| Records |
| Author |
Cristhian A. Aguilera-Carrasco; C. Aguilera; Angel Sappa |
| Title |
Melamine Faced Panels Defect Classification beyond the Visible Spectrum |
Type |
Journal Article |
| Year |
2018 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
18 |
Issue |
11 |
Pages |
1-10 |
| Keywords |
industrial application; infrared; machine learning |
| Abstract |
In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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MSIAU; 600.122 |
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no |
| Call Number |
Admin @ si @ AAS2018 |
Serial |
3191 |
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| Author |
Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa |
| Title |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
Type |
Journal Article |
| Year |
2020 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
20 |
Issue |
11 |
Pages |
3249 |
| Keywords |
stereo matching; deep learning; embedded GPU |
| Abstract |
Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices. |
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MSIAU; 600.122 |
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no |
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Admin @ si @ AAN2020 |
Serial |
3428 |
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| Author |
Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras |
| Title |
Segmentation of aerial images for plausible detail synthesis |
Type |
Journal Article |
| Year |
2018 |
Publication |
Computers & Graphics |
Abbreviated Journal |
CG |
| Volume |
71 |
Issue |
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Pages |
23-34 |
| Keywords |
Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation |
| Abstract |
The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. |
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0097-8493 |
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MSIAU; 600.086; 600.118 |
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no |
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Admin @ si @ ACC2018 |
Serial |
3147 |
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| Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
| Title |
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution |
Type |
Journal Article |
| Year |
2022 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
22 |
Issue |
6 |
Pages |
2254 |
| Keywords |
Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images |
| Abstract |
This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. |
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MSIAU; |
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no |
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Admin @ si @ RSV2022b |
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3688 |
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| Author |
Armin Mehri; Parichehr Behjati; Angel Sappa |
| Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
11 |
Issue |
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Pages |
11529-11540 |
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| Abstract |
Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to 0.19∼2.3dB , while it is memory efficient. |
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MSIAU |
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no |
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Admin @ si @ MBS2023 |
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3876 |
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| Author |
Armin Mehri; Parichehr Behjati; Dario Carpio; Angel Sappa |
| Title |
SRFormer: Efficient Yet Powerful Transformer Network for Single Image Super Resolution |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
11 |
Issue |
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Pages |
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Recent breakthroughs in single image super resolution have investigated the potential of deep Convolutional Neural Networks (CNNs) to improve performance. However, CNNs based models suffer from their limited fields and their inability to adapt to the input content. Recently, Transformer based models were presented, which demonstrated major performance gains in Natural Language Processing and Vision tasks while mitigating the drawbacks of CNNs. Nevertheless, Transformer computational complexity can increase quadratically for high-resolution images, and the fact that it ignores the original structures of the image by converting them to the 1D structure can make it problematic to capture the local context information and adapt it for real-time applications. In this paper, we present, SRFormer, an efficient yet powerful Transformer-based architecture, by making several key designs in the building of Transformer blocks and Transformer layers that allow us to consider the original structure of the image (i.e., 2D structure) while capturing both local and global dependencies without raising computational demands or memory consumption. We also present a Gated Multi-Layer Perceptron (MLP) Feature Fusion module to aggregate the features of different stages of Transformer blocks by focusing on inter-spatial relationships while adding minor computational costs to the network. We have conducted extensive experiments on several super-resolution benchmark datasets to evaluate our approach. SRFormer demonstrates superior performance compared to state-of-the-art methods from both Transformer and Convolutional networks, with an improvement margin of 0.1∼0.53dB . Furthermore, while SRFormer has almost the same model size, it outperforms SwinIR by 0.47% and inference time by half the time of SwinIR. The code will be available on GitHub. |
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MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ MBC2023 |
Serial |
3887 |
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| Author |
Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Aftab Alam; Rosie Campbell; Petrus J Gerrits; Jonas Gregorio de Souza; Afifa Khan; Maria Suarez Moreno; Jack Tomaney; Rebecca C Roberts; Cameron A Petrie |
| Title |
Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan |
Type |
Journal Article |
| Year |
2023 |
Publication |
Scientific Reports |
Abbreviated Journal |
ScR |
| Volume |
13 |
Issue |
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Pages |
11257 |
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| Abstract |
This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map. |
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MSIAU |
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no |
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Admin @ si @ BOL2023 |
Serial |
3976 |
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| Author |
Xavier Soria; Angel Sappa; Patricio Humanante; Arash Akbarinia |
| Title |
Dense extreme inception network for edge detection |
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Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
139 |
Issue |
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Pages |
109461 |
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Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs. |
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MSIAU |
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no |
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Admin @ si @ SSH2023 |
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3982 |
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| Author |
Henry Velesaca; Gisel Bastidas-Guacho; Mohammad Rouhani; Angel Sappa |
| Title |
Multimodal image registration techniques: a comprehensive survey |
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Journal Article |
