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| Author | Debora Gil; Oriol Ramos Terrades; Raquel Perez | ||||
| Title | Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution | Type | Book Chapter | ||
| Year | 2021 | Publication | Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 | Abbreviated Journal | |
| Volume | 15 | Issue | Pages  |
89–93 | |
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| Abstract | Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces. | ||||
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| Publisher | Springer Nature | Place of Publication | Editor | ||
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| Area | Expedition | Conference | |||
| Notes | IAM; DAG; 600.120; 600.145; 600.139 | Approved | no | ||
| Call Number | Admin @ si @ GRP2021 | Serial | 3594 | ||
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| Author | Patricia Suarez; Henry Velesaca; Dario Carpio; Angel Sappa | ||||
| Title | Corn kernel classification from few training samples | Type | Journal | ||
| Year | 2023 | Publication | Artificial Intelligence in Agriculture | Abbreviated Journal | |
| Volume | 9 | Issue | Pages |
89-99 | |
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| Abstract | This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types. | ||||
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| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SVC2023 | Serial | 3892 | ||
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| Author | Ernest Valveny; Enric Marti | ||||
| Title | Dimensions analysis in hand-drawn architectural drawings | Type | Conference Article | ||
| Year | 1997 | Publication | VII National Simposium of Pattern Recognition and image Analysis, SNRFAI´97 | Abbreviated Journal | |
| Volume | Issue | Pages |
90-91 | ||
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| Publisher | Place of Publication | CVC-UAB | Editor | ||
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| Notes | DAG;IAM; | Approved | no | ||
| Call Number | IAM @ iam @ VAM1997 | Serial | 1659 | ||
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| Author | Santiago Segui; Oriol Pujol; Jordi Vitria | ||||
| Title | Learning to count with deep object features | Type | Conference Article | ||
| Year | 2015 | Publication | Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop | Abbreviated Journal | |
| Volume | Issue | Pages |
90-96 | ||
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| Abstract | Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. |
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| Address | Boston; USA; June 2015 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | MILAB; HuPBA; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ SPV2015 | Serial | 2636 | ||
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| Author | Josep Llados; Gemma Sanchez; Enric Marti | ||||
| Title | A string based method to recognize symbols and structural textures in architectural plans | Type | Book Chapter | ||
| Year | 1998 | Publication | Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers | Abbreviated Journal | LNCS |
| Volume | 1389 | Issue | 1998 | Pages |
91-103 |
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| Abstract | This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. | ||||
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| Publisher | Springer Link | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | LNCS | Abbreviated Series Title | ||
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| Notes | DAG; IAM | Approved | no | ||
| Call Number | IAM @ iam @ SLE1998 | Serial | 1573 | ||
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| Author | Enric Marti; Jordi Regincos;Jaime Lopez-Krahe; Juan J.Villanueva | ||||
| Title | Hand line drawing interpretation as three-dimensional objects | Type | Journal Article | ||
| Year | 1993 | Publication | Signal Processing – Intelligent systems for signal and image understanding | Abbreviated Journal | |
| Volume | 32 | Issue | 1-2 | Pages |
91-110 |
| Keywords | Line drawing interpretation; line labelling; scene analysis; man-machine interaction; CAD input; line extraction | ||||
| Abstract | In this paper we present a technique to interpret hand line drawings as objects in a three-dimensional space. The object domain considered is based on planar surfaces with straight edges, concretely, on ansextension of Origami world to hidden lines. The line drawing represents the object under orthographic projection and it is sensed using a scanner. Our method is structured in two modules: feature extraction and feature interpretation. In the first one, image processing techniques are applied under certain tolerance margins to detect lines and junctions on the hand line drawing. Feature interpretation module is founded on line labelling techniques using a labelled junction dictionary. A labelling algorithm is here proposed. It uses relaxation techniques to reduce the number of incompatible labels with the junction dictionary so that the convergence of solutions can be accelerated. We formulate some labelling hypotheses tending to eliminate elements in two sets of labelled interpretations. That is, those which are compatible with the dictionary but do not correspond to three-dimensional objects and those which represent objects not very probable to be specified by means of a line drawing. New entities arise on the line drawing as a result of the extension of Origami world. These are defined to enunciate the assumptions of our method as well as to clarify the algorithms proposed. This technique is framed in a project aimed to implement a system to create 3D objects to improve man-machine interaction in CAD systems. | ||||
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| Publisher | Elsevier North-Holland, Inc. | Place of Publication | Amsterdam, The Netherlands, The Netherlands | Editor | |
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| ISSN | 0165-1684 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM;ISE; | Approved | no | ||
| Call Number | IAM @ iam @ MRL1993 | Serial | 1611 | ||
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| Author | Misael Rosales; Petia Radeva; Oriol Rodriguez-Leor; Debora Gil | ||||
| Title | Modelling of image-catheter motion for 3-D IVUS | Type | Journal Article | ||
| Year | 2009 | Publication | Medical image analysis | Abbreviated Journal | MIA |
| Volume | 13 | Issue | 1 | Pages |
91-104 |
| Keywords | Intravascular ultrasound (IVUS); Motion estimation; Motion decomposition; Fourier | ||||
| Abstract | Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS. | ||||
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| Notes | IAM;MILAB | Approved | no | ||
| Call Number | IAM @ iam @ RRR2009 | Serial | 1646 | ||
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| Author | Vitaliy Konovalov; Albert Clapes; Sergio Escalera | ||||
| Title | Automatic Hand Detection in RGB-Depth Data Sequences | Type | Conference Article | ||
| Year | 2013 | Publication | 16th Catalan Conference on Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages |
91-100 | ||
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| Abstract | Detecting hands in multi-modal RGB-Depth visual data has become a challenging Computer Vision problem with several applications of interest. This task involves dealing with changes in illumination, viewpoint variations, the articulated nature of the human body, the high flexibility of the wrist articulation, and the deformability of the hand itself. In this work, we propose an accurate and efficient automatic hand detection scheme to be applied in Human-Computer Interaction (HCI) applications in which the user is seated at the desk and, thus, only the upper body is visible. Our main hypothesis is that hand landmarks remain at a nearly constant geodesic distance from an automatically located anatomical reference point.
