and patch-based redundancy analysis for image change detection

International audienceTo develop better image change detection algorithms, new models able to capture all the spatio-temporal regularities and geometries seen in an image pair are needed. In con- trast to the usual pixel-wise methods, we propose a patch- based formulation for modeling semi-local interactions and detecting occlusions and other local or regional changes in an image pair. To this end, the image redundancy property is exploited to detect unusual spatio-temporal patterns in the scene. We first define adaptive detectors of changes between two given image patches and combine locally in space and scale such detectors. The resulting score at a given loca- tion is exploited within a discriminant Markov random field (DRF) whose global optimization flags out changes with no optical flow computation. Experimental results on several applications demonstrate that the method performs well at detecting occlusions and meaningful regional changes and is especially robust in the case of low signal-to-noise ratios

Aesthetically Relevant Image Captioning

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the observation that most textual comments of an image are about objects and their interactions rather than aspects of aesthetics, we first introduce the concept of Aesthetic Relevance Score (ARS) of a sentence and have developed a model to automatically label a sentence with its ARS. We then use the ARS to design the ARIC model which includes an ARS weighted IAC loss function and an ARS based diverse aesthetic caption selector (DACS). We present extensive experimental results to show the soundness of the ARS concept and the effectiveness of the ARIC model by demonstrating that texts with higher ARS's can predict the aesthetic ratings more accurately and that the new ARIC model can generate more accurate, aesthetically more relevant and more diverse image captions. Furthermore, a large new research database containing 510K images with over 5 million comments and 350K aesthetic scores, and code for implementing ARIC are available at https://github.com/PengZai/ARIC.Comment: Aceepted by AAAI2023. Code and results available at https://github.com/PengZai/ARI

Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Identification and Characterisation of Novel Phages of Pectobacterium and Erwinia

Losses in crop yields due to disease need to be reduced to meet increasing global food demands associated with growth in the human population. There is a well-recognised need to develop new environmentally-friendly control strategies to combat bacterial crop diseases. There are several crop diseases for which no effective bactericidal agents are currently available, such as potato blackleg and soft rot disease caused by Pectobacterium atrosepticum and other members of soft rot Enterobacteriaceae (Czajkowski et al., 2011). Furthermore, current control measures involving the use of traditional chemicals or antibiotics are losing their efficacy due to the natural development of bacterial resistance to these agents, as seen for fire blight of the pear and apple tree caused by Erwinia amylovora (de León Door et al., 2013; Mayerhofer et al., 2009; Ordax et al., 2006; Russo et al., 2008). Bacteriophages (phage), the viruses of bacteria, have received increased research interest in recent years as an environmentally friendly means of controlling bacterial diseases. However, not all phages possess the features that would enable them to be effective bactericidal agents. To this end, this thesis provides a detailed study of phages that infect Pectobacterium atrosepticum and Erwinia amylovora. The knowledge gained in the execution of this PhD thesis contributes to the pool knowledge about the lifestyles of the phages examined thus enabling a more informed choice with regard to the selection of suitable phages for biocontrol applications for the relevant phytopathogens

Forests for a Better Future Sustainability, Innovation and Interdisciplinarity

This book highlights the role of research in innovation and sustainability in the forest sector. The contributions included fall within the broad thematic areas of forest science and cover crucial topics such as biocontrol, forest fire risk, harvesting and logging practices, quantitative and qualitative assessments of forest products, urban forests, and wood treatments—topics that have also been addressed from an interdisciplinary perspective. The contributions also have practical applications, as they deal with the ecological and economic importance of forests and new technologies for the conservation, monitoring, and improvement of services and forest value