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Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla |
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Title |
Thermal Image Super-Resolution: A Novel Unsupervised Approach |
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Conference Article |
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Year |
2022 |
Publication |
International Joint Conference on Computer Vision, Imaging and Computer Graphics |
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1474 |
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495–506 |
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This paper proposes the use of a CycleGAN architecture for thermal image super-resolution under a transfer domain strategy, where middle-resolution images from one camera are transferred to a higher resolution domain of another camera. The proposed approach is trained with a large dataset acquired using three thermal cameras at different resolutions. An unsupervised learning process is followed to train the architecture. Additional loss function is proposed trying to improve results from the state of the art approaches. Following the first thermal image super-resolution challenge (PBVS-CVPR2020) evaluations are performed. A comparison with previous works is presented showing the proposed approach reaches the best results. |
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VISIGRAPP |
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MSIAU; 600.130 |
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no |
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Call Number |
Admin @ si @ RSV2022d |
Serial |
3776 |
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Author |
Jorge Charco; Angel Sappa; Boris X. Vintimilla |
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Title |
Human Pose Estimation through a Novel Multi-view Scheme |
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Conference Article |
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Year |
2022 |
Publication |
17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) |
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5 |
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855-862 |
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Multi-view Scheme; Human Pose Estimation; Relative Camera Pose; Monocular Approach |
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This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human pose estimation problem. The proposed approach first obtains the human body joints of a set of images, which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and
comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements in the accuracy of body joints estimations. |
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On line; Feb 6, 2022 – Feb 8, 2022 |
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Edition |
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ISSN |
2184-4321 |
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978-989-758-555-5 |
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VISAPP |
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MSIAU; 600.160 |
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no |
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Call Number |
Admin @ si @ CSV2022 |
Serial |
3689 |
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Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla |
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Title |
Multi-Image Super-Resolution for Thermal Images |
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Conference Article |
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Year |
2022 |
Publication |
17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) |
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4 |
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635-642 |
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Keywords |
Thermal Images; Multi-view; Multi-frame; Super-Resolution; Deep Learning; Attention Block |
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This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two
attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches. |
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Online; Feb 6-8, 2022 |
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VISAPP |
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MSIAU; 601.349 |
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no |
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Admin @ si @ RSV2022a |
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3690 |
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Author |
Henry Velesaca; Patricia Suarez; Angel Sappa; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez |
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Title |
Review on Common Techniques for Urban Environment Video Analytics |
Type |
Conference Article |
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Year |
2022 |
Publication |
Anais do III Workshop Brasileiro de Cidades Inteligentes |
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Pages |
107-118 |
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Keywords |
Video Analytics; Review; Urban Environments; Smart Cities |
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Abstract |
This work compiles the different computer vision-based approaches
from the state-of-the-art intended for video analytics in urban environments.
The manuscript groups the different approaches according to the typical modules present in video analysis, including image preprocessing, object detection,
classification, and tracking. This proposed pipeline serves as a basic guide to
representing these most representative approaches in this topic of video analysis
that will be addressed in this work. Furthermore, the manuscript is not intended
to be an exhaustive review of the most advanced approaches, but only a list of
common techniques proposed to address recurring problems in this field. |
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WBCI |
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MSIAU; 601.349 |
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no |
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Admin @ si @ VSS2022 |
Serial |
3773 |
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Author |
Daniela Rato; Miguel Oliveira; Vitor Santos; Manuel Gomes; Angel Sappa |
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Title |
A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells |
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Journal Article |
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Year |
2022 |
Publication |
Journal of Manufacturing Systems |
Abbreviated Journal |
JMANUFSYST |
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64 |
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Pages |
497-507 |
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Keywords |
Calibration; Collaborative cell; Multi-modal; Multi-sensor |
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Abstract |
Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensure this, collaborative cells are equipped with a large set of sensors of multiple modalities, covering the entire work volume. However, the fusion of information from all these sensors requires an accurate extrinsic calibration. The calibration of such complex systems is challenging, due to the number of sensors and modalities, and also due to the small overlapping fields of view between the sensors, which are positioned to capture different viewpoints of the cell. This paper proposes a sensor to pattern methodology that can calibrate a complex system such as a collaborative cell in a single optimization procedure. Our methodology can tackle RGB and Depth cameras, as well as LiDARs. Results show that our methodology is able to accurately calibrate a collaborative cell containing three RGB cameras, a depth camera and three 3D LiDARs. |
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Science Direct |
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MSIAU; MACO |
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no |
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Call Number |
Admin @ si @ ROS2022 |
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3750 |
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Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Cross-Spectral Image Processing |
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Book Chapter |
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Year |
2022 |
Publication |
Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
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23-34 |
