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Author  |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
| Title | Fast and Robust Object Segmentation with the Integral Linear Classifier | Type | Conference Article | ||
| Year | 2010 | Publication | 23rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1046–1053 | ||
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| Abstract | We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets. | ||||
| Address | San Francisco; CA; USA; June 2010 | ||||
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| ISSN | 1063-6919 | ISBN | 978-1-4244-6984-0 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ ARL2010a | Serial | 1311 | ||
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| Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
| Title | Real-time Object Segmentation using a Bag of Features Approach | Type | Conference Article | ||
| Year | 2010 | Publication | 13th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | 220 | Issue | Pages | 321–329 | |
| Keywords | Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors | ||||
| Abstract | In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset. | ||||
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| Publisher | IOS Press Amsterdam, | Place of Publication | Editor | In R.Alquezar, A.Moreno, J.Aguilar. | |
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| ISSN | ISBN | 9781607506423 | Medium | ||
| Area | Expedition | Conference | CCIA | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ ARL2010b | Serial | 1417 | ||
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| Author |
David Aldavert | ||||
| Title | Visual Simultaneous Localization and Mapping | Type | Report | ||
| Year | 2006 | Publication | CVC Technical Report #98 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Ald2006 | Serial | 736 | ||
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| Author |
David Aldavert | ||||
| Title | Efficient and Scalable Handwritten Word Spotting on Historical Documents using Bag of Visual Words | Type | Book Whole | ||
| Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Word spotting can be defined as the pattern recognition tasked aimed at locating and retrieving a specific keyword within a document image collection without explicitly transcribing the whole corpus. Its use is particularly interesting when applied in scenarios where Optical Character Recognition performs poorly or can not be used at all. This thesis focuses on such a scenario, word spotting on historical handwritten documents that have been written by a single author or by multiple authors with a similar calligraphy.
This problem requires a visual signature that is robust to image artifacts, flexible to accommodate script variations and efficient to retrieve information in a rapid manner. For this, we have developed a set of word spotting methods that on their foundation use the well known Bag-of-Visual-Words (BoVW) representation. This representation has gained popularity among the document image analysis community to characterize handwritten words in an unsupervised manner. However, most approaches on this field rely on a basic BoVW configuration and disregard complex encoding and spatial representations. We determine which BoVW configurations provide the best performance boost to a spotting system. Then, we extend the segmentation-based word spotting, where word candidates are given a priori, to segmentation-free spotting. The proposed approach seeds the document images with overlapping word location candidates and characterizes them with a BoVW signature. Retrieval is achieved comparing the query and candidate signatures and returning the locations that provide a higher consensus. This is a simple but powerful approach that requires a more compact signature than in a segmentation-based scenario. We first project the BoVW signature into a reduced semantic topics space and then compress it further using Product Quantizers. The resulting signature only requires a few dozen bytes, allowing us to index thousands of pages on a common desktop computer. The final system still yields a performance comparable to the state-of-the-art despite all the information loss during the compression phases. Afterwards, we also study how to combine different modalities of information in order to create a query-by-X spotting system where, words are indexed using an information modality and queries are retrieved using another. We consider three different information modalities: visual, textual and audio. Our proposal is to create a latent feature space where features which are semantically related are projected onto the same topics. Creating thus a new feature space where information from different modalities can be compared. Later, we consider the codebook generation and descriptor encoding problem. The codebooks used to encode the BoVW signatures are usually created using an unsupervised clustering algorithm and, they require to test multiple parameters to determine which configuration is best for a certain document collection. We propose a semantic clustering algorithm which allows to estimate the best parameter from data. Since gather annotated data is costly, we use synthetically generated word images. The resulting codebook is database agnostic, i. e. a codebook that yields a good performance on document collections that use the same script. We also propose the use of an additional codebook to approximate descriptors and reduce the descriptor encoding complexity to sub-linear. Finally, we focus on the problem of signatures dimensionality. We propose a new symbol probability signature where each bin represents the probability that a certain symbol is present a certain location of the word image. This signature is extremely compact and combined with compression techniques can represent word images with just a few bytes per signature. |
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| Address | April 2021 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Marçal Rusiñol;Josep Llados | |
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| ISSN | ISBN | 978-84-122714-5-4 | Medium | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ Ald2021 | Serial | 3601 | ||
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| Author |
David Alcalde | ||||
| Title | Image classification in terms of rotation-invariant pattern matching | Type | Report | ||
| Year | 2003 | Publication | CVC Technical Report #73 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Alc2003 | Serial | 518 | ||