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2024 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
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This manuscript presents a review of state-of-the-art techniques proposed in the literature for multimodal image registration, addressing instances where images from different modalities need to be precisely aligned in the same reference system. This scenario arises when the images to be registered come from different modalities, among the visible and thermal spectral bands, 3D-RGB, or flash-no flash, or NIR-visible. The review spans different techniques from classical approaches to more modern ones based on deep learning, aiming to highlight the particularities required at each step in the registration pipeline when dealing with multimodal images. It is noteworthy that medical images are excluded from this review due to their specific characteristics, including the use of both active and passive sensors or the non-rigid nature of the body contained in the image. |
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MSIAU |
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no |
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Admin @ si @ VBR2024 |
Serial |
3997 |
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| Author |
Patricia Suarez; Dario Carpio; Angel Sappa |
| Title |
Enhancement of guided thermal image super-resolution approaches |
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Journal Article |
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2024 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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573 |
Issue |
127197 |
Pages |
1-17 |
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Guided image processing techniques are widely used to extract meaningful information from a guiding image and facilitate the enhancement of the guided one. This paper specifically addresses the challenge of guided thermal image super-resolution, where a low-resolution thermal image is enhanced using a high-resolution visible spectrum image. We propose a new strategy that enhances outcomes from current guided super-resolution methods. This is achieved by transforming the initial guiding data into a representation resembling a thermal-like image, which is more closely in sync with the intended output. Experimental results with upscale factors of 8 and 16, demonstrate the outstanding performance of our approach in guided thermal image super-resolution obtained by mapping the original guiding information to a thermal-like image representation. |
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MSIAU |
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no |
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Admin @ si @ SCS2024 |
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3998 |
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Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol |
| Title |
Online Error-Correcting Output Codes |
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Journal Article |
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2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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32 |
Issue |
3 |
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458-467 |
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IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier. |
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Elsevier |
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North Holland |
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0167-8655 |
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MILAB;OR;HuPBA;MV |
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no |
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Admin @ si @ EMP2011 |
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1714 |
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Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
| Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
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Journal Article |
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2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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27 |
Issue |
8 |
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875–881 |
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ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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800 |
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MILAB;MV;SIAI |
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no |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
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647 |
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Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
| Title |
Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions |
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Journal Article |
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2010 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
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29 |
Issue |
2 |
Pages |
246-259 |
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| Abstract |
Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions. |
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IEEE |
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0278-0062 |
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800 |
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MILAB;MV;OR;SIAI |
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no |
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BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
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1281 |
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Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
| Title |
Motility bar: a new tool for motility analysis of endoluminal videos |
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Journal Article |
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2015 |
Publication |
Computers in Biology and Medicine |
Abbreviated Journal |
CBM |
| Volume |
65 |
Issue |
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Pages |
320-330 |
| Keywords |
Small intestine; Motility; WCE; Computer vision; Image classification |
| Abstract |
Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information. |
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| Notes |
MILAB;MV |
Approved |
no |
| Call Number |
Admin @ si @ DSR2015 |
Serial |
2635 |
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| Author |
Ole Larsen; Petia Radeva; Enric Marti |
| Title |
Bounds on the optimal elasticity parameters for a snake |
Type |
Journal Article |
| Year |
1995 |
Publication |
Image Analysis and Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
37-42 |
| Keywords |
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| Abstract |
This paper develops a formalism by which an estimate for the upper and lower bounds for the elasticity parameters for a snake can be obtained. Objects different in size and shape give rise to different bounds. The bounds can be obtained based on an analysis of the shape of the object of interest. Experiments on synthetic images show a good correlation between the estimated behaviour of the snake and the one actually observed. Experiments on real X-ray images show that the parameters for optimal segmentation lie within the estimated bounds. |
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| Notes |
MILAB;IAM |
Approved |
no |
| Call Number |
IAM @ iam @ LRM1995a |
Serial |
1559 |
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