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| Author | Naveen Onkarappa; Angel Sappa | ||||
| Title | Synthetic sequences and ground-truth flow field generation for algorithm validation | Type | Journal Article | ||
| Year | 2015 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume  |
74 | Issue | 9 | Pages | 3121-3135 |
| Keywords | Ground-truth optical flow; Synthetic sequence; Algorithm validation | ||||
| Abstract | Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1380-7501 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.055; 601.215; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ OnS2014b | Serial | 2472 | ||
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| Author | Pedro Martins; Paulo Carvalho; Carlo Gatta | ||||
| Title | On the completeness of feature-driven maximally stable extremal regions | Type | Journal Article | ||
| Year | 2016 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume |
74 | Issue | Pages | 9-16 | |
| Keywords | Local features; Completeness; Maximally Stable Extremal Regions | ||||
| Abstract | By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0167-8655 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ MCG2016 | Serial | 2748 | ||
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| Author | Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal | ||||
| Title | Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing | Type | Journal Article | ||
| Year | 2014 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume |
72 | Issue | 1 | Pages | 515-539 |
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| Abstract | In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well. | ||||
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| Corporate Author | Thesis | ||||
| Publisher | Springer US | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1380-7501 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ SDT2014 | Serial | 2357 | ||
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| Author | Pau Rodriguez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
| Title | Age and gender recognition in the wild with deep attention | Type | Journal Article | ||
| Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
72 | Issue | Pages | 563-571 | |
| Keywords | Age recognition; Gender recognition; Deep neural networks; Attention mechanisms | ||||
| Abstract | Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy. | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ISE; 600.098; 602.133; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RCG2017b | Serial | 2962 | ||
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| Author | Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras | ||||
| Title | Segmentation of aerial images for plausible detail synthesis | Type | Journal Article | ||
| Year | 2018 | Publication | Computers & Graphics | Abbreviated Journal | CG |
| Volume |
71 | Issue | Pages | 23-34 | |
| Keywords | Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation | ||||
| Abstract | The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0097-8493 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MSIAU; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ACC2018 | Serial | 3147 | ||
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| Author | Cesar Isaza; Joaquin Salas; Bogdan Raducanu | ||||
| Title | Rendering ground truth data sets to detect shadows cast by static objects in outdoors | Type | Journal Article | ||
| Year | 2014 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume |
70 | Issue | 1 | Pages | 557-571 |
| Keywords | Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection | ||||
| Abstract | In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1380-7501 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ ISR2014 | Serial | 2229 | ||
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| Author | Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild | Type | Journal Article | ||
| Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
70 | Issue | Pages | 60-74 | |
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| Abstract | Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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| Notes | DAG; 600.084; 601.197; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GoK2017 | Serial | 2886 | ||
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| Author | Fernando Vilariño; Stephan Ameling; Gerard Lacey; Stephen Patchett; Hugh Mulcahy | ||||
| Title | Eye Tracking Search Patterns in Expert and Trainee Colonoscopists: A Novel Method of Assessing Endoscopic Competency? | Type | Journal Article | ||
| Year | 2009 | Publication | Gastrointestinal Endoscopy | Abbreviated Journal | GI |
| Volume |
69 | Issue | 5 | Pages | 370 |
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| Area | 800 | Expedition | Conference | ||
| Notes | MV;SIAI | Approved | no | ||
| Call Number | fernando @ fernando @ | Serial | 2420 | ||
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| Author | Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras | ||||
| Title | Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot | Type | Journal Article | ||
| Year | 2012 | Publication | Journal of Intelligent and Robotic Systems | Abbreviated Journal | JIRC |
| Volume |
68 | Issue | 2 | Pages | 185-208 |
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| Abstract | This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings. | ||||
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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0921-0296 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RAV2012 | Serial | 2150 | ||