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| Author |
Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang | ||||
| Title | Burst Perception-Distortion Tradeoff: Analysis and Evaluation | Type | Conference Article | ||
| Year | 2023 | Publication | IEEE International Conference on Acoustics, Speech and Signal Processing | Abbreviated Journal | |
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| Abstract | Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best option regarding reconstruction quality and efficiency, as the images acquired by handheld imaging devices suffer from degradation and misalignment caused by the camera noise and shake. In this paper, we extend the perception-distortion tradeoff theory by introducing multiple-frame information. We propose the area of the unattainable region as a new metric for perception-distortion tradeoff evaluation and comparison. Based on this metric, we analyse the performance of burst restoration from the perspective of the perception-distortion tradeoff under both aligned bursts and misaligned bursts situations. Our analysis reveals the importance of inter-frame alignment for burst restoration and shows that the optimal burst length for the restoration model depends both on the degree of degradation and misalignment. | ||||
| Address | Rodhes Islands; Greece; June 2023 | ||||
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| Area | Expedition | Conference | ICASSP | ||
| Notes | CIC; MACO | Approved | no | ||
| Call Number | Admin @ si @ XHV2023 | Serial | 3909 | ||
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| Author |
Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown | ||||
| Title | Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring | Type | Journal Article | ||
| Year | 2023 | Publication | Computer Graphics Forum | Abbreviated Journal | CGF |
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| Abstract | Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework. | ||||
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| Notes | CIC; MACO | Approved | no | ||
| Call Number | Admin @ si @ XVH2023 | Serial | 3883 | ||
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| Author |
Danna Xue; Fei Yang; Pei Wang; Luis Herranz; Jinqiu Sun; Yu Zhu; Yanning Zhang | ||||
| Title | SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision | Type | Conference Article | ||
| Year | 2022 | Publication | 30th ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | 6539-6548 | ||
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| Abstract | Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted lightweight models to achieve fast inference. However, these models cannot flexibly adapt to varying accuracy and efficiency requirements. In this paper, we propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference depending on the desired accuracy-efficiency tradeoff. More specifically, we employ parametrized channel slimming by stepwise downward knowledge distillation during training. Motivated by the observation that the differences between segmentation results of each submodel are mainly near the semantic borders, we introduce an additional boundary guided semantic segmentation loss to further improve the performance of each submodel. We show that our proposed SlimSeg with various mainstream networks can produce flexible models that provide dynamic adjustment of computational cost and better performance than independent models. Extensive experiments on semantic segmentation benchmarks, Cityscapes and CamVid, demonstrate the generalization ability of our framework. | ||||
| Address | Lisboa, Portugal, October 2022 | ||||
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| Publisher | Association for Computing Machinery | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-1-4503-9203-7 | Medium | ||
| Area | Expedition | Conference | MM | ||
| Notes | MACO; 600.161; 601.400 | Approved | no | ||
| Call Number | Admin @ si @ XYW2022 | Serial | 3758 | ||
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| Author |
Daniela Rato; Miguel Oliveira; Vitor Santos; Manuel Gomes; Angel Sappa | ||||
| Title | A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Manufacturing Systems | Abbreviated Journal | JMANUFSYST |
| Volume | 64 | Issue | Pages | 497-507 | |
| Keywords | Calibration; Collaborative cell; Multi-modal; Multi-sensor | ||||
| Abstract | Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensure this, collaborative cells are equipped with a large set of sensors of multiple modalities, covering the entire work volume. However, the fusion of information from all these sensors requires an accurate extrinsic calibration. The calibration of such complex systems is challenging, due to the number of sensors and modalities, and also due to the small overlapping fields of view between the sensors, which are positioned to capture different viewpoints of the cell. This paper proposes a sensor to pattern methodology that can calibrate a complex system such as a collaborative cell in a single optimization procedure. Our methodology can tackle RGB and Depth cameras, as well as LiDARs. Results show that our methodology is able to accurately calibrate a collaborative cell containing three RGB cameras, a depth camera and three 3D LiDARs. | ||||
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| Publisher | Science Direct | Place of Publication | Editor | ||
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| Notes | MSIAU; MACO | Approved | no | ||
| Call Number | Admin @ si @ ROS2022 | Serial | 3750 | ||
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| Author |
Daniel Sanchez; Miguel Angel Bautista; Sergio Escalera | ||||
| Title | HuPBA 8k+: Dataset and ECOC-GraphCut based Segmentation of Human Limbs | Type | Journal Article | ||
| Year | 2015 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 150 | Issue | A | Pages | 173–188 |
| Keywords | Human limb segmentation; ECOC; Graph-Cuts | ||||
