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As household waste increases, it becomes more and more important to sort and recycle. However European studies suggest that there is still an important part of incorrect sorting, and that many citizens could use an assistance of some sort. In the last few years, computer vision algorithms have been used to tackle this problem with high classification accuracies. However, fewer works required a sorting with more than six classes, as some features might become too difficult to distinguish. This thesis presents a comparative study of several object detection algorithms, for sorting of trash pieces following IDELUX's sorting directives and real-time constraints. IDELUX's dataset of 10050 waste images is divided into 12 classes. Some of those classes are very similar and with an important imbalance in the number of samples per class, this dataset presents a new challenge in the waste detection field. Four algorithms were specifically trained and tested in order to determine which architecture was the fittest to perform the task, whether it could be performed by a simple classifier, a one stage or a two stage detector. The proposed objects detectors are RetinaNet, YOLOv5 and Faster R-CNN, while the chosen classifier is a ResNet model. The models were evaluated on their accuracy, their mean average precision, their IOU, their inference time and their training time. For this specific project, ResNet outperforms all of the other models and achieves an accuracy of 90 \%. Overall, the results show that classification and detection algorithms are capable of tackling more complex waste sorting problems than the ones currently explored in the literature.

object detection --- waste sorting --- waste classification --- detection benchmark study for waste sorting --- classification --- YOLO --- ResNet --- RetinaNet --- Faster R-CNN --- Ingénierie, informatique & technologie > Sciences informatiques

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Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews.

Information technology industries --- Machine vision --- image analysis --- item counting device --- electro-deposition industry --- digital intraoral radiography --- image preprocessing --- periapical lesions --- texture analysis --- prostate cancer --- histopathology --- microscopic --- tissue image --- segmentation --- morphological --- quantitative --- classification --- SVM --- image resizing --- local Tchebichef moments (LTM) --- scaling --- scale-and-stretch --- seam carving --- faster R-CNN --- cutting pieces --- multi-period pattern --- skew angle --- period length --- colored texture pattern classification --- global-local texture classification --- color-texture features --- color-texture feature extraction --- bagging post-processing --- BQMP and Haralick global-local feature integration --- maceral components --- image segmentation --- coal petrography --- random forest --- two-level clustering --- deep neural networks --- adaptive gradient methods --- stochastic gradient descent --- bounded scheduling method --- image classification --- language modeling --- texture --- deep learning --- MB-LBP --- surface defect detection --- feature extraction --- defect recognition --- mammogram --- meta-heuristics --- optimization --- breast cancer --- detection --- skin microrelief --- water sorption --- aging --- hair --- mathematics of colour and texture --- hand-designed image descriptors --- rank features --- partial orders --- river scene segmentation --- local binary pattern --- hue variance --- surface reflection --- audio classification --- dissimilarity space --- siamese network --- ensemble of classifiers --- pattern recognition --- animal audio --- co-saliency --- omnidirectional images --- video saliency --- visual saliency estimation --- Machine vision --- image analysis --- item counting device --- electro-deposition industry --- digital intraoral radiography --- image preprocessing --- periapical lesions --- texture analysis --- prostate cancer --- histopathology --- microscopic --- tissue image --- segmentation --- morphological --- quantitative --- classification --- SVM --- image resizing --- local Tchebichef moments (LTM) --- scaling --- scale-and-stretch --- seam carving --- faster R-CNN --- cutting pieces --- multi-period pattern --- skew angle --- period length --- colored texture pattern classification --- global-local texture classification --- color-texture features --- color-texture feature extraction --- bagging post-processing --- BQMP and Haralick global-local feature integration --- maceral components --- image segmentation --- coal petrography --- random forest --- two-level clustering --- deep neural networks --- adaptive gradient methods --- stochastic gradient descent --- bounded scheduling method --- image classification --- language modeling --- texture --- deep learning --- MB-LBP --- surface defect detection --- feature extraction --- defect recognition --- mammogram --- meta-heuristics --- optimization --- breast cancer --- detection --- skin microrelief --- water sorption --- aging --- hair --- mathematics of colour and texture --- hand-designed image descriptors --- rank features --- partial orders --- river scene segmentation --- local binary pattern --- hue variance --- surface reflection --- audio classification --- dissimilarity space --- siamese network --- ensemble of classifiers --- pattern recognition --- animal audio --- co-saliency --- omnidirectional images --- video saliency --- visual saliency estimation

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The use of electronics, close-range sensing, and artificial intelligence has changed the management paradigm in many contemporary industries in which Big Data analytics by automated processes has become the backbone of decision making and improvement. Acknowledging the integration of electronics, devices, sensors, and intelligent algorithms in much of the equipment used in forest operations, as well as their use in various forestry-related applications, it is apparent that many disciplines within forestry and forest science still rely on data collected traditionally, which is resource-intensive. In turn, this brings limitations in characterizing the specific behaviors of forest product systems and wood supply chains, and often prevents the development of solutions for improvement or inferring the laws behind the operation and management of such systems. Undoubtedly, many solutions still need to be developed in the future to provide the technology required for the effective management of forests. In this regard, the Special Issue entitled “Electronics, Close-Range Sensors and Artificial Intelligence in Forestry” highlights many examples of how technological improvements can be brought to forestry and to other related fields of science and practice.

