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| Author | Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros | ||||
| Title | From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example | Type | Book Chapter | ||
| Year | 2017 | Publication | Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
| Volume | Issue | 13 | Pages | 243-258 | |
| Keywords | Domain Adaptation | ||||
| Abstract | Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world. | ||||
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| Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
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Notes  |
ADAS; 600.085; 601.223; 600.076; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ LXG2017 | Serial | 2872 | ||
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| Author | Vassileios Balntas; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk | ||||
| Title | Learning local feature descriptors with triplets and shallow convolutional neural networks | Type | Conference Article | ||
| Year | 2016 | Publication | 27th British Machine Vision Conference | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets. |
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| Address | York; UK; September 2016 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes |
ADAS; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ BRP2016 | Serial | 2818 | ||
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| Author | Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa | ||||
| Title | Fine-tuning based deep convolutional networks for lepidopterous genus recognition | Type | Conference Article | ||
| Year | 2016 | Publication | 21st Ibero American Congress on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 467-475 | ||
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| Abstract | This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. | ||||
| Address | Lima; Perú; November 2016 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CIARP | ||
| Notes |
ADAS; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ CRS2016 | Serial | 2913 | ||
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| Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira | ||||
| Title | Incremental texture mapping for autonomous driving | Type | Journal Article | ||
| Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
| Volume | 84 | Issue | Pages | 113-128 | |
| Keywords | Scene reconstruction; Autonomous driving; Texture mapping | ||||
| Abstract | Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. | ||||
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| Area | Expedition | Conference | |||
| Notes |
ADAS; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ OSS2016b | Serial | 2912 | ||
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| Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira | ||||
| Title | Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives | Type | Journal Article | ||
| Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
| Volume | 83 | Issue | Pages | 312-325 | |
| Keywords | Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives | ||||
| Abstract | When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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| Notes |
ADAS; 600.086, 600.076 | Approved | no | ||
| Call Number | Admin @ si @OSS2016a | Serial | 2806 | ||
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| Author | Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla | ||||
| Title | Wavelet based visible and infrared image fusion: a comparative study | Type | Journal Article | ||
| Year | 2016 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 16 | Issue | 6 | Pages | 1-15 |
| Keywords | Image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform | ||||
| Abstract | This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). | ||||
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| Area | Expedition | Conference | |||
| Notes |
ADAS; 600.086; 600.076 | Approved | no | ||
| Call Number | Admin @ si @SCA2016 | Serial | 2807 | ||
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| Author | Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo | ||||
| Title | Learning cross-spectral similarity measures with deep convolutional neural networks | Type | Conference Article | ||
| Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains. | ||||
| Address | Las vegas; USA; June 2016 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes |
ADAS; 600.086; 600.076 | Approved | no | ||
| Call Number | Admin @ si @AAS2016 | Serial | 2809 | ||
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| Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo | ||||
| Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 17 | Issue | 4 | Pages | 873 |
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| Abstract | This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data. | ||||
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| Notes |
ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ASA2017 | Serial | 2914 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Cross-Spectral Image Patch Similarity using Convolutional Neural Network | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
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| Abstract | The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach. |
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| Address | San Sebastian; Spain; May 2017 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ECMSM | ||
| Notes |
ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017a | Serial | 2916 | ||
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| Author | Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa | ||||
| Title | A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
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| Abstract | In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point that minimizes the vacuum force needed to guarantee a grasp. Experimental results in real scenarios are presented to show the validity of the proposed approach. |
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| Address | San Sebastian; Spain; May 2017 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ECMSM | ||
| Notes |
ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ VIS2017 | Serial | 2917 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Infrared Image Colorization based on a Triplet DCGAN Architecture | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
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| Abstract | This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time. |
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| Address | Honolulu; Hawaii; USA; July 2017 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | CVPRW | ||
| Notes |
ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017b | Serial | 2920 | ||
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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 | 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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| ISSN | 0097-8493 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ACC2018 | Serial | 3147 | ||
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| Author | Gema Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo | ||||
| Title | 2D-to-3D Facial Expression Transfer | Type | Conference Article | ||
| Year | 2018 | Publication | 24th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2008 - 2013 | ||
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| Abstract | Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an
input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops. |
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| Area | Expedition | Conference | ICPR | ||
| Notes |
ADAS; 600.086; 600.130; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ RLM2018 | Serial | 3232 | ||
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| Author | Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo | ||||
| Title | Single view facial hair 3D reconstruction | Type | Conference Article | ||
| Year | 2019 | Publication | 9th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 11867 | Issue | Pages | 423-436 | |
| Keywords | 3D Vision; Shape Reconstruction; Facial Hair Modeling | ||||
| Abstract | n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. | ||||
| Address | Madrid; July 2019 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes |
ADAS; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3707 | ||
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| Author | Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo | ||||
| Title | Detailed 3D face reconstruction from a single RGB image | Type | Journal | ||
| Year | 2019 | Publication | Journal of WSCG | Abbreviated Journal | JWSCG |
| Volume | 27 | Issue | 2 | Pages | 103-112 |
| Keywords | 3D Wrinkle Reconstruction; Face Analysis, Optimization. | ||||
| Abstract | This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles. |
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| Address | 2019/11 | ||||
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| Notes |
ADAS; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3708 | ||
| Permanent link to this record | |||||