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| Author | Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde | ||||
| Title | Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 - ℓ0 layer decomposition domain | Type | Journal Article | ||
| Year | 2021 | Publication | Biomedical Signal Processing and Control | Abbreviated Journal | BSPC |
| Volume |
68 | Issue | Pages | 102535 | |
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| Abstract | Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ DGR2021b | Serial | 3636 | ||
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| Author | Victor Ponce; Sergio Escalera; Marc Perez; Oriol Janes; Xavier Baro | ||||
| Title | Non-Verbal Communication Analysis in Victim-Offender Mediations | Type | Journal Article | ||
| Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume |
67 | Issue | 1 | Pages | 19-27 |
| Keywords | Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning | ||||
| Abstract | We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals. | ||||
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| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ PEP2015 | Serial | 2583 | ||
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| Author | David Sanchez-Mendoza; David Masip; Agata Lapedriza | ||||
| Title | Emotion recognition from mid-level features | Type | Journal Article | ||
| Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume |
67 | Issue | Part 1 | Pages | 66–74 |
| Keywords | Facial expression; Emotion recognition; Action units; Computer vision | ||||
| Abstract | In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0167-8655 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ SML2015 | Serial | 2746 | ||
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| Author | Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas | ||||
| Title | Improving patch‐based scene text script identification with ensembles of conjoined networks | Type | Journal Article | ||
| Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
67 | Issue | Pages | 85-96 | |
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| Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GNK2017 | Serial | 2887 | ||
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| Author | Ferran Poveda; Debora Gil; Enric Marti; Albert Andaluz; Manel Ballester;Francesc Carreras Costa | ||||
| Title | Helical structure of the cardiac ventricular anatomy assessed by Diffusion Tensor Magnetic Resonance Imaging multi-resolution tractography | Type | Journal Article | ||
| Year | 2013 | Publication | Revista Española de Cardiología | Abbreviated Journal | REC |
| Volume |
66 | Issue | 10 | Pages | 782-790 |
| Keywords | Heart;Diffusion magnetic resonance imaging;Diffusion tractography;Helical heart;Myocardial ventricular band. | ||||
| Abstract | Deep understanding of myocardial structure linking morphology and function of the heart would unravel crucial knowledge for medical and surgical clinical procedures and studies. Several conceptual models of myocardial fiber organization have been proposed but the lack of an automatic and objective methodology prevented an agreement. We sought to deepen in this knowledge through advanced computer graphic representations of the myocardial fiber architecture by diffusion tensor magnetic resonance imaging (DT-MRI).
We performed automatic tractography reconstruction of unsegmented DT-MRI canine heart datasets coming from the public database of the Johns Hopkins University. Full scale tractographies have been build with 200 seeds and are composed by streamlines computed on the vectorial field of primary eigenvectors given at the diffusion tensor volumes. Also, we introduced a novel multi-scale visualization technique in order to obtain a simplified tractography. This methodology allowed to keep the main geometric features of the fiber tracts, making easier to decipher the main properties of the architectural organization of the heart. On the analysis of the output from our tractographic representations we found exact correlation with low-level details of myocardial architecture, but also with the more abstract conceptualization of a continuous helical ventricular myocardial fiber array. Objective analysis of myocardial architecture by an automated method, including the entire myocardium and using several 3D levels of complexity, reveals a continuous helical myocardial fiber arrangement of both right and left ventricles, supporting the anatomical model of the helical ventricular myocardial band described by Torrent-Guasp. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | IAM; 600.044; 600.060 | Approved | no | ||
| Call Number | IAM @ iam @ PGM2013 | Serial | 2194 | ||
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| Author | AN Ruchai; VI Kober; KA Dorofeev; VN Karnaukhov; Mikhail Mozerov | ||||
| Title | Classification of breast abnormalities using a deep convolutional neural network and transfer learning | Type | Journal Article | ||
| Year | 2021 | Publication | Journal of Communications Technology and Electronics | Abbreviated Journal | |
| Volume |
66 | Issue | 6 | Pages | 778–783 |
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| Abstract | A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database. | ||||
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| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ RKD2022 | Serial | 3680 | ||
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