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Author |
Naveen Onkarappa; Angel Sappa |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Space Variant Representations for Mobile Platform Vision Applications |
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Conference Article |
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Year |
2011 |
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14th International Conference on Computer Analysis of Images and Patterns |
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6855 |
Issue |
II |
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146-154 |
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The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow. |
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Seville, Spain |
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Springer Berlin Heidelberg |
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P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch |
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0302-9743 |
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978-3-642-23677-8 |
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CAIP |
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ADAS |
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NaS2011; ADAS @ adas @ |
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1686 |
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Author |
Francisco Javier Orozco; Ognjen Rudovic; Jordi Gonzalez; Maja Pantic |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Hierarchical On-line Appearance-Based Tracking for 3D Head Pose, Eyebrows, Lips, Eyelids and Irises |
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Journal Article |
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2013 |
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Image and Vision Computing |
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IMAVIS |
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31 |
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4 |
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322-340 |
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On-line appearance models; Levenberg–Marquardt algorithm; Line-search optimization; 3D face tracking; Facial action tracking; Eyelid tracking; Iris tracking |
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Abstract |
In this paper, we propose an On-line Appearance-Based Tracker (OABT) for simultaneous tracking of 3D head pose, lips, eyebrows, eyelids and irises in monocular video sequences. In contrast to previously proposed tracking approaches, which deal with face and gaze tracking separately, our OABT can also be used for eyelid and iris tracking, as well as 3D head pose, lips and eyebrows facial actions tracking. Furthermore, our approach applies an on-line learning of changes in the appearance of the tracked target. Hence, the prior training of appearance models, which usually requires a large amount of labeled facial images, is avoided. Moreover, the proposed method is built upon a hierarchical combination of three OABTs, which are optimized using a Levenberg–Marquardt Algorithm (LMA) enhanced with line-search procedures. This, in turn, makes the proposed method robust to changes in lighting conditions, occlusions and translucent textures, as evidenced by our experiments. Finally, the proposed method achieves head and facial actions tracking in real-time. |
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Elsevier |
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ISE; 605.203; 302.012; 302.018; 600.049 |
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ORG2013 |
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2221 |
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Author |
C. Alejandro Parraga |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Colours and Colour Vision: An Introductory Survey |
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Journal Article |
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2017 |
Publication |
Perception |
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PER |
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46 |
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5 |
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640-641 |
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NEUROBIT; no menciona |
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Par2017 |
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3101 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca |
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Title |
Toward Real-Time Pedestrian Detection Based on a Deformable Template Model |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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Volume |
15 |
Issue |
1 |
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355-364 |
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Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed. |
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1524-9050 |
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ISE; 601.213; 600.078 |
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PGH2014 |
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2350 |
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Author |
Victor Ponce |
![goto web page url](img/www.gif)
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Title |
Evolutionary Bags of Space-Time Features for Human Analysis |
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Book Whole |
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Year |
2016 |
Publication |
PhD Thesis Universitat de Barcelona, UOC and CVC |
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Computer algorithms; Digital image processing; Digital video; Analysis of variance; Dynamic programming; Evolutionary computation; Gesture |
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The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. In computer vision, a very common representation has adopted the form of the well-known Bag of Visual Words. This representation appears implicitly in most approaches where images are described, and is also present in a huge number of areas and domains: image content retrieval, pedestrian detection, human-computer interaction, surveillance, e-health, and social computing, amongst others. The early stages of this dissertation provide an approach for learning visual representations inside evolutionary algorithms, which consists of evolving weighting schemes to improve the BoVW representations for the task of recognizing categories of videos and images. Thus, we demonstrate the applicability of the most common weighting schemes, which are often used in text mining but are less frequently found in computer vision tasks. Beyond learning these visual representations, we provide an approach based on fusion strategies for learning spatiotemporal representations, from multimodal data obtained by depth sensors. Besides, we specially aim at the evolutionary and dynamic modelling, where the temporal factor is present in the nature of the data, such as video sequences of gestures and actions. Indeed, we explore the effects of probabilistic modelling for those approaches based on dynamic programming, so as to handle the temporal deformation and variance amongst video sequences of different categories. Finally, we integrate dynamic programming and generative models into an evolutionary computation framework, with the aim of learning Bags of SubGestures (BoSG) representations and hence to improve the generalization capability of standard gesture recognition approaches. The results obtained in the experimentation demonstrate, first, that evolutionary algorithms are useful for improving the representation of BoVW approaches in several datasets for recognizing categories in still images and video sequences. On the other hand, our experimentation reveals that both, the use of dynamic programming and generative models to align video sequences, and the representations obtained from applying fusion strategies in multimodal data, entail an enhancement on the performance when recognizing some gesture categories. Furthermore, the combination of evolutionary algorithms with models based on dynamic programming and generative approaches results, when aiming at the classification of video categories on large video datasets, in a considerable improvement over standard gesture and action recognition approaches. Finally, we demonstrate the applications of these representations in several domains for human analysis: classification of images where humans may be present, action and gesture recognition for general applications, and in particular for conversational settings within the field of restorative justice |
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June 2016 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Sergio Escalera;Xavier Baro;Hugo Jair Escalante |
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HuPBA |
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no |
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Pon2016 |
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2814 |
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Author |
A. Pujol; Juan J. Villanueva |
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Title |
A supervised Modification of the Hausdorff distance for visual shape classification |
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Journal |
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2002 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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16 |
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3 |
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349-359 |
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(IF: 0.359) |
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ISE |
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PuV2002 |
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273 |
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Author |
Christophe Rigaud; Clement Guerin; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
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Title |
Knowledge-driven understanding of images in comic books |
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Journal Article |
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Year |
2015 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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Volume |
18 |
Issue |
3 |
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199-221 |
