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| Author | Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza | ||||
| Title | Evolving weighting schemes for the Bag of Visual Words | Type | Journal Article | ||
| Year | 2017 | Publication | Neural Computing and Applications | Abbreviated Journal  |
Neural Computing and Applications |
| Volume | 28 | Issue | 5 | Pages | 925–939 |
| Keywords | Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision | ||||
| Abstract | The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method. |
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| Publisher | Place of Publication | Editor | Springer | ||
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| Notes | HUPBA;MV; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EPE2017 | Serial | 2743 | ||
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| Author | Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla | ||||
| Title | Editorial: Special Issue on Computational Intelligence for Vision and Robotics | Type | Journal Article | ||
| Year | 2017 | Publication | Neural Computing and Applications | Abbreviated Journal |
Neural Computing and Applications |
| Volume | 28 | Issue | 5 | Pages | 853–854 |
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| Notes | HuPBA;MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ GGE2017 | Serial | 2845 | ||
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| Author | Sounak Dey; Palaiahnakote Shivakumara; K.S. Raghunanda; Umapada Pal; Tong Lu; G. Hemantha Kumar; Chee Seng Chan | ||||
| Title | Script independent approach for multi-oriented text detection in scene image | Type | Journal Article | ||
| Year | 2017 | Publication | Neurocomputing | Abbreviated Journal |
NEUCOM |
| Volume | 242 | Issue | Pages | 96-112 | |
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| Abstract | Developing a text detection method which is invariant to scripts in natural scene images is a challeng- ing task due to different geometrical structures of various scripts. Besides, multi-oriented of text lines in natural scene images make the problem more challenging. This paper proposes to explore ring radius transform (RRT) for text detection in multi-oriented and multi-script environments. The method finds component regions based on convex hull to generate radius matrices using RRT. It is a fact that RRT pro- vides low radius values for the pixels that are near to edges, constant radius values for the pixels that represent stroke width, and high radius values that represent holes created in background and convex hull because of the regular structures of text components. We apply k -means clustering on the radius matrices to group such spatially coherent regions into individual clusters. Then the proposed method studies the radius values of such cluster components that are close to the centroid and far from the cen- troid to detect text components. Furthermore, we have developed a Bangla dataset (named as ISI-UM dataset) and propose a semi-automatic system for generating its ground truth for text detection of arbi- trary orientations, which can be used by the researchers for text detection and recognition in the future. The ground truth will be released to public. Experimental results on our ISI-UM data and other standard datasets, namely, ICDAR 2013 scene, SVT and MSRA data, show that the proposed method outperforms the existing methods in terms of multi-lingual and multi-oriented text detection ability. | ||||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DSR2017 | Serial | 3260 | ||
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| Author | F. Javier Sanchez; Jorge Bernal; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach | ||||
| Title | Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos | Type | Journal Article | ||
| Year | 2017 | Publication | Machine Vision and Applications | Abbreviated Journal |
MVAP |
| Volume | Issue | Pages | 1-20 | ||
| Keywords | Specular highlights; bright spot regions segmentation; region classification; colonoscopy | ||||
| Abstract | A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance de ning specular
highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classi cation of bright spot regions. The former de nes a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; nal regions provided depend on restrictions over contrast value. Non-specular regions are ltered through a classi cation stage performed by a linear SVM classi er using model-based features from each region. We introduce a new validation database with more than 25; 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology. |
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| Notes | MV; 600.096; 600.175 | Approved | no | ||
| Call Number | Admin @ si @ SBS2017 | Serial | 2975 | ||
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| Author | Debora Gil; Sergio Vera; Agnes Borras; Albert Andaluz; Miguel Angel Gonzalez Ballester | ||||
| Title | Anatomical Medial Surfaces with Efficient Resolution of Branches Singularities | Type | Journal Article | ||
| Year | 2017 | Publication | Medical Image Analysis | Abbreviated Journal |
MIA |
| Volume | 35 | Issue | Pages | 390-402 | |
| Keywords | Medial Representations; Shape Recognition; Medial Branching Stability ; Singular Points | ||||
| Abstract | Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a con dent application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an ecient GPU-CPU implementation using standard image processing tools. We show the method computational eciency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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| Notes | IAM; 600.060; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GVB2017 | Serial | 2775 | ||
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| Author | Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate | ||||
| Title | Decremental generalized discriminative common vectors applied to images classification | Type | Journal Article | ||
| Year | 2017 | Publication | Knowledge-Based Systems | Abbreviated Journal |
KBS |
| Volume | 131 | Issue | Pages | 46-57 | |
| Keywords | Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification | ||||
| Abstract | In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. | ||||
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| Notes | ADAS; 600.118; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DMH2017a | Serial | 3003 | ||
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| Author | Arash Akbarinia; Karl R. Gegenfurtner | ||||
| Title | Metameric Mismatching in Natural and Artificial Reflectances | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of Vision | Abbreviated Journal |
JV |
| Volume | 17 | Issue | 10 | Pages | 390-390 |
| Keywords | Metamer; colour perception; spectral discrimination; photoreceptors | ||||
| Abstract | The human visual system and most digital cameras sample the continuous spectral power distribution through three classes of receptors. This implies that two distinct spectral reflectances can result in identical tristimulus values under one illuminant and differ under another – the problem of metamer mismatching. It is still debated how frequent this issue arises in the real world, using naturally occurring reflectance functions and common illuminants.
