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Author |
Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas |
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Title |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
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Conference Article |
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Year |
2018 |
Publication |
International Workshop on Artificial Intelligence and Pattern Recognition |
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Volume  |
11047 |
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Pages |
34-42 |
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Keywords |
Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis |
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Abstract |
The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall. |
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Cuba; September 2018 |
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IWAIPR |
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Notes |
MILAB; no menciona |
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no |
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Call Number |
Admin @ si @ BGÑ2018 |
Serial |
3237 |
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Author |
Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy |
Type |
Conference Article |
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Year |
2018 |
Publication |
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis |
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Volume |
11041 |
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Keywords |
Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification |
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Abstract |
Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems. |
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Granada; September 2018 |
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MICCAIW |
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Notes |
IAM; 600.096; 600.075; 601.323; 600.145 |
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Call Number |
Admin @ si @ RSB2018b |
Serial |
3137 |
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Author |
Alicia Fornes; Bart Lamiroy |
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Title |
Graphics Recognition, Current Trends and Evolutions |
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Book Whole |
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Year |
2018 |
Publication |
Graphics Recognition, Current Trends and Evolutions |
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Volume |
11009 |
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This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017.
The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps. |
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Springer International Publishing |
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978-3-030-02283-9 |
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Notes |
DAG; 600.121 |
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no |
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Call Number |
Admin @ si @ FoL2018 |
Serial |
3171 |
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Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
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Title |
Optical Music Recognition by Long Short-Term Memory Networks |
Type |
Book Chapter |
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Year |
2018 |
Publication |
Graphics Recognition. Current Trends and Evolutions |
Abbreviated Journal |
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Volume |
11009 |
Issue |
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Pages |
81-95 |
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Keywords |
Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory |
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Abstract |
Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. |
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Springer |
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A. Fornes, B. Lamiroy |
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978-3-030-02283-9 |
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GREC |
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Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
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no |
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Call Number |
Admin @ si @ BRC2018 |
Serial |
3227 |
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Author |
Stefan Schurischuster; Beatriz Remeseiro; Petia Radeva; Martin Kampel |
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Title |
A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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Volume |
10882 |
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Pages |
465-473 |
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Abstract |
Varroa destructor is a parasite harming bee colonies. As the worldwide bee population is in danger, beekeepers as well as researchers are looking for methods to monitor the health of bee hives. In this context, we present a preliminary study to detect parasites on bee videos by means of image analysis and machine learning techniques. For this purpose, each video frame is analyzed individually to extract bee image patches, which are then processed to compute image descriptors and finally classified into mite and no mite bees. The experimental results demonstrated the adequacy of the proposed method, which will be a perfect stepping stone for a further bee monitoring system. |
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Address |
Povoa de Varzim; Portugal; June 2018 |
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ICIAR |
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Notes |
MILAB; no proj |
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no |
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Call Number |
Admin @ si @ SRR2018a |
Serial |
3110 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Vegetation Index Estimation from Monospectral Images |
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Conference Article |
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Year |
2018 |
Publication |
15th International Conference on Images Analysis and Recognition |
Abbreviated Journal |
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Volume |
10882 |
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Pages |
353-362 |
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Abstract |
This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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Address |
Povoa de Varzim; Portugal; June 2018 |
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ICIAR |
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Notes |
MSIAU; 600.086; 600.130; 600.122 |
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no |
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Call Number |
Admin @ si @ SSV2018c |
Serial |
3196 |
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Author |
Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell |
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Title |
Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer |
Type |
Conference Article |
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Year |
2017 |
Publication |
6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging |
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Volume |
10550 |
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Pages |
151-159 |
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Abstract |
Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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Address |
Quebec; Canada; September 2017 |
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CLIP |
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Notes |
IAM; 600.096; 600.075; 600.145 |
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no |
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Call Number |
Admin @ si @ GRM2017 |
Serial |
2957 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Error-tolerant coarse-to-fine matching model for hierarchical graphs |
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Conference Article |
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Year |
2017 |
Publication |
11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition |
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10310 |
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107-117 |
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Keywords |
Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching |
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Abstract |
Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting. |
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Address |
Anacapri; Italy; May 2017 |
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Springer International Publishing |
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Editor |
Pasquale Foggia; Cheng-Lin Liu; Mario Vento |
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GbRPR |
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Notes |
DAG; 600.097; 601.302; 600.121 |
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no |
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Call Number |
Admin @ si @ RLF2017a |
Serial |
2951 |
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Author |
Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen |
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Title |
Top-Down Deep Appearance Attention for Action Recognition |
Type |
Conference Article |
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Year |
2017 |
Publication |
20th Scandinavian Conference on Image Analysis |
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Volume |
10269 |
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297-309 |
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Keywords |
Action recognition; CNNs; Feature fusion |
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Abstract |
Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches. |
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Address |
Tromso; June 2017 |
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SCIA |
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Notes |
LAMP; 600.109; 600.068; 600.120 |
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no |
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Call Number |
Admin @ si @ RKW2017b |
Serial |
3039 |
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Author |
Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
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Title |
Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology |
Type |
Conference Article |
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Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
10255 |
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287-294 |
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Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model |
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Abstract |
Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach. |
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Faro; Portugal; June 2017 |
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L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez |
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978-3-319-58837-7 |
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IbPRIA |
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Notes |
DAG; 602.006; 600.097; 600.121 |
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no |
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Call Number |
Admin @ si @ RFV2017 |
Serial |
2952 |
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