| Records |
Author  |
Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez |
| Title |
Beyond Oneshot Encoding: lower dimensional target embedding |
Type |
Journal Article |
| Year |
2018 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
| Volume |
75 |
Issue |
|
Pages |
21-31 |
| Keywords |
Error correcting output codes; Output embeddings; Deep learning; Computer vision |
| Abstract |
Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ISE; HuPBA; 600.098; 602.133; 602.121; 600.119 |
Approved |
no |
| Call Number |
Admin @ si @ RBE2018 |
Serial |
3120 |
| Permanent link to this record |
| |
|
| |
| Author |
Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera |
| Title |
RGB-D-based Human Motion Recognition with Deep Learning: A Survey |
Type |
Journal Article |
| Year |
2018 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
171 |
Issue |
|
Pages |
118-139 |
| Keywords |
Human motion recognition; RGB-D data; Deep learning; Survey |
| Abstract |
Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ WLO2018 |
Serial |
3123 |
| Permanent link to this record |
| |
|
| |
| Author |
Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari |
| Title |
Review on Emotion Recognition Databases |
Type |
Book Chapter |
| Year |
2018 |
Publication |
Human-Robot Interaction: Theory and Application |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
emotion; computer vision; databases |
| Abstract |
Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
978-1-78923-316-2 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
HUPBA; 602.133 |
Approved |
no |
| Call Number |
Admin @ si @ HRL2018 |
Serial |
3212 |
| Permanent link to this record |
| |
|
| |
| Author |
Rain Eric Haamer; Kaustubh Kulkarni; Nasrin Imanpour; Mohammad Ahsanul Haque; Egils Avots; Michelle Breisch; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Xavier Baro; Ahmad R. Naghsh-Nilchi; Thomas B. Moeslund; Gholamreza Anbarjafari |
| Title |
Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification |
Type |
Conference Article |
| Year |
2018 |
Publication |
8th International Workshop on Human Behavior Understanding |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames. |
| Address |
Xian; China; May 2018 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
FGW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ HKI2018 |
Serial |
3118 |
| Permanent link to this record |
| |
|
| |
| Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
| Title |
Learning to Learn from Web Data through Deep Semantic Embeddings |
Type |
Conference Article |
| Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
Abbreviated Journal |
|
| Volume |
11134 |
Issue |
|
Pages |
514-529 |
| Keywords |
|
| Abstract |
In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. |
| Address |
Munich; Alemanya; September 2018 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ECCVW |
| Notes |
DAG; 600.129; 601.338; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ GGG2018a |
Serial |
3175 |
| Permanent link to this record |
| |
|
| |
| Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
| Title |
Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods |
Type |
Conference Article |
| Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
Abbreviated Journal |
|
| Volume |
11134 |
Issue |
|
Pages |
530-544 |
| Keywords |
|
| Abstract |
Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis. |
| Address |
Munich; Alemanya; September 2018 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ECCVW |
| Notes |
DAG; 600.129; 601.338; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ GGG2018b |
Serial |
3176 |
| Permanent link to this record |
| |
|
| |
| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
| Title |
Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine |
Type |
Journal Article |
| Year |
2018 |
Publication |
Entropy |
Abbreviated Journal |
ENTROPY |
| Volume |
20 |
Issue |
11 |
Pages |
809 |
| Keywords |
hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image |
| Abstract |
In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ RKE2018 |
Serial |
3198 |
| Permanent link to this record |
| |
|
| |
| Author |
Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
| Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
Type |
Journal Article |
| Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
80 |
Issue |
|
Pages |
64-82 |
| Keywords |
Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
| Abstract |
Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
DAG; 600.097; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ RSJ2018 |
Serial |
3096 |
| Permanent link to this record |
| |
|
| |
| Author |
Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska |
| Title |
Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation |
Type |
Conference Article |
| Year |
2018 |
Publication |
International MICCAI Brainlesion Workshop |
Abbreviated Journal |
|
| Volume |
11384 |
Issue |
|
Pages |
393-405 |
| Keywords |
Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution |
| Abstract |
In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
MICCAIW |
| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ PSH2018 |
Serial |
3251 |
| Permanent link to this record |
| |
|
| |
| Author |
Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon |
| Title |
Looking at People Special Issue |
Type |
Journal Article |
| Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
126 |
Issue |
2-4 |
Pages |
141-143 |
| Keywords |
|
| Abstract |
|
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
HUPBA; ISE; 600.119 |
Approved |
no |
| Call Number |
Admin @ si @ EGJ2018 |
Serial |
3093 |
| Permanent link to this record |
| |
|
| |
| Author |
Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio |
| Title |
Introduction to NIPS 2017 Competition Track |
Type |
Book Chapter |
| Year |
2018 |
Publication |
The NIPS ’17 Competition: Building Intelligent Systems |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
1-23 |
| Keywords |
|
| Abstract |
Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?
In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
Springer |
Place of Publication |
|
Editor |
Sergio Escalera; Markus Weimer |
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
978-3-319-94042-7 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ EWB2018 |
Serial |
3200 |
| Permanent link to this record |
| |
|
| |
| Author |
Shanxin Yuan; Guillermo Garcia-Hernando; Bjorn Stenger; Gyeongsik Moon; Ju Yong Chang; Kyoung Mu Lee; Pavlo Molchanov; Jan Kautz; Sina Honari; Liuhao Ge; Junsong Yuan; Xinghao Chen; Guijin Wang; Fan Yang; Kai Akiyama; Yang Wu; Qingfu Wan; Meysam Madadi; Sergio Escalera; Shile Li; Dongheui Lee; Iason Oikonomidis; Antonis Argyros; Tae-Kyun Kim |
| Title |
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals |
Type |
Conference Article |
| Year |
2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
2636 - 2645 |
| Keywords |
Three-dimensional displays; Task analysis; Pose estimation; Two dimensional displays; Joints; Training; Solid modeling |
| Abstract |
In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints. |
| Address |
Salt Lake City; USA; June 2018 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
CVPR |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ YGS2018 |
Serial |
3115 |
| Permanent link to this record |
| |
|
| |
| Author |
Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas |
| Title |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
Type |
Conference Article |
| Year |
2018 |
Publication |
International Workshop on Artificial Intelligence and Pattern Recognition |
Abbreviated Journal |
|
| Volume |
11047 |
Issue |
|
Pages |
34-42 |
| Keywords |
Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis |
| 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. |
| Address |
Cuba; September 2018 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
IWAIPR |
| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BGÑ2018 |
Serial |
3237 |
| Permanent link to this record |
| |
|
| |
| Author |
Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal |
| Title |
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification |
Type |
Miscellaneous |
| Year |
2018 |
Publication |
Arxiv |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
DAG; 600.097; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ DDT2018 |
Serial |
3085 |
| Permanent link to this record |
| |
|
| |
| Author |
Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados |
| Title |
Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework |
Type |
Conference Article |
| Year |
2018 |
Publication |
14th Asian Conference on Computer Vision |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset. |
| Address |
Perth; Australia; December 2018 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ACCV |
| Notes |
DAG; 600.097; 600.121; 600.129 |
Approved |
no |
| Call Number |
Admin @ si @ DDG2018a |
Serial |
3151 |
| Permanent link to this record |