Research & information: general --- Biology, life sciences --- Forestry & related industries --- forest fire detection --- deep learning --- ensemble learning --- Yolov5 --- EfficientDet --- EfficientNet --- big data --- automation --- artificial intelligence --- multi-modality --- acceleration --- classification --- events --- performance --- motor-manual felling --- willow --- Romania --- region detection of forest fire --- grading of forest fire --- weakly supervised loss --- fine segmentation --- region-refining segmentation --- lightweight Faster R-CNN --- ultrasound sensors --- road scanner --- terrestrial laser scanning --- TLS --- forest road maintenance --- forest road monitoring --- crowned road surface --- digital twinning --- climate smart --- LiDAR --- digitalization --- forest loss --- land-cover change --- machine learning --- spatial heterogeneity --- random forest model --- geographically weighted regression --- aboveground biomass --- estimation --- remote sensing --- Sentinel-2 --- Iran --- multiple regression --- artificial neural network --- k-nearest neighbor --- random forest --- canopy --- drone --- leaf --- leaves --- foliar --- samples --- sampling --- Aerial robotics --- UAS --- UAV --- IoT --- forest ecology --- accessibility --- wood --- diameter --- length --- close-range sensing --- Augmented Reality --- comparison --- accuracy --- effectiveness --- potential --- forestry 4.0 --- wood technology --- sawmilling --- productivity --- prediction --- long-term --- tree ring --- forestry detection --- resistance sensor --- micro-drilling resistance method --- signal processing --- Signal-to-Noise Ratio (SNR) --- n/a

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This book presents collective works published in the recent Special Issue (SI) entitled " Digital Signal, Image and Video Processing for Emerging Multimedia Technology". These works address the emerging technology in signal processing and its new aspects, as well as the related applications. Recent developments in image/video-based deep learning technology have enabled new services in the field of multimedia and recognition technology. The applications vary and range from digital signal processing to image, video and multimedia signal processing, also including object classification, learning mechanism design and data security. Recent advances in numerical, theoretical and experimental methodologies are presented within the scope of the current book, along with the finding of new learning methods and new methodological developments and their limitations. This book brings together a collection of inter-/multidisciplinary works applied to many classification and data security applications in a coherent manner.

closed circuit television (CCTV) --- character order preserving --- cloud system --- privacy risk --- security --- video surveillance --- 3D --- depth map --- inter-component prediction --- MVD --- reversible data hiding --- texture --- wavelet analysis --- deep learning --- super-resolution --- deep neural architecture --- pattern mining --- multi-scale analysis --- reversible data hiding (RDH) --- image processing --- cloud computing --- public key cryptography (PKC) --- classification --- content–based image retrieval --- genetic algorithms --- image retrieval --- image classification --- Wiener-Granger causality --- block-compressive sensing (BCS) --- saliency --- error analysis --- flexible partitioning --- step-less adaptive sampling --- non-linear filters --- MCV and MLV filters --- de-noising --- noise removal --- edge preserving --- video coding --- motion estimation --- motion compensation --- affine motion model --- perspective motion model --- VVC --- quantization (signal) --- channel allocation --- scalable video coding --- convolution neural network --- scene recognition --- vector of locally aggregated descriptor --- weakly supervised attention map --- fire and smoke detection --- spatial and temporal --- wavelet transform --- coefficient of variation --- image steganalysis --- WOW --- UNIWARD --- ternary classification --- convolutional neural network (CNN) --- bayesian optimization --- gaussian process --- learning rate --- acauisition function --- machine learning --- moving object --- image stabilization --- object detection --- optical flow --- surveillance --- UAVs --- multiview high efficiency video coding --- ρ model --- bit allocation --- rate control --- image similarity --- frame complexity --- image deblurring --- generative adversarial network --- Wasserstein distance --- adversarial loss --- perceptual loss --- sentiment analysis --- social media --- lexicon --- image fusion --- multi-focus --- trimaps --- focus maps --- VisDrone2019 --- aerial imagery --- Faster R-CNN --- SSD --- RFCN --- YOLOv3 --- RetinaNet --- SNIPER --- CenterNet --- wrist-mounted DiverPAD --- electrical insulator --- capacitive touchscreen --- marine leisure activities --- convolutional neural networks --- pattern recognition --- low light --- image restoration --- denoise --- noise reduction --- deep leaning --- multiple feature --- dependency detection --- surveillance system --- n/a --- content-based image retrieval

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This book presents collective works published in the recent Special Issue (SI) entitled " Digital Signal, Image and Video Processing for Emerging Multimedia Technology". These works address the emerging technology in signal processing and its new aspects, as well as the related applications. Recent developments in image/video-based deep learning technology have enabled new services in the field of multimedia and recognition technology. The applications vary and range from digital signal processing to image, video and multimedia signal processing, also including object classification, learning mechanism design and data security. Recent advances in numerical, theoretical and experimental methodologies are presented within the scope of the current book, along with the finding of new learning methods and new methodological developments and their limitations. This book brings together a collection of inter-/multidisciplinary works applied to many classification and data security applications in a coherent manner.