In a given frame, the human body is segmented first in the depth image. Then, a graph representation of the body is built in which the geodesic paths are computed from the reference point. The dense optical flow vectors on the corresponding RGB image are used to reduce ambiguities of the geodesic paths’ connectivity, allowing to eliminate false edges interconnecting different body parts. Finally, we are able to detect the position of both hands based on invariant geodesic distances and optical flow within the body region, without involving costly learning procedures. |
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| Address | Vic; October 2013 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CCIA | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ KCE2013 | Serial | 2323 | ||
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| Author | Cristhian Aguilera; M.Ramos; Angel Sappa | ||||
| Title | Simulated Annealing: A Novel Application of Image Processing in the Wood Area | Type | Book Chapter | ||
| Year | 2012 | Publication | Simulated Annealing – Advances, Applications and Hybridizations | Abbreviated Journal | |
| Volume | Issue | Pages |
91-104 | ||
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| Publisher | Place of Publication | Editor | Marcos de Sales Guerra Tsuzuki | ||
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| ISSN | ISBN | 978-953-51-0710-1 | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ ARS2012 | Serial | 2156 | ||
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| Author | Robert Benavente; C. Alejandro Parraga; Maria Vanrell | ||||
| Title | La influencia del contexto en la definicion de las fronteras entre las categorias cromaticas | Type | Conference Article | ||
| Year | 2010 | Publication | 9th Congreso Nacional del Color | Abbreviated Journal | |
| Volume | Issue | Pages |
92–95 | ||
| Keywords | Categorización del color; Apariencia del color; Influencia del contexto; Patrones de Mondrian; Modelos paramétricos | ||||
| Abstract | En este artículo presentamos los resultados de un experimento de categorización de color en el que las muestras se presentaron sobre un fondo multicolor (Mondrian) para simular los efectos del contexto. Los resultados se comparan con los de un experimento previo que, utilizando un paradigma diferente, determinó las fronteras sin tener en cuenta el contexto. El análisis de los resultados muestra que las fronteras obtenidas con el experimento en contexto presentan menos confusión que las obtenidas en el experimento sin contexto. | ||||
| Address | Alicante (Spain) | ||||
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| ISSN | ISBN | 978-84-9717-144-1 | Medium | ||
| Area | Expedition | Conference | CNC | ||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ BPV2010 | Serial | 1327 | ||
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| Author | Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio | ||||
| Title | A computational framework for cancer response assessment based on oncological PET-CT scans | Type | Journal Article | ||
| Year | 2014 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
| Volume | 55 | Issue | Pages |
92–99 | |
| Keywords | Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis | ||||
| Abstract | In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks. | ||||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SED2014 | Serial | 2606 | ||
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| Author | Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca | ||||
| Title | Factorized appearances for object detection | Type | Journal Article | ||
| Year | 2015 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 138 | Issue | Pages |
92–101 | |
| Keywords | Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts | ||||
| Abstract | Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure. Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories. |
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| Notes | ISE; 600.063; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ GPG2015 | Serial | 2705 | ||
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| Author | Muhammad Muzzamil Luqman; Josep Llados; Jean-Yves Ramel; Thierry Brouard | ||||
| Title | A Fuzzy-Interval Based Approach For Explicit Graph Embedding, Recognizing Patterns in Signals, Speech, Images and Video | Type | Conference Article | ||
| Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | 6388 | Issue | Pages |
93–98 | |
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| Abstract | We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the number of edges. These are followed by w features for node degrees, m features for k node attributes and n features for l edge attributes — which represent the distribution of node degrees, node attribute values and edge attribute values, and are obtained by defining (in an unsupervised fashion), fuzzy-intervals over the list of node degrees, node attributes and edge attributes. Experimental results are provided for sample data of ICPR2010 contest GEPR. | ||||
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| Publisher | Springer, Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-17710-1 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ LLR2010 | Serial | 1459 | ||
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| Author | L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip | ||||
| Title | Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation | Type | Journal Article | ||
| Year | 2016 | Publication | Computers & Industrial Engineering | Abbreviated Journal | CIE |
| Volume | 94 | Issue | Pages |
93-104 | |
| Keywords | Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning | ||||
| Abstract | In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one. | ||||
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| Publisher | PERGAMON-ELSEVIER SCIENCE LTD | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | CIE | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0360-8352 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | OR;MV; | Approved | no | ||
| Call Number | Admin @ si @ CFG2016 | Serial | 2749 | ||
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| Author | Artur Xarles; Sergio Escalera; Thomas B. Moeslund; Albert Clapes | ||||
| Title | ASTRA: An Action Spotting TRAnsformer for Soccer Videos | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports | Abbreviated Journal | |
| Volume | Issue | Pages |
93–102 | ||
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| Abstract | In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set. | ||||
| Address | Otawa; Canada; October 2023 | ||||
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| Area | Expedition | Conference | MMSports | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ XEM2023 | Serial | 3970 | ||
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