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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 |
Type |
Book Chapter |
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Year |
2022 |
Publication |
ICT Applications for Smart Cities |
Abbreviated Journal |
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Volume |
224 |
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Pages |
101-121 |
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Abstract |
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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no |
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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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Volume |
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 |
Serial |
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 |
Type |
Journal Article |
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Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
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Volume |
10 |
Issue |
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Pages |
68281-68290 |
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Abstract |
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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MSIAU; MACO; 600.160; 600.167 |
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no |
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Call Number |
Admin @ si @ SPS2022 |
Serial |
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 |
Type |
Journal Article |
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Year |
2022 |
Publication |
Journal of Archaeological Science |
Abbreviated Journal |
JArchSci |
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Volume |
148 |
Issue |
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Pages |
105654 |
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Abstract |
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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no |
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Call Number |
Admin @ si @ BOL2022 |
Serial |
3753 |
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Author |
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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Title |
Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 |
Type |
Conference Article |
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Year |
2022 |
Publication |
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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no |
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Call Number |
Admin @ si @ RMV2022 |
Serial |
3774 |
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Author |
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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Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2022 |
Type |
Conference Article |
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Year |
2022 |
Publication |
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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no |
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Call Number |
Admin @ si @ RSV2022c |
Serial |
3775 |
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Author |
Patricia Suarez; Angel Sappa; Dario Carpio; Henry Velesaca; Francisca Burgos; Patricia Urdiales |
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Title |
Deep Learning Based Shrimp Classification |
Type |
Conference Article |
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Year |
2022 |
Publication |
17th International Symposium on Visual Computing |
Abbreviated Journal |
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Volume |
13598 |
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36–45 |
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Keywords |
Pigmentation; Color space; Light weight network |
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This work proposes a novel approach based on deep learning to address the classification of shrimp (Pennaeus vannamei) into two classes, according to their level of pigmentation accepted by shrimp commerce. The main goal of this actual study is to support the shrimp industry in terms of price and process. An efficient CNN architecture is proposed to perform image classification through a program that could be set other in mobile devices or in fixed support in the shrimp supply chain. The proposed approach is a lightweight model that uses HSV color space shrimp images. A simple pipeline shows the most important stages performed to determine a pattern that identifies the class to which they belong based on their pigmentation. For the experiments, a database acquired with mobile devices of various brands and models has been used to capture images of shrimp. The results obtained with the images in the RGB and HSV color space allow for testing the effectiveness of the proposed model. |
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ISVC |
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Notes |
MSIAU; no proj |
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no |
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Call Number |
Admin @ si @ SAC2022 |
Serial |
3772 |
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Author |
Patricia Suarez; Dario Carpio; Angel Sappa; Henry Velesaca |
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Title |
Transformer based Image Dehazing |
Type |
Conference Article |
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Year |
2022 |
Publication |
16th IEEE International Conference on Signal Image Technology & Internet Based System |
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atmospheric light; brightness component; computational cost; dehazing quality; haze-free image |
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Abstract |
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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SITIS |
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MSIAU; no proj |
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no |
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Call Number |
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 |
Type |
Conference Article |
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Year |
2022 |
Publication |
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 |
Francesc Tanarro Marquez; Pau Gratacos Marti; F. Javier Sanchez; Joan Ramon Jimenez Minguell; Coen Antens; Enric Sala i Esteva |
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Title |
A device for monitoring condition of a railway supply |
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Patent |
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2012 |
Publication |
EP 2 404 777 A1 |
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of a railway supply line when the supply line is in contact with a head of a pantograph of a vehicle in order to power said vehicle . The device includes a camera ( for monitoring parameters indicative of operating capability of said supply line.
