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Isabelle Guyon; Lisheng Sun Hosoya; Marc Boulle; Hugo Jair Escalante; Sergio Escalera; Zhengying Liu; Damir Jajetic; Bisakha Ray; Mehreen Saeed; Michele Sebag; Alexander R.Statnikov; Wei-Wei Tu; Evelyne Viegas |
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Title |
Analysis of the AutoML Challenge Series 2015-2018. |
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Book Chapter |
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Year |
2019 |
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Automated Machine Learning |
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177-219 |
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The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most of the numerical analyses and plots.) (NIPS 2015 – ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed bya one-round AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn. All materials discussed in this chapter (data and code) have been made publicly available at http://automl.chalearn.org/. |
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HuPBA; no proj |
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Admin @ si @ GHB2019 |
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3330 |
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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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SLCV |
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978-3-031-00698-2 |
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MSIAU; MACO |
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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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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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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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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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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 |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
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Title |
Face Presentation Attack Detection (PAD) Challenges |
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Book Chapter |
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Year |
2023 |
Publication |
Advances in Face Presentation Attack Detection |
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17–35 |
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In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges. |
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SLCV |
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HUPBA |
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no |
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Admin @ si @ WGE2023b |
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3956 |
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Author |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
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Title |
Face Anti-spoofing Progress Driven by Academic Challenges |
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Book Chapter |
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Year |
2023 |
Publication |
Advances in Face Presentation Attack Detection |
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1–15 |
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With the ubiquity of facial authentication systems and the prevalence of security cameras around the world, the impact that facial presentation attack techniques may have is huge. However, research progress in this field has been slowed by a number of factors, including the lack of appropriate and realistic datasets, ethical and privacy issues that prevent the recording and distribution of facial images, the little attention that the community has given to potential ethnic biases among others. This chapter provides an overview of contributions derived from the organization of academic challenges in the context of face anti-spoofing detection. Specifically, we discuss the limitations of benchmarks and summarize our efforts in trying to boost research by the community via the participation in academic challenges |
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SLCV |
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HUPBA |
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no |
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Admin @ si @ WGE2023c |
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3957 |
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Author |
Victor Campmany; Sergio Silva; Juan Carlos Moure; Antoni Espinosa; David Vazquez; Antonio Lopez |
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Title |
GPU-based pedestrian detection for autonomous driving |
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Abstract |
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Year |
2015 |
Publication |
Programming and Tunning Massive Parallel Systems |
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PUMPS |
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Autonomous Driving; ADAS; CUDA; Pedestrian Detection |
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Pedestrian detection for autonomous driving has gained a lot of prominence during the last few years. Besides the fact that it is one of the hardest tasks within computer vision, it involves huge computational costs. The real-time constraints in the field are tight, and regular processors are not able to handle the workload obtaining an acceptable ratio of frames per second (fps). Moreover, multiple cameras are required to obtain accurate results, so the need to speed up the process is even higher. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system. Further, we introduce significant algorithmic adjustments and optimizations to adapt the problem to the GPU architecture. The aim is to provide a system capable of running in real-time obtaining reliable results. |
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Barcelona; Spain |
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PUMPS |
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PUMPS |
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ADAS; 600.076; 600.082; 600.085 |
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no |
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ADAS @ adas @ CSM2015 |
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2644 |
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Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Opponent Colors for Human Detection |
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Conference Article |
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2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
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6669 |
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363-370 |
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Pedestrian Detection; Color; Part Based Models |
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Abstract |
Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. |
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Las Palmas de Gran Canaria. Spain |
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Springer |
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Berlin Heidelberg |
Editor |
J. Vitria; J.M. Sanches; M. Hernandez |
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English |
Summary Language |
English |
Original Title |
Opponent Colors for Human Detection |
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Lecture Notes on Computer Science |
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LNCS |
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0302-9743 |
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978-3-642-21256-7 |
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IbPRIA |
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ADAS |
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no |
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ADAS @ adas @ RVL2011a |
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1666 |
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Author |
Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions |
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Book Chapter |
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2006 |
Publication |
9th International Conference on Medical Image Computing and Computer–Assisted Intervention |
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4191 |
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161–168 |
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Abstract |
Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions. |
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Copenhagen (Denmark) |
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Springer Verlag |
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Berlin Heidelberg |
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R. Larsen, M. Nielsen, and J. Sporring |
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LNCS |
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800 |
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MICCAI06 |
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MV;OR;MILAB;SIAI |
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BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006 |
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725 |
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Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; C. Malagelada; Petia Radeva |
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Title |
Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions |
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2006 |
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11th Iberoamerican Congress on Pattern Recognition |
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4225 |
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178–187 |
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This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns. |
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Cancun (Mexico) |
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Springer Verlag |
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Berlin Heidelberg |
Editor |
.F. Mart ́ınez-Trinidad et al |
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LNCS |
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800 |
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MV;OR;MILAB;SIAI |
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no |
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BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f |
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728 |
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