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Document Understanding; comics analysis; expert system |
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Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; 600.056; 600.077 |
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no |
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RGK2015 |
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2595 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
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Title |
Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases |
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Journal Article |
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2017 |
Publication |
Pattern Recognition Letters |
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PRL |
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87 |
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203-211 |
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Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans. |
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DAG; 600.097; 602.006; 603.053; 600.121 |
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RLF2017b |
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2873 |
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Author |
Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu |
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Title |
Automatic Image-Based Waste Classification |
Type |
Conference Article |
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Year |
2019 |
Publication |
International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning |
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11487 |
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422–431 |
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Computer Vision; Deep learning; Convolutional neural networks; Waste classification |
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The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset. |
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Almeria; June 2019 |
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LNCS |
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LAMP; 600.120 |
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RSV2019 |
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3273 |
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Author |
Jordi Roca; Maria Vanrell; C. Alejandro Parraga |
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Title |
What is constant in colour constancy? |
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Conference Article |
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2012 |
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6th European Conference on Colour in Graphics, Imaging and Vision |
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337-343 |
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Color constancy refers to the ability of the human visual system to stabilize
the color appearance of surfaces under an illuminant change. In this work we studied how the interrelations among nine colors are perceived under illuminant changes, particularly whether they remain stable across 10 different conditions (5 illuminants and 2 backgrounds). To do so we have used a paradigm that measures several colors under an immersive state of adaptation. From our measures we defined a perceptual structure descriptor that is up to 87% stable over all conditions, suggesting that color category features could be used to predict color constancy. This is in agreement with previous results on the stability of border categories [1,2] and with computational color constancy
algorithms [3] for estimating the scene illuminant. |
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9781622767014 |
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CGIV |
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CIC |
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RVP2012 |
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2189 |
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Yasuko Sugito; Trevor Canham; Javier Vazquez; Marcelo Bertalmio |
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Title |
A Study of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding |
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2021 |
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SMPTE Motion Imaging Journal |
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SMPTE |
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130 |
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4 |
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53 - 65 |
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In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including ΔITPR , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components. |
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SCV2021 |
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3671 |
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Frederic Sampedro; Anna Domenech; Sergio Escalera |
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Title |
Obtaining quantitative global tumoral state indicators based on whole-body PET/CT scans: A breast cancer case study |
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Journal Article |
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2014 |
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Nuclear Medicine Communications |
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NMC |
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35 |
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4 |
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362-371 |
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Objectives: In this work we address the need for the computation of quantitative global tumoral state indicators from oncological whole-body PET/computed tomography scans. The combination of such indicators with other oncological information such as tumor markers or biopsy results would prove useful in oncological decision-making scenarios.
Materials and methods: From an ordering of 100 breast cancer patients on the basis of oncological state through visual analysis by a consensus of nuclear medicine specialists, a set of numerical indicators computed from image analysis of the PET/computed tomography scan is presented, which attempts to summarize a patient’s oncological state in a quantitative manner taking into consideration the total tumor volume, aggressiveness, and spread.
Results: Results obtained by comparative analysis of the proposed indicators with respect to the experts’ evaluation show up to 87% Pearson’s correlation coefficient when providing expert-guided PET metabolic tumor volume segmentation and 64% correlation when using completely automatic image analysis techniques.
Conclusion: Global quantitative tumor information obtained by whole-body PET/CT image analysis can prove useful in clinical nuclear medicine settings and oncological decision-making scenarios. The completely automatic computation of such indicators would improve its impact as time efficiency and specialist independence would be achieved. |
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SDE2014a |
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2444 |
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Carles Sanchez; Debora Gil; R. Tazi; Jorge Bernal; Y. Ruiz; L. Planas; F. Javier Sanchez; Antoni Rosell |
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Quasi-real time digital assessment of Central Airway Obstruction |
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2015 |
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3rd European congress for bronchology and interventional pulmonology ECBIP2015 |
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Barcelona; Spain; April 2015 |
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IAM; MV; 600.075 |
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2612 |
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Daniel Sanchez; J.C.Ortega; Miguel Angel Bautista |
![download PDF file pdf](img/file_PDF.gif)
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Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization |
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2013 |
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6th Iberian Conference on Pattern Recognition and Image Analysis |
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7887 |
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50-58 |
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Human Body Segmentation; Error-Correcting Output Codes; Cascade of Classifiers; Graph Cuts |
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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. |
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Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38627-5 |
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HUPBA |
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2250 |
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Juan Ignacio Toledo; Alicia Fornes; Jordi Cucurull; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Election Tally Sheets Processing System |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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364-368 |
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In paper based elections, manual tallies at polling station level produce myriads of documents. These documents share a common form-like structure and a reduced vocabulary worldwide. On the other hand, each tally sheet is filled by a different writer and on different countries, different scripts are used. We present a complete document analysis system for electoral tally sheet processing combining state of the art techniques with a new handwriting recognition subprocess based on unsupervised feature discovery with Variational Autoencoders and sequence classification with BLSTM neural networks. The whole system is designed to be script independent and allows a fast and reliable results consolidation process with reduced operational cost. |
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Santorini; Greece; April 2016 |
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DAS |
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DAG; 602.006; 600.061; 601.225; 600.077; 600.097 |
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TFC2016 |
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2752 |
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