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| Author | Mohammad Rouhani; Angel Sappa | ||||
| Title | The Richer Representation the Better Registration | Type | Journal Article | ||
| Year | 2013 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 22 | Issue | 12 | Pages  |
5036-5049 |
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| Abstract | In this paper, the registration problem is formulated as a point to model distance minimization. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, this formulation avoids the correspondence search that is time-consuming. In the first stage, the target set is described through an implicit function by employing a linear least squares fitting. This function can be either an implicit polynomial or an implicit B-spline from a coarse to fine representation. In the second stage, we show how the obtained implicit representation is used as an interface to convert point-to-point registration into point-to-implicit problem. Furthermore, we show that this registration distance is smooth and can be minimized through the Levengberg-Marquardt algorithm. All the formulations presented for both stages are compact and easy to implement. In addition, we show that our registration method can be handled using any implicit representation though some are coarse and others provide finer representations; hence, a tradeoff between speed and accuracy can be set by employing the right implicit function. Experimental results and comparisons in 2D and 3D show the robustness and the speed of convergence of the proposed approach. | ||||
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| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RoS2013 | Serial | 2665 | ||
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| Author | Jaykishan Patel; Alban Flachot; Javier Vazquez; David H. Brainard; Thomas S. A. Wallis; Marcus A. Brubaker; Richard F. Murray | ||||
| Title | A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions | Type | Journal Article | ||
| Year | 2023 | Publication | Journal of Vision | Abbreviated Journal | JV |
| Volume | 23 | Issue | 9 | Pages |
4817-4817 |
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| Abstract | A long-standing view is that lightness illusions are by-products of strategies employed by the visual system to stabilize its perceptual representation of surface reflectance against changes in illumination. Computationally, one such strategy is to infer reflectance from the retinal image, and to base the lightness percept on this inference. CNNs trained to infer reflectance from images have proven successful at solving this problem under limited conditions. To evaluate whether these CNNs provide suitable starting points for computational models of human lightness perception, we tested a state-of-the-art CNN on several lightness illusions, and compared its behaviour to prior measurements of human performance. We trained a CNN (Yu & Smith, 2019) to infer reflectance from luminance images. The network had a 30-layer hourglass architecture with skip connections. We trained the network via supervised learning on 100K images, rendered in Blender, each showing randomly placed geometric objects (surfaces, cubes, tori, etc.), with random Lambertian reflectance patterns (solid, Voronoi, or low-pass noise), under randomized point+ambient lighting. The renderer also provided the ground-truth reflectance images required for training. After training, we applied the network to several visual illusions. These included the argyle, Koffka-Adelson, snake, White’s, checkerboard assimilation, and simultaneous contrast illusions, along with their controls where appropriate. The CNN correctly predicted larger illusions in the argyle, Koffka-Adelson, and snake images than in their controls. It also correctly predicted an assimilation effect in White's illusion. It did not, however, account for the checkerboard assimilation or simultaneous contrast effects. These results are consistent with the view that at least some lightness phenomena are by-products of a rational approach to inferring stable representations of physical properties from intrinsically ambiguous retinal images. Furthermore, they suggest that CNN models may be a promising starting point for new models of human lightness perception. | ||||
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| Notes | MACO; CIC | Approved | no | ||
| Call Number | Admin @ si @ PFV2023 | Serial | 3890 | ||
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| Author | Zhijie Fang; Antonio Lopez | ||||
| Title | Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 21 | Issue | 11 | Pages |
4773 - 4783 |
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| Abstract | Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ FaL2019 | Serial | 3305 | ||
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| Author | Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez | ||||
| Title | SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities | Type | Journal Article | ||
| Year | 2020 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 20 | Issue | 16 | Pages |
4587 |
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| Abstract | This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included. | ||||
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| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MSM2020 | Serial | 3452 | ||
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| Author | Fahad Shahbaz Khan; Jiaolong Xu; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez | ||||
| Title | Recognizing Actions through Action-specific Person Detection | Type | Journal Article | ||
| Year | 2015 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 24 | Issue | 11 | Pages |
4422-4432 |