We gathered more than ten thousand spectral reflectance samples from various sources, covering a wide range of environments (e.g., flowers, plants, Munsell chips) and evaluated their responses under a number of natural and artificial source of lights. For each pair of reflectance functions, we estimated the perceived difference using the CIE-defined distance ΔE2000 metric in Lab color space. The degree of metamer mismatching depended on the lower threshold value l when two samples would be considered to lead to equal sensor excitations (ΔE < l), and on the higher threshold value h when they would be considered different. For example, for l=h=1, we found that 43.129 comparisons out of a total of 6×107 pairs would be considered metameric (1 in 104). For l=1 and h=5, this number reduced to 705 metameric pairs (2 in 106). Extreme metamers, for instance l=1 and h=10, were rare (22 pairs or 6 in 108), as were instances where the two members of a metameric pair would be assigned to different color categories. Not unexpectedly, we observed variations among different reflectance databases and illuminant spectra with more frequency under artificial illuminants than natural ones. Overall, our numbers are not very different from those obtained earlier (Foster et al, JOSA A, 2006). However, our results also show that the degree of metamerism is typically not very strong and that category switches hardly ever occur. |
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| Address | Florida, USA; May 2017 | ||||
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| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Admin @ si @ AkG2017 | Serial | 2899 | ||
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| Author | Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso | ||||
| Title | Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of Thoracic Oncology | Abbreviated Journal |
JTO |
| Volume | 12 | Issue | 1S | Pages | S596-S597 |
| Keywords | Thorax CT; diagnosis; Peripheral Pulmonary Nodule | ||||
| Abstract | A main weakness of virtual bronchoscopic navigation (VBN) is unsuccessful segmentation of distal branches approaching peripheral pulmonary nodules (PPN). CT scan acquisition protocol is pivotal for segmentation covering the utmost periphery. We hypothesize that application of continuous positive airway pressure (CPAP) during CT acquisition could improve visualization and segmentation of peripheral bronchi. The purpose of the present pilot study is to compare quality of segmentations under 4 CT acquisition modes: inspiration (INSP), expiration (EXP) and both with CPAP (INSP-CPAP and EXP-CPAP). | ||||
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| Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ DGC2017a | Serial | 2883 | ||
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| Author | Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez | ||||
| Title | Enhancing spatio-chromatic representation with more-than-three color coding for image description | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of the Optical Society of America A | Abbreviated Journal |
JOSA A |
| Volume | 34 | Issue | 5 | Pages | 827-837 |
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| Abstract | Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding. | ||||
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| Notes | CIC; 600.087 | Approved | no | ||
| Call Number | Admin @ si @ RVB2017 | Serial | 2892 | ||
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| Author | Jordi Esquirol; Cristina Palmero; Vanessa Bayo; Miquel Angel Cos; Sergio Escalera; David Sanchez; Maider Sanchez; Noelia Serrano; Mireia Relats | ||||
| Title | Automatic RBG-depth-pressure anthropometric analysis and individualised sleep solution prescription | Type | Journal | ||
| Year | 2017 | Publication | Journal of Medical Engineering & Technology | Abbreviated Journal |
JMET |
| Volume | 41 | Issue | 6 | Pages | 486-497 |
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| Abstract | INTRODUCTION:
Sleep surfaces must adapt to individual somatotypic features to maintain a comfortable, convenient and healthy sleep, preventing diseases and injuries. Individually determining the most adequate rest surface can often be a complex and subjective question. OBJECTIVES: To design and validate an automatic multimodal somatotype determination model to automatically recommend an individually designed mattress-topper-pillow combination. METHODS: Design and validation of an automated prescription model for an individualised sleep system is performed through a single-image 2 D-3 D analysis and body pressure distribution, to objectively determine optimal individual sleep surfaces combining five different mattress densities, three different toppers and three cervical pillows. RESULTS: A final study (n = 151) and re-analysis (n = 117) defined and validated the model, showing high correlations between calculated and real data (>85% in height and body circumferences, 89.9% in weight, 80.4% in body mass index and more than 70% in morphotype categorisation). CONCLUSIONS: Somatotype determination model can accurately prescribe an individualised sleep solution. This can be useful for healthy people and for health centres that need to adapt sleep surfaces to people with special needs. Next steps will increase model's accuracy and analise, if this prescribed individualised sleep solution can improve sleep quantity and quality; additionally, future studies will adapt the model to mattresses with technological improvements, tailor-made production and will define interfaces for people with special needs. |
