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Author |
Patricia Suarez; Dario Carpio; Angel Sappa; Henry Velesaca |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Transformer based Image Dehazing |
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Conference Article |
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Year |
2022 |
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16th IEEE International Conference on Signal Image Technology & Internet Based System |
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Keywords |
atmospheric light; brightness component; computational cost; dehazing quality; haze-free image |
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This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models. |
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Dijon; France; October 2022 |
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MSIAU; no proj |
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Admin @ si @ SCS2022 |
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3803 |
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Author |
Angel Sappa; Patricia Suarez; Henry Velesaca; Dario Carpio |
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Title |
Domain Adaptation in Image Dehazing: Exploring the Usage of Images from Virtual Scenarios |
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Conference Article |
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2022 |
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16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing |
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85-92 |
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Domain adaptation; Synthetic hazed dataset; Dehazing |
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This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image dehazing
problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator; these synthetic
images contain different densities of haze, which are used for training the model that is later adapted to any real scenario.
The adaptation process requires just a few images to fine-tune the model parameters. The proposed strategy allows
overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements
the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite
difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised
way dehazing algorithms. Experimental results are provided showing the validity of the proposed domain adaptation
strategy. |
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Lisboa; Portugal; July 2022 |
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CGVCVIP |
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MSIAU; no proj |
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no |
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Admin @ si @ SSV2022 |
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3804 |
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Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
![goto web page (via DOI) doi](img/doi.gif)
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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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Year |
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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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 |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Image and Video Enhancement |
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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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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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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 |
Alex Falcon; Swathikiran Sudhakaran; Giuseppe Serra; Sergio Escalera; Oswald Lanz |
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Title |
Relevance-based Margin for Contrastively-trained Video Retrieval Models |
Type |
Conference Article |
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Year |
2022 |
Publication |
ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval |
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146-157 |
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Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries to web-scale video search. Learning the cross-similarity of video and text in a joint embedding space is the dominant approach. To do so, a contrastive loss is usually employed because it organizes the embedding space by putting similar items close and dissimilar items far. This framework leads to competitive recall rates, as they solely focus on the rank of the groundtruth items. Yet, assessing the quality of the ranking list is of utmost importance when considering intelligent retrieval systems, since multiple items may share similar semantics, hence a high relevance. Moreover, the aforementioned framework uses a fixed margin to separate similar and dissimilar items, treating all non-groundtruth items as equally irrelevant. In this paper we propose to use a variable margin: we argue that varying the margin used during training based on how much relevant an item is to a given query, i.e. a relevance-based margin, easily improves the quality of the ranking lists measured through nDCG and mAP. We demonstrate the advantages of our technique using different models on EPIC-Kitchens-100 and YouCook2. We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance. Finally, extensive ablation studies and qualitative analysis support the robustness of our approach. Code will be released at \urlhttps://github.com/aranciokov/RelevanceMargin-ICMR22. |
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Newwark, NJ, USA, 27 June 2022 |
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ICMR |
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HuPBA; no menciona |
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no |
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Admin @ si @ FSS2022 |
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3808 |
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Author |
Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds) |
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Title |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 |
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2022 |
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Frontiers in Handwriting Recognition. |
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13639 |
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ICFHR 2022, Hyderabad, India, December 4–7, 2022 |
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Springer |
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Utkarsh Porwal; Alicia Fornes; Faisal Shafait |
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LNCS |
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978-3-031-21648-0 |
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ICFHR |
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DAG |
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no |
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Admin @ si @ PFS2022 |
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3809 |
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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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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 |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
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Title |
Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project |
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Journal Article |
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2023 |
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International Journal of Computer Assisted Radiology and Surgery |
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IJCARS |
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IAM |
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Admin @ si @ TGM2023 |
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3830 |
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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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2022 |
Publication |
ICT Applications for Smart Cities |
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224 |
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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 |
Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu |
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Title |
Waste Classification with Small Datasets and Limited Resources |
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Book Chapter |
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2022 |
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ICT Applications for Smart Cities. Intelligent Systems Reference Library |
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224 |
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185-203 |
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Automatic waste recycling has become a very important societal challenge nowadays, raising people’s awareness for a cleaner environment and a more sustainable lifestyle. With the transition to Smart Cities, and thanks to advanced ICT solutions, this problem has received a new impulse. The waste recycling focus has shifted from general waste treating facilities to an individual responsibility, where each person should become aware of selective waste separation. The surge of the mobile devices, accompanied by a significant increase in computation power, has potentiated and facilitated this individual role. An automated image-based waste classification mechanism can help with a more efficient recycling and a reduction of contamination from residuals. Despite the good results achieved with the deep learning methodologies for this task, the Achille’s heel is that they require large neural networks which need significant computational resources for training and therefore are not suitable for mobile devices. To circumvent this apparently intractable problem, we will rely on knowledge distillation in order to transfer the network’s knowledge from a larger network (called ‘teacher’) to a smaller, more compact one, (referred as ‘student’) and thus making it possible the task of image classification on a device with limited resources. For evaluation, we considered as ‘teachers’ large architectures such as InceptionResNet or DenseNet and as ‘students’, several configurations of the MobileNets. We used the publicly available TrashNet dataset to demonstrate that the distillation process does not significantly affect system’s performance (e.g. classification accuracy) of the student network. |
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September 2022 |
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Springer |
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978-3-031-06306-0 |
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3813 |
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Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method |
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2022 |
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Arxiv |
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We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. This objective encourages local neighborhood features in feature space to have similar predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. Code is available in this https URL. |
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LAMP; 600.147 |
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Admin @ si @ YWW2022b |
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3815 |
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Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados |
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Classification of aesthetic natural scene images using statistical and semantic features |
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2023 |
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Multimedia Tools and Applications |
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MTAP |
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82 |
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13507-13532 |
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Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images. |
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DAG |
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Admin @ si @ BSP2023 |
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3873 |
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Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados |
![goto web page url](img/www.gif)
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I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation |
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2023 |
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21st International Graphonomics Conference |
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136–148 |
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During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation. |
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Evora; Portugal; October 2023 |
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Admin @ si @ BPG2023 |
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3838 |
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