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| Abstract | Action recognition in still images is a challenging problem in computer vision. To facilitate comparative evaluation independently of person detection, the standard evaluation protocol for action recognition uses an oracle person detector to obtain perfect bounding box information at both training and test time. The assumption is that, in practice, a general person detector will provide candidate bounding boxes for action recognition. In this paper, we argue that this paradigm is suboptimal and that action class labels should already be considered during the detection stage. Motivated by the observation that body pose is strongly conditioned on action class, we show that: 1) the existing state-of-the-art generic person detectors are not adequate for proposing candidate bounding boxes for action classification; 2) due to limited training examples, the direct training of action-specific person detectors is also inadequate; and 3) using only a small number of labeled action examples, the transfer learning is able to adapt an existing detector to propose higher quality bounding boxes for subsequent action classification. To the best of our knowledge, we are the first to investigate transfer learning for the task of action-specific person detection in still images. We perform extensive experiments on two benchmark data sets: 1) Stanford-40 and 2) PASCAL VOC 2012. For the action detection task (i.e., both person localization and classification of the action performed), our approach outperforms methods based on general person detection by 5.7% mean average precision (MAP) on Stanford-40 and 2.1% MAP on PASCAL VOC 2012. Our approach also significantly outperforms the state of the art with a MAP of 45.4% on Stanford-40 and 31.4% on PASCAL VOC 2012. We also evaluate our action detection approach for the task of action classification (i.e., recognizing actions without localizing them). For this task, our approach, without using any ground-truth person localization at test tim- , outperforms on both data sets state-of-the-art methods, which do use person locations. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; LAMP; 600.076; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ KXR2015 | Serial | 2668 | ||
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| Author | Idoia Ruiz; Joan Serrat | ||||
| Title | Hierarchical Novelty Detection for Traffic Sign Recognition | Type | Journal Article | ||
| Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 22 | Issue | 12 | Pages |
4389 |
| Keywords | Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision | ||||
| Abstract | Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy. | ||||
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| Notes | ADAS; 600.154 | Approved | no | ||
| Call Number | Admin @ si @ RuS2022 | Serial | 3684 | ||
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| Author | Susana Alvarez; Maria Vanrell | ||||
| Title | Texton theory revisited: a bag-of-words approach to combine textons | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 12 | Pages |
4312-4325 |
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| Abstract | The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ AlV2012a | Serial | 2130 | ||
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| Author | Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Jordi Gonzalez | ||||
| Title | A survey on model based approaches for 2D and 3D visual human pose recovery | Type | Journal Article | ||
| Year | 2014 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 14 | Issue | 3 | Pages |
4189-4210 |
| Keywords | human pose recovery; human body modelling; behavior analysis; computer vision | ||||
| Abstract | Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature. | ||||
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| Notes | HuPBA; ISE; 600.046; 600.063; 600.078;MILAB | Approved | no | ||
| Call Number | Admin @ si @ PEA2014 | Serial | 2443 | ||
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| Author | Joan Mas; Josep Llados; Gemma Sanchez; J.A. Jorge | ||||
| Title | A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 43 | Issue | 12 | Pages |
4148–4164 |
| Keywords | Syntactic Pattern Recognition; Symbol recognition; Diagram understanding; Sketched diagrams; Adjacency Grammars; Incremental parsing; Spatial directed parsing | ||||
| Abstract | This paper presents a syntactic approach based on Adjacency Grammars (AG) for sketch diagram modeling and understanding. Diagrams are a combination of graphical symbols arranged according to a set of spatial rules defined by a visual language. AG describe visual shapes by productions defined in terms of terminal and non-terminal symbols (graphical primitives and subshapes), and a set functions describing the spatial arrangements between symbols. Our approach to sketch diagram understanding provides three main contributions. First, since AG are linear grammars, there is a need to define shapes and relations inherently bidimensional using a sequential formalism. Second, our parsing approach uses an indexing structure based on a spatial tessellation. This serves to reduce the search space when finding candidates to produce a valid reduction. This allows order-free parsing of 2D visual sentences while keeping combinatorial explosion in check. Third, working with sketches requires a distortion model to cope with the natural variations of hand drawn strokes. To this end we extended the basic grammar with a distortion measure modeled on the allowable variation on spatial constraints associated with grammar productions. Finally, the paper reports on an experimental framework an interactive system for sketch analysis. User tests performed on two real scenarios show that our approach is usable in interactive settings. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ MLS2010 | Serial | 1336 | ||
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| Author | Umapada Pal; Partha Pratim Roy; N. Tripathya; Josep Llados | ||||
| Title | Multi-oriented Bangla and Devnagari text recognition | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 43 | Issue | 12 | Pages |
4124–4136 |
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| Abstract | There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters.