| Abstract | Human multi-limb segmentation in RGB images has attracted a lot of interest in the research community because of the huge amount of possible applications in fields like Human-Computer Interaction, Surveillance, eHealth, or Gaming. Nevertheless, human multi-limb segmentation is a very hard task because of the changes in appearance produced by different points of view, clothing, lighting conditions, occlusions, and number of articulations of the human body. Furthermore, this huge pose variability makes the availability of large annotated datasets difficult. In this paper, we introduce the HuPBA8k+ dataset. The dataset contains more than 8000 labeled frames at pixel precision, including more than 120000 manually labeled samples of 14 different limbs. For completeness, the dataset is also labeled at frame-level with action annotations drawn from an 11 action dictionary which includes both single person actions and person-person interactive actions. Furthermore, we also propose a two-stage approach for the segmentation of human limbs. In a first stage, human limbs are trained using cascades of classifiers to be split in a tree-structure way, which is included in an Error-Correcting Output Codes (ECOC) framework to define a body-like probability map. This map is used to obtain a binary mask of the subject by means of GMM color modelling and GraphCuts theory. In a second stage, we embed a similar tree-structure in an ECOC framework to build a more accurate set of limb-like probability maps within the segmented user mask, that are fed to a multi-label GraphCut procedure to obtain final multi-limb segmentation. The methodology is tested on the novel HuPBA8k+ dataset, showing performance improvements in comparison to state-of-the-art approaches. In addition, a baseline of standard action recognition methods for the 11 actions categories of the novel dataset is also provided. | ||||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SBE2015 | Serial | 2552 | ||
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| Author |
Daniel Sanchez; Meysam Madadi; Marc Oliu; Sergio Escalera | ||||
| Title | Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation | Type | Conference Article | ||
| Year | 2019 | Publication | 14th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | While many individual tasks in the domain of human analysis have recently received an accuracy boost from deep learning approaches, multi-task learning has mostly been ignored due to a lack of data. New synthetic datasets are being released, filling this gap with synthetic generated data. In this work, we analyze four related human analysis tasks in still images in a multi-task scenario by leveraging such datasets. Specifically, we study the correlation of 2D/3D pose estimation, body part segmentation and full-body depth estimation. These tasks are learned via the well-known Stacked Hourglass module such that each of the task-specific streams shares information with the others. The main goal is to analyze how training together these four related tasks can benefit each individual task for a better generalization. Results on the newly released SURREAL dataset show that all four tasks benefit from the multi-task approach, but with different combinations of tasks: while combining all four tasks improves 2D pose estimation the most, 2D pose improves neither 3D pose nor full-body depth estimation. On the other hand 2D parts segmentation can benefit from 2D pose but not from 3D pose. In all cases, as expected, the maximum improvement is achieved on those human body parts that show more variability in terms of spatial distribution, appearance and shape, e.g. wrists and ankles. | ||||
| Address | Lille; France; May 2019 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ SMO2019 | Serial | 3326 | ||
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| Author |
Daniel Sanchez; J.C.Ortega; Miguel Angel Bautista | ||||
| Title | Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization | Type | Conference Article | ||
| Year | 2013 | Publication | 6th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 7887 | Issue | Pages | 50-58 | |
| Keywords | Human Body Segmentation; Error-Correcting Output Codes; Cascade of Classifiers; Graph Cuts | ||||
| Abstract | Human body segmentation is a hard task because of the high variability in appearance produced by changes in the point of view, lighting conditions, and number of articulations of the human body. In this paper, we propose a two-stage approach for the segmentation of the human body. In a first step, a set of human limbs are described, normalized to be rotation invariant, and trained using cascade of classifiers to be split in a tree structure way. Once the tree structure is trained, it is included in a ternary Error-Correcting Output Codes (ECOC) framework. This first classification step is applied in a windowing way on a new test image, defining a body-like probability map, which is used as an initialization of a GMM color modelling and binary Graph Cuts optimization procedure. The proposed methodology is tested in a novel limb-labelled data set. Results show performance improvements of the novel approach in comparison to classical cascade of classifiers and human detector-based Graph Cuts segmentation approaches. | ||||
| Address | Madeira; Portugal; June 2013 | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | 0302-9743 | ISBN | 978-3-642-38627-5 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | SOB2013 | Serial | 2250 | ||
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| Author |
Daniel Ponsa; Xavier Roca | ||||
| Title | A Novel Approach to Generate Multiple Shape Models. | Type | Miscellaneous | ||
| Year | 2002 | Publication | Articulated Motion and Deformable Objects, LNCS 2492: 80–91, Springer Verlag. | Abbreviated Journal | |
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| Notes | ADAS;ISE | Approved | no | ||
| Call Number | ADAS @ adas @ PoR2002a | Serial | 283 | ||
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| Author |
Daniel Ponsa; Xavier Roca | ||||
| Title | Unsupervised Parameterisation of Gaussian Mixture Models | Type | Miscellaneous | ||
| Year | 2002 | Publication | Abbreviated Journal | ||
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| Notes | ADAS;ISE | Approved | no | ||
| Call Number | ADAS @ adas @ PoR2002c | Serial | 313 | ||
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| Author |
Daniel Ponsa; Xavier Roca | ||||
| Title | Multiple Model Approach to Deformable Shape Tracking | Type | Miscellaneous | ||
| Year | 2003 | Publication | In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science 2652:782–792 | Abbreviated Journal | |
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| Address | Springer-Verlag | ||||
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| Notes | ADAS;ISE | Approved | no | ||
| Call Number | ADAS @ adas @ PoR2003 | Serial | 397 | ||
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