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Author Joan Serrat; Felipe Lumbreras; Idoia Ruiz edit   pdf
url  openurl
  Title Learning to measure for preshipment garment sizing Type Journal Article
  Year 2018 Publication Measurement Abbreviated Journal MEASURE  
  Volume 130 Issue Pages 327-339  
  Keywords Apparel; Computer vision; Structured prediction; Regression  
  Abstract (up) Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.  
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  Notes ADAS; MSIAU; 600.122; 600.118 Approved no  
  Call Number Admin @ si @ SLR2018 Serial 3128  
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Author David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville edit   pdf
url  doi
openurl 
  Title A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Type Journal Article
  Year 2017 Publication Journal of Healthcare Engineering Abbreviated Journal JHCE  
  Volume Issue Pages 2040-2295  
  Keywords Colonoscopy images; Deep Learning; Semantic Segmentation  
  Abstract (up) Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.  
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  Notes ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118;MILAB Approved no  
  Call Number VBS2017b Serial 2940  
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Author Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari edit   pdf
url  openurl
  Title Training my car to see using virtual worlds Type Journal Article
  Year 2017 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 38 Issue Pages 102-118  
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  Abstract (up) Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks that it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting of training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ LVS2017 Serial 2985  
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Author Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez edit   pdf
doi  openurl
  Title Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models Type Journal Article
  Year 2020 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 128 Issue Pages 1505–1536  
  Keywords Procedural generation; Human action recognition; Synthetic data; Physics  
  Abstract (up) Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos.  
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  Notes ADAS; 600.124; 600.118;CIC Approved no  
  Call Number Admin @ si @ SGC2019 Serial 3303  
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Author Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez edit  url
doi  openurl
  Title Semantic Monocular Depth Estimation Based on Artificial Intelligence Type Journal Article
  Year 2020 Publication IEEE Intelligent Transportation Systems Magazine Abbreviated Journal ITSM  
  Volume 13 Issue 4 Pages 99-103  
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  Abstract (up) Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation.  
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  Notes ADAS; 600.124; 600.118 Approved no  
  Call Number Admin @ si @ GUH2019 Serial 3306  
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Author Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez edit   pdf
doi  openurl
  Title Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision Type Journal Article
  Year 2021 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 23 Issue 8 Pages 12738-12751  
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  Abstract (up) Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ GTS2021 Serial 3598  
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen edit  doi
openurl 
  Title Compact color texture description for texture classification Type Journal Article
  Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 51 Issue Pages 16-22  
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  Abstract (up) Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature.
However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This
gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive
evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7:8%, 4:3% and 5:0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively.		  
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  Notes LAMP; 600.068; 600.079;ADAS;CIC Approved no  
  Call Number Admin @ si @ KRW2015a Serial 2587  
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Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen edit   pdf
url  openurl
  Title Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification Type Journal Article
  Year 2018 Publication ISPRS Journal of Photogrammetry and Remote Sensing Abbreviated Journal ISPRS J  
  Volume 138 Issue Pages 74-85  
  Keywords Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis  
  Abstract (up) Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene  
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  Notes LAMP; 600.109; 600.106; 600.120;CIC;ADAS Approved no  
  Call Number Admin @ si @ RKW2018 Serial 3158  
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Author Alejandro Gonzalez Alzate; Zhijie Fang; Yainuvis Socarras; Joan Serrat; David Vazquez; Jiaolong Xu; Antonio Lopez edit   pdf
doi  openurl
  Title Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison Type Journal Article
  Year 2016 Publication Sensors Abbreviated Journal SENS  
  Volume 16 Issue 6 Pages 820  
  Keywords Pedestrian Detection; FIR  
  Abstract (up) Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and night time. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images, (b) just infrared images and (c) both of them. In order to obtain results for the last item we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset we have built for this purpose as well as on the publicly available KAIST multispectral dataset.  
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  ISSN 1424-8220 ISBN Medium  
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  Notes ADAS; 600.085; 600.076; 600.082; 601.281 Approved no  
  Call Number ADAS @ adas @ GFS2016 Serial 2754  
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Author Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores edit   pdf
doi  openurl
  Title On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts Type Journal Article
  Year 2017 Publication IEEE Transactions on cybernetics Abbreviated Journal Cyber  
  Volume 47 Issue 11 Pages 3980 - 3990  
  Keywords Multicue; multimodal; multiview; object detection  
  Abstract (up) Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient.  
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  ISSN 2168-2267 ISBN Medium  
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  Notes ADAS; 600.085; 600.082; 600.076; 600.118 Approved no  
  Call Number Admin @ si @ Serial 2810  
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