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Although this book is on IR computer vision and its main focus lies on IR image and video processing and analysis, a special attention is dedicated to cross-spectral image processing due to the increasing number of publications and applications in this domain. In these cross-spectral frameworks, IR information is used together with information from other spectral bands to tackle some specific problems by developing more robust solutions. Tasks considered for cross-spectral processing are for instance dehazing, segmentation, vegetation index estimation, or face recognition. This increasing number of applications is motivated by cross- and multi-spectral camera setups available already on the market like for example smartphones, remote sensing multispectral cameras, or multi-spectral cameras for automotive systems or drones. In this chapter, different cross-spectral image processing techniques will be reviewed together with possible applications. Initially, image registration approaches for the cross-spectral case are reviewed: the registration stage is the first image processing task, which is needed to align images acquired by different sensors within the same reference coordinate system. Then, recent cross-spectral image colorization approaches, which are intended to colorize infrared images for different applications are presented. Finally, the cross-spectral image enhancement problem is tackled by including guided super resolution techniques, image dehazing approaches, cross-spectral filtering and edge detection. Figure 3.1 illustrates cross-spectral image processing stages as well as their possible connections. Table 3.1 presents some of the available public cross-spectral datasets generally used as reference data to evaluate cross-spectral image registration, colorization, enhancement, or exploitation results. |
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Springer |
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SLCV |
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978-3-031-00698-2 |
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MSIAU; MACO |
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no |
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Admin @ si @ TSH2022b |
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3805 |
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Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Detection, Classification, and Tracking |
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Book Chapter |
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2022 |
Publication |
Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
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35-58 |
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Automatic image and video exploitation or content analysis is a technique to extract higher-level information from a scene such as objects, behavior, (inter-)actions, environment, or even weather conditions. The relevant information is assumed to be contained in the two-dimensional signal provided in an image (width and height in pixels) or the three-dimensional signal provided in a video (width, height, and time). But also intermediate-level information such as object classes [196], locations [197], or motion [198] can help applications to fulfill certain tasks such as intelligent compression [199], video summarization [200], or video retrieval [201]. Usually, videos with their temporal dimension are a richer source of data compared to single images [202] and thus certain video content can be extracted from videos only such as object motion or object behavior. Often, machine learning or nowadays deep learning techniques are utilized to model prior knowledge about object or scene appearance using labeled training samples [203, 204]. After a learning phase, these models are then applied in real world applications, which is called inference. |
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Springer |
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SLCV |
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978-3-031-00698-2 |
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MSIAU; MACO |
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no |
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Admin @ si @ TSH2022c |
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3806 |
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Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Image and Video Enhancement |
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Book Chapter |
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2022 |
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Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
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9-21 |
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Image and video enhancement aims at improving the signal quality relative to imaging artifacts such as noise and blur or atmospheric perturbations such as turbulence and haze. It is usually performed in order to assist humans in analyzing image and video content or simply to present humans visually appealing images and videos. However, image and video enhancement can also be used as a preprocessing technique to ease the task and thus improve the performance of subsequent automatic image content analysis algorithms: preceding dehazing can improve object detection as shown by [23] or explicit turbulence modeling can improve moving object detection as discussed by [24]. But it remains an open question whether image and video enhancement should rather be performed explicitly as a preprocessing step or implicitly for example by feeding affected images directly to a neural network for image content analysis like object detection [25]. Especially for real-time video processing at low latency it can be better to handle image perturbation implicitly in order to minimize the processing time of an algorithm. This can be achieved by making algorithms for image content analysis robust or even invariant to perturbations such as noise or blur. Additionally, mistakes of an individual preprocessing module can obviously affect the quality of the entire processing pipeline. |
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Springer |
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SLCV |
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MSIAU; MACO |
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no |
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Admin @ si @ TSH2022a |
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3807 |
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Author |
Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca |
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Title |
Human Body Pose Estimation in Multi-view Environments |
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Book Chapter |
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Year |
2022 |
Publication |
ICT Applications for Smart Cities. Intelligent Systems Reference Library |
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224 |
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79-99 |
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This chapter tackles the challenging problem of human pose estimation in multi-view environments to handle scenes with self-occlusions. The proposed approach starts by first estimating the camera pose—extrinsic parameters—in multi-view scenarios; due to few real image datasets, different virtual scenes are generated by using a special simulator, for training and testing the proposed convolutional neural network based approaches. Then, these extrinsic parameters are used to establish the relation between different cameras into the multi-view scheme, which captures the pose of the person from different points of view at the same time. The proposed multi-view scheme allows to robustly estimate human body joints’ position even in situations where they are occluded. This would help to avoid possible false alarms in behavioral analysis systems of smart cities, as well as applications for physical therapy, safe moving assistance for the elderly among other. The chapter concludes by presenting experimental results in real scenes by using state-of-the-art and the proposed multi-view approaches. |
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September 2022 |
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Springer |
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ISRL |
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978-3-031-06306-0 |
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MSIAU; MACO |