History of engineering & technology --- closed circuit television (CCTV) --- character order preserving --- cloud system --- privacy risk --- security --- video surveillance --- 3D --- depth map --- inter-component prediction --- MVD --- reversible data hiding --- texture --- wavelet analysis --- deep learning --- super-resolution --- deep neural architecture --- pattern mining --- multi-scale analysis --- reversible data hiding (RDH) --- image processing --- cloud computing --- public key cryptography (PKC) --- classification --- content-based image retrieval --- genetic algorithms --- image retrieval --- image classification --- Wiener-Granger causality --- block-compressive sensing (BCS) --- saliency --- error analysis --- flexible partitioning --- step-less adaptive sampling --- non-linear filters --- MCV and MLV filters --- de-noising --- noise removal --- edge preserving --- video coding --- motion estimation --- motion compensation --- affine motion model --- perspective motion model --- VVC --- quantization (signal) --- channel allocation --- scalable video coding --- convolution neural network --- scene recognition --- vector of locally aggregated descriptor --- weakly supervised attention map --- fire and smoke detection --- spatial and temporal --- wavelet transform --- coefficient of variation --- image steganalysis --- WOW --- UNIWARD --- ternary classification --- convolutional neural network (CNN) --- bayesian optimization --- gaussian process --- learning rate --- acauisition function --- machine learning --- moving object --- image stabilization --- object detection --- optical flow --- surveillance --- UAVs --- multiview high efficiency video coding --- ρ model --- bit allocation --- rate control --- image similarity --- frame complexity --- image deblurring --- generative adversarial network --- Wasserstein distance --- adversarial loss --- perceptual loss --- sentiment analysis --- social media --- lexicon --- image fusion --- multi-focus --- trimaps --- focus maps --- VisDrone2019 --- aerial imagery --- Faster R-CNN --- SSD --- RFCN --- YOLOv3 --- RetinaNet --- SNIPER --- CenterNet --- wrist-mounted DiverPAD --- electrical insulator --- capacitive touchscreen --- marine leisure activities --- convolutional neural networks --- pattern recognition --- low light --- image restoration --- denoise --- noise reduction --- deep leaning --- multiple feature --- dependency detection --- surveillance system --- closed circuit television (CCTV) --- character order preserving --- cloud system --- privacy risk --- security --- video surveillance --- 3D --- depth map --- inter-component prediction --- MVD --- reversible data hiding --- texture --- wavelet analysis --- deep learning --- super-resolution --- deep neural architecture --- pattern mining --- multi-scale analysis --- reversible data hiding (RDH) --- image processing --- cloud computing --- public key cryptography (PKC) --- classification --- content-based image retrieval --- genetic algorithms --- image retrieval --- image classification --- Wiener-Granger causality --- block-compressive sensing (BCS) --- saliency --- error analysis --- flexible partitioning --- step-less adaptive sampling --- non-linear filters --- MCV and MLV filters --- de-noising --- noise removal --- edge preserving --- video coding --- motion estimation --- motion compensation --- affine motion model --- perspective motion model --- VVC --- quantization (signal) --- channel allocation --- scalable video coding --- convolution neural network --- scene recognition --- vector of locally aggregated descriptor --- weakly supervised attention map --- fire and smoke detection --- spatial and temporal --- wavelet transform --- coefficient of variation --- image steganalysis --- WOW --- UNIWARD --- ternary classification --- convolutional neural network (CNN) --- bayesian optimization --- gaussian process --- learning rate --- acauisition function --- machine learning --- moving object --- image stabilization --- object detection --- optical flow --- surveillance --- UAVs --- multiview high efficiency video coding --- ρ model --- bit allocation --- rate control --- image similarity --- frame complexity --- image deblurring --- generative adversarial network --- Wasserstein distance --- adversarial loss --- perceptual loss --- sentiment analysis --- social media --- lexicon --- image fusion --- multi-focus --- trimaps --- focus maps --- VisDrone2019 --- aerial imagery --- Faster R-CNN --- SSD --- RFCN --- YOLOv3 --- RetinaNet --- SNIPER --- CenterNet --- wrist-mounted DiverPAD --- electrical insulator --- capacitive touchscreen --- marine leisure activities --- convolutional neural networks --- pattern recognition --- low light --- image restoration --- denoise --- noise reduction --- deep leaning --- multiple feature --- dependency detection --- surveillance system