The device is intended to monitor condition
tive of operating capability of said supply line. The device includes a reflective element. comprising a pattern , intended to be arranged onto the pantograph head . The camera is intended to be arranged on the vehicle (10) so as to register the pattern position regarding a vertical direction. |
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ALSTOM Transport SA |
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European Patent Office |
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MV |
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IAM @ iam @ MMS2012 |
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1854 |
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Author |
Onur Ferhat |
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Title |
Eye-Tracking with Webcam-Based Setups: Implementation of a Real-Time System and an Analysis of Factors Affecting Performance |
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Report |
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2012 |
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CVC Technical Report |
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172 |
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Computer vision, eye-tracking, gaussian process, feature selection, optical flow |
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In the recent years commercial eye-tracking hardware has become more common, with the introduction of new models from several brands that have better performance and easier setup procedures. A cause and at the same time a result of this phenomenon is the popularity of eye-tracking research directed at marketing, accessibility and usability, among others.
One problem with these hardware components is scalability, because both the price and the necessary expertise to operate them makes it practically impossible in the large scale. In this work, we analyze the feasibility of a software eye-tracking system based on a single, ordinary webcam. Our aim is to discover the limits of such a system and to see whether it provides acceptable performances.
The significance of this setup is that it is the most common setup found in consumer environments, off-the-shelf electronic devices such as laptops, mobile phones and tablet computers. As no special equipment such as infrared lights, mirrors or zoom lenses are used; setting up and calibrating the system is easier compared to other approaches using these components.
Our work is based on the open source application Opengazer, which provides a good starting point for our contributions. We propose several improvements in order to push the system's performance further and make it feasible as a robust, real-time device. Then we carry out an elaborate experiment involving 18 human subjects and 4 different system setups. Finally, we give an analysis of the results and discuss the effects of setup changes, subject differences and modifications in the software. |
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Address |
Bellaterra |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
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Editor |
Fernando Vilariño |
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MV |
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no |
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Admin @ si @ Fer2012; IAM @ iam @ Fer2012 |
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2165 |
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Author |
Jorge Bernal |
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Title |
Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Colorectal cancer is the fourth most common cause of cancer death worldwide and its survival rate depends on the stage in which it is detected on hence the necessity for an early colon screening. There are several screening techniques but colonoscopy is still nowadays the gold standard, although it has some drawbacks such as the miss rate. Our contribution, in the field of intelligent systems for colonoscopy, aims at providing a polyp localization and a polyp segmentation system based on a model of appearance for polyps. To develop both methods we define a model of appearance for polyps, which describes a polyp as enclosed by intensity valleys. The novelty of our contribution resides on the fact that we include in our model aspects of the image formation and we also consider the presence of other elements from the endoluminal scene such as specular highlights and blood vessels, which have an impact on the performance of our methods. In order to develop our polyp localization method we accumulate valley information in order to generate energy maps, which are also used to guide the polyp segmentation. Our methods achieve promising results in polyp localization and segmentation. As we want to explore the usability of our methods we present a comparative analysis between physicians fixations obtained via an eye tracking device and our polyp localization method. The results show that our method is indistinguishable to novice physicians although it is far from expert physicians. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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F. Javier Sanchez;Fernando Vilariño |
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Admin @ si @ Ber2012 |
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2211 |
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Author |
Mirko Arnold; Anarta Ghosh; Stephen Ameling; G Lacey |
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Automatic segmentation and inpainting of specular highlights for endoscopic imaging |
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2010 |
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EURASIP Journal on Image and Video Processing |
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EURASIP JIVP |
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2010 |
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9 |
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fernando @ fernando @ |
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2423 |
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Mirko Arnold; Anarta Ghosh; Gerard Lacey; Stephen Patchett; Hugh Mulcahy |
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Indistinct frame detection in colonoscopy videos |
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2009 |
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Machine Vision and Image Processing Conference |
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47-52 |
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fernando @ fernando @ |
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2424 |
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Mirko Arnold; Stephan Ameling; Anarta Ghosh; Gerard Lacey |
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Title |
Quality Improvement of Endoscopy Videos |
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2011 |
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Proceedings of the 8th IASTED International Conference on Biomedical Engineering |
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723 |
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no |
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Call Number |
fernando @ fernando @ |
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2426 |
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