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| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EPB2017 | Serial | 3010 | ||
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| Author | David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville | ||||
| Title | A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of Healthcare Engineering | Abbreviated Journal |
JHCE |
| Volume | Issue | Pages | 2040-2295 | ||
| Keywords | Colonoscopy images; Deep Learning; Semantic Segmentation | ||||
| Abstract | Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. | ||||
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| Notes | ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 | Approved | no | ||
| Call Number | VBS2017b | Serial | 2940 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Francesca Vidal; Zaida Sarrate | ||||
| Title | Noves perspectives en l estudi de la territorialitat cromosomica de cel·lules germinals masculines: estudis tridimensionals | Type | Journal | ||
| Year | 2017 | Publication | Biologia de la Reproduccio | Abbreviated Journal |
JBR |
| Volume | 15 | Issue | Pages | 73-78 | |
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| Abstract | In somatic cells, chromosomes occupy specific nuclear regions called chromosome territories which are involved in the
maintenance and regulation of the genome. Preliminary data in male germ cells also suggest the importance of chromosome territoriality in cell functionality. Nevertheless, the specific characteristics of testicular tissue (presence of different cell types with different morphological characteristics, in different stages of development and with different ploidy) makes difficult to achieve conclusive results. In this study we have developed a methodology to approach the threedimensional study of all chromosome territories in male germ cells from C57BL/6J mice (Mus musculus). The method includes the following steps: i) Optimized cell fixation to obtain an optimal preservation of the three-dimensionality cell morphology, ii) Chromosome identification by FISH (Chromoprobe Multiprobe® OctoChrome™ Murine System; Cytocell) and confocal microscopy (TCS-SP5, Leica Microsystems), iii) Cell type identification by immunofluorescence iv) Image analysis using Matlab scripts, v) Numerical data extraction related to chromosome features, chromosome radial position and chromosome relative position. This methodology allows the unequivocally identification and the analysis of the chromosome territories of all spermatogenic stages. Results will provide information about the features that determine chromosomal position, preferred associations between chromosomes, and the relationship between chromosome positioning and genome regulation. |
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| ISSN | ISBN | 978-84-697-3767-5 | Medium | ||
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| Notes | IAM; 600.096; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ SBG2017c | Serial | 2961 | ||
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| Author | Karim Lekadir; Alfiia Galimzianova; Angels Betriu; Maria del Mar Vila; Laura Igual; Daniel L. Rubin; Elvira Fernandez-Giraldez; Petia Radeva; Sandy Napel | ||||
| Title | A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Journal Biomedical and Health Informatics | Abbreviated Journal |
J-BHI |
| Volume | 21 | Issue | 1 | Pages | 48-55 |
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| Abstract | Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound. | ||||
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LGB2017 | Serial | 2931 | ||
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| Author | Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari | ||||
| Title | Training my car to see using virtual worlds | Type | Journal Article | ||
| Year | 2017 | Publication | Image and Vision Computing | Abbreviated Journal |
IMAVIS |
| Volume | 38 | Issue | Pages | 102-118 | |
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| Abstract | Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks that it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting of training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ LVS2017 | Serial | 2985 | ||
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| Author | Cristina Palmero; Jordi Esquirol; Vanessa Bayo; Miquel Angel Cos; Pouya Ahmadmonfared; Joan Salabert; David Sanchez; Sergio Escalera | ||||
| Title | Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis | Type | Journal Article | ||
| Year | 2017 | Publication | International Journal of Computer Vision | Abbreviated Journal |
IJCV |
| Volume | 122 | Issue | 2 | Pages | 212–227 |
| Keywords | Sleep system recommendation; RGB-Depth data Pressure imaging; Anthropometric landmark extraction; Multi-part human body segmentation | ||||
| Abstract | This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures. | ||||
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| Notes | HuPBA;MILAB; 303.100 | Approved | no | ||
| Call Number | Admin @ si @ PEB2017 | Serial | 2765 | ||
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