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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Sparse representation over learned dictionary for symbol recognition | Type | Journal Article | ||
| Year | 2016 | Publication | Signal Processing | Abbreviated Journal | SP |
| Volume | 125 | Issue | Pages | 36-47 | |
| Keywords | Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points | ||||
| Abstract | In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. | ||||
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| Notes | DAG; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ DTR2016 | Serial | 2946 | ||
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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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| Notes | ADAS; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ OSS2016b | Serial | 2912 | ||
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| Author | Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez | ||||
| Title | GPU-based pedestrian detection for autonomous driving | Type | Conference Article | ||
| Year | 2016 | Publication | 16th International Conference on Computational Science | Abbreviated Journal | |
| Volume | 80 | Issue | Pages | 2377-2381 | |
| Keywords | Pedestrian detection; Autonomous Driving; CUDA | ||||
| Abstract | We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study. | ||||
| Address | San Diego; CA; USA; June 2016 | ||||
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| Area | Expedition | Conference | ICCS | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ CSE2016 | Serial | 2741 | ||
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| Author | Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez | ||||
| Title | Embedded real-time stereo estimation via Semi-Global Matching on the GPU | Type | Conference Article | ||
| Year | 2016 | Publication | 16th International Conference on Computational Science | Abbreviated Journal | |
| Volume | 80 | Issue | Pages | 143-153 | |
| Keywords | Autonomous Driving; Stereo; CUDA; 3d reconstruction | ||||
| Abstract | Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. | ||||
| Address | San Diego; CA; USA; June 2016 | ||||
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| Area | Expedition | Conference | ICCS | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ HCE2016a | Serial | 2740 | ||
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| Author | Kai Wang; Joost Van de Weijer; Luis Herranz | ||||
| Title | ACAE-REMIND for online continual learning with compressed feature replay | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 150 | Issue | Pages | 122-129 | |
| Keywords | online continual learning; autoencoders; vector quantization | ||||
| Abstract | Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset. | ||||
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| Notes | LAMP; 600.147; 601.379; 600.120; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ WWH2021 | Serial | 3575 | ||
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| Author | Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit | ||||
| Title | Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 149 | Issue | Pages | 164-171 | |
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| Abstract | Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. | ||||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DGV2021 | Serial | 3364 | ||
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| Author | Carola Figueroa Flores; David Berga; Joost Van de Weijer; Bogdan Raducanu | ||||
| Title | Saliency for free: Saliency prediction as a side-effect of object recognition | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 150 | Issue | Pages | 1-7 | |
| Keywords | Saliency maps; Unsupervised learning; Object recognition | ||||
| Abstract | Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods. | ||||
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| Notes | LAMP; 600.147; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ FBW2021 | Serial | 3559 | ||
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| Author | Eduardo Aguilar; Petia Radeva | ||||
| Title | Uncertainty-aware integration of local and flat classifiers for food recognition | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 237-243 | |
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| Abstract | Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ AgR2020 | Serial | 3525 | ||
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| Author | Zhengying Liu; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu | ||||
| Title | Towards automated computer vision: analysis of the AutoCV challenges 2019 | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 135 | Issue | Pages | 196-203 | |
| Keywords | Computer vision; AutoML; Deep learning | ||||
| Abstract | We present the results of recent challenges in Automated Computer Vision (AutoCV, renamed here for clarity AutoCV1 and AutoCV2, 2019), which are part of a series of challenge on Automated Deep Learning (AutoDL). These two competitions aim at searching for fully automated solutions for classification tasks in computer vision, with an emphasis on any-time performance. The first competition was limited to image classification while the second one included both images and videos. Our design imposed to the participants to submit their code on a challenge platform for blind testing on five datasets, both for training and testing, without any human intervention whatsoever. Winning solutions adopted deep learning techniques based on already published architectures, such as AutoAugment, MobileNet and ResNet, to reach state-of-the-art performance in the time budget of the challenge (only 20 minutes of GPU time). The novel contributions include strategies to deliver good preliminary results at any time during the learning process, such that a method can be stopped early and still deliver good performance. This feature is key for the adoption of such techniques by data analysts desiring to obtain rapidly preliminary results on large datasets and to speed up the development process. The soundness of our design was verified in several aspects: (1) Little overfitting of the on-line leaderboard providing feedback on 5 development datasets was observed, compared to the final blind testing on the 5 (separate) final test datasets, suggesting that winning solutions might generalize to other computer vision classification tasks; (2) Error bars on the winners’ performance allow us to say with confident that they performed significantly better than the baseline solutions we provided; (3) The ranking of participants according to the any-time metric we designed, namely the Area under the Learning Curve, was different from that of the fixed-time metric, i.e. AUC at the end of the fixed time budget. We released all winning solutions under open-source licenses. At the end of the AutoDL challenge series, all data of the challenge will be made publicly available, thus providing a collection of uniformly formatted datasets, which can serve to conduct further research, particularly on meta-learning. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LXE2020 | Serial | 3427 | ||
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| Author | Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias | ||||
| Title | Understanding trained CNNs by indexing neuron selectivity | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 318-325 | |
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| Abstract | The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. | ||||
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| Notes | CIC; 600.087; 600.140; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ RVL2019 | Serial | 3310 | ||
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| Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
| Title | From Optical Music Recognition to Handwritten Music Recognition: a Baseline | Type | Journal Article | ||
| Year | 2019 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 123 | Issue | Pages | 1-8 | |
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| Abstract | Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community. | ||||
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| Notes | DAG; 600.097; 601.302; 601.330; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRC2019 | Serial | 3275 | ||
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| Author | Pau Riba; Josep Llados; Alicia Fornes | ||||
| Title | Hierarchical graphs for coarse-to-fine error tolerant matching | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 134 | Issue | Pages | 116-124 | |
| Keywords | Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval | ||||
| Abstract | During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting). | ||||
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| Notes | DAG; 600.097; 601.302; 603.057; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RLF2020 | Serial | 3349 | ||
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| Author | Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam | ||||
| Title | Subgraph spotting in graph representations of comic book images | Type | Journal Article | ||
| Year | 2018 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 112 | Issue | Pages | 118-124 | |
| Keywords | Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images | ||||
| Abstract | Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. | ||||
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| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ LLD2018 | Serial | 3150 | ||
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| Author | Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov | ||||
| Title | Fast: Facilitated and accurate scene text proposals through fcn guided pruning | Type | Journal Article | ||
| Year | 2019 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 119 | Issue | Pages | 112-120 | |
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| Abstract | Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition. | ||||
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| Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ BGN2019 | Serial | 3342 | ||
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| Author | Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund | ||||
| Title | Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields | Type | Journal Article | ||
| Year | 2016 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 80 | Issue | Pages | 208–215 | |
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| Abstract | This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset. The approach shows that simple multi-modal features with the power of the MMSSL paradigm can achieve better performance than state of the art results on the same dataset. |
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| Notes | HuPBA; ISE;MILAB; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ TEG2016 | Serial | 2843 | ||
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