For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ PRT2010 | Serial | 1337 | ||
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| Author | Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez | ||||
| Title | Determining the Best Suited Semantic Events for Cognitive Surveillance | Type | Journal Article | ||
| Year | 2011 | Publication | Expert Systems with Applications | Abbreviated Journal | EXSY |
| Volume | 38 | Issue | 4 | Pages |
4068–4079 |
| Keywords | Cognitive surveillance; Event modeling; Content-based video retrieval; Ontologies; Advanced user interfaces | ||||
| Abstract | State-of-the-art systems on cognitive surveillance identify and describe complex events in selected domains, thus providing end-users with tools to easily access the contents of massive video footage. Nevertheless, as the complexity of events increases in semantics and the types of indoor/outdoor scenarios diversify, it becomes difficult to assess which events describe better the scene, and how to model them at a pixel level to fulfill natural language requests. We present an ontology-based methodology that guides the identification, step-by-step modeling, and generalization of the most relevant events to a specific domain. Our approach considers three steps: (1) end-users provide textual evidence from surveilled video sequences; (2) transcriptions are analyzed top-down to build the knowledge bases for event description; and (3) the obtained models are used to generalize event detection to different image sequences from the surveillance domain. This framework produces user-oriented knowledge that improves on existing advanced interfaces for video indexing and retrieval, by determining the best suited events for video understanding according to end-users. We have conducted experiments with outdoor and indoor scenes showing thefts, chases, and vandalism, demonstrating the feasibility and generalization of this proposal. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ FBR2011a | Serial | 1722 | ||
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| Author | Javier Marin; Sergio Escalera | ||||
| Title | SSSGAN: Satellite Style and Structure Generative Adversarial Networks | Type | Journal Article | ||
| Year | 2021 | Publication | Remote Sensing | Abbreviated Journal | |
| Volume | 13 | Issue | 19 | Pages |
3984 |
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| Abstract | This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce
consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area. |
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ MaE2021 | Serial | 3651 | ||
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| Author | Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores | ||||
| 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 | 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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| Series Volume | Series Issue | Edition | |||
| ISSN | 2168-2267 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2810 | ||
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| Author | Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
| Title | Segmentation-free Word Spotting with Exemplar SVMs | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 12 | Pages |
3967–3978 |
| Keywords | Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression | ||||
| Abstract | In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. | ||||
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| Notes | DAG; 600.045; 600.056; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ AGF2014b | Serial | 2485 | ||
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| Author | Wenwen Fu; Zhihong An; Wendong Huang; Haoran Sun; Wenjuan Gong; Jordi Gonzalez | ||||
| Title | A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection | Type | Journal Article | ||
| Year | 2023 | Publication | Electronics | Abbreviated Journal | ELEC |
| Volume | 12 | Issue | 18 | Pages |
3947 |
| Keywords | micro-expression spotting; sliding window; key frame extraction | ||||
| Abstract | Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ FAH2023 | Serial | 3864 | ||
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