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no |
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Admin @ si @ CSV2022b |
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3810 |
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Author |
Henry Velesaca; Patricia Suarez; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez; Angel Morera |
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Title |
Video Analytics in Urban Environments: Challenges and Approaches |
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Book Chapter |
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Year |
2022 |
Publication |
ICT Applications for Smart Cities |
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224 |
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101-121 |
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This chapter reviews state-of-the-art approaches generally present in the pipeline of video analytics on urban scenarios. A typical pipeline is used to cluster approaches in the literature, including image preprocessing, object detection, object classification, and object tracking modules. Then, a review of recent approaches for each module is given. Additionally, applications and datasets generally used for training and evaluating the performance of these approaches are included. This chapter does not pretend to be an exhaustive review of state-of-the-art video analytics in urban environments but rather an illustration of some of the different recent contributions. The chapter concludes by presenting current trends in video analytics in the urban scenario field. |
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September 2022 |
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Springer |
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ISRL |
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978-3-031-06306-0 |
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MSIAU; MACO |
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Admin @ si @ VSC2022 |
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3811 |
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Author |
Angel Sappa (ed) |
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Title |
ICT Applications for Smart Cities |
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Book Whole |
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Year |
2022 |
Publication |
ICT Applications for Smart Cities |
Abbreviated Journal |
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224 |
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Keywords |
Computational Intelligence; Intelligent Systems; Smart Cities; ICT Applications; Machine Learning; Pattern Recognition; Computer Vision; Image Processing |
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Abstract |
Part of the book series: Intelligent Systems Reference Library (ISRL)
This book is the result of four-year work in the framework of the Ibero-American Research Network TICs4CI funded by the CYTED program. In the following decades, 85% of the world's population is expected to live in cities; hence, urban centers should be prepared to provide smart solutions for problems ranging from video surveillance and intelligent mobility to the solid waste recycling processes, just to mention a few. More specifically, the book describes underlying technologies and practical implementations of several successful case studies of ICTs developed in the following smart city areas:
• Urban environment monitoring
• Intelligent mobility
• Waste recycling processes
• Video surveillance
• Computer-aided diagnose in healthcare systems
• Computer vision-based approaches for efficiency in production processes
The book is intended for researchers and engineers in the field of ICTs for smart cities, as well as to anyone who wants to know about state-of-the-art approaches and challenges on this field. |
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September 2022 |
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Springer |
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Angel Sappa |
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ISRL |
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978-3-031-06306-0 |
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MSIAU; MACO |
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no |
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Admin @ si @ Sap2022 |
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3812 |
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Author |
Xavier Soria; Gonzalo Pomboza-Junez; Angel Sappa |
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Title |
LDC: Lightweight Dense CNN for Edge Detection |
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Journal Article |
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2022 |
Publication |
IEEE Access |
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ACCESS |
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10 |
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68281-68290 |
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This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC |
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27 June 2022 |
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IEEE |
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Notes |
MSIAU; MACO; 600.160; 600.167 |
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no |
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Admin @ si @ SPS2022 |
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3751 |
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Author |
Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey |
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Title |
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms |
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Journal Article |
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Year |
2022 |
Publication |
Journal of Archaeological Science |
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JArchSci |
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148 |
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105654 |
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This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. |
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December 2022 |
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MSIAU; MACO; 600.167 |
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Admin @ si @ BOL2022 |
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3753 |
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Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim |
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Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 |
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2022 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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390-398 |
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Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers |
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This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. |
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New Orleans; USA; June 2022 |
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CVPRW |
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MSIAU; no menciona |
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Admin @ si @ RMV2022 |
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3774 |
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Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Jin Kim; Dogun Kim; Zhihao Li; Yingchun Jian; Bo Yan; Leilei Cao; Fengliang Qi; Hongbin Wang Rongyuan Wu; Lingchen Sun; Yongqiang Zhao; Lin Li; Kai Wang; Yicheng Wang; Xuanming Zhang; Huiyuan Wei; Chonghua Lv; Qigong Sun; Xiaolin Tian; Zhuang Jia; Jiakui Hu; Chenyang Wang; Zhiwei Zhong; Xianming Liu; Junjun Jiang |
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Thermal Image Super-Resolution Challenge Results – PBVS 2022 |
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2022 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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418-426 |
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This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic. |
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New Orleans; USA; June 2022 |
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CVPRW |
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MSIAU; no menciona |
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Admin @ si @ RSV2022c |
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3775 |
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