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Sumit K. Banchhor; Narendra D. Londhe; Tadashi Araki; Luca Saba; Petia Radeva; Narendra N. Khanna; Jasjit S. Suri |
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Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. |
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Journal Article |
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Year |
2018 |
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Computers in Biology and Medicine |
Abbreviated Journal |
CBM |
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Volume |
101 |
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184-198 |
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Heart disease; Stroke; Atherosclerosis; Intravascular; Coronary; Carotid; Calcium; Morphology; Risk stratification |
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Abstract |
Purpose of review
Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins.
Recent finding
Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes. |
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MILAB; no proj |
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no |
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Admin @ si @ BLA2018 |
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3188 |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Automatic Verification of Properly Signed Multi-page Document Images |
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Conference Article |
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2015 |
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Proceedings of the Eleventh International Symposium on Visual Computing |
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9475 |
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327-336 |
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Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow |
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Abstract |
In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. |
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Las Vegas, Nevada, USA; December 2015 |
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9475 |
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ISVC |
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DAG; 600.077 |
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Admin @ si @ |
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3189 |
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Author |
L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink |
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Title |
A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting |
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2014 |
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Manuscript Cultures |
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7 |
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47-58 |
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With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline. |
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DAG; 600.061; 600.077 |
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no |
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Admin @ si @ |
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3190 |
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Author |
Cristhian A. Aguilera-Carrasco; C. Aguilera; Angel Sappa |
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Title |
Melamine Faced Panels Defect Classification beyond the Visible Spectrum |
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Journal Article |
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Year |
2018 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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18 |
Issue |
11 |
Pages |
1-10 |
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Keywords |
industrial application; infrared; machine learning |
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In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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MSIAU; 600.122 |
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no |
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Call Number |
Admin @ si @ AAS2018 |
Serial |
3191 |
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Author |
Xavier Soria; Angel Sappa |
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Title |
Improving Edge Detection in RGB Images by Adding NIR Channel |
Type |
Conference Article |
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Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images |
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The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum
images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum. |
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Las Palmas de Gran Canaria; November 2018 |
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MSIAU; 600.122 |
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Admin @ si @ SoS2018 |
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3192 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Cross-spectral image dehaze through a dense stacked conditional GAN based approach |
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Conference Article |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Infrared imaging; Dense; Stacked CGAN; Crossspectral; Convolutional networks |
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This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented
receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors
and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
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Las Palmas de Gran Canaria; November 2018 |
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978-1-5386-9385-8 |
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MSIAU; 600.086; 600.130; 600.122 |
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no |
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Admin @ si @ SSV2018a |
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3193 |
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Author |
Jorge Charco; Boris X. Vintimilla; Angel Sappa |
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Title |
Deep learning based camera pose estimation in multi-view environment |
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Conference Article |
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2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Deep learning; Camera pose estimation; Multiview environment; Siamese architecture |
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This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from
scratch on a large data set that takes as input a pair of imagesfrom the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. |
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Las Palmas de Gran Canaria; November 2018 |
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MSIAU; 600.086; 600.130; 600.122 |
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no |
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Admin @ si @ CVS2018 |
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3194 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Near InfraRed Imagery Colorization |
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Conference Article |
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2018 |
Publication |
25th International Conference on Image Processing |
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2237 - 2241 |
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Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization |
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This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics. |
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Athens; Greece; October 2018 |
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ICIP |
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MSIAU; 600.086; 600.130; 600.122 |
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no |
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Admin @ si @ SSV2018b |
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3195 |
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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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2018 |
Publication |
15th International Conference on Images Analysis and Recognition |
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10882 |
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353-362 |
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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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Povoa de Varzim; Portugal; June 2018 |
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ICIAR |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018c |
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3196 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Deep Learning based Single Image Dehazing |
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Conference Article |
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2018 |
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31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop |
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1250 - 12507 |
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Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis |
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This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
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Salt Lake City; USA; June 2018 |
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CVPRW |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018d |
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3197 |
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Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine |
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2018 |
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Entropy |
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ENTROPY |
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20 |
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11 |
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809 |
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hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image |
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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. |
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HUPBA; no proj |
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Admin @ si @ RKE2018 |
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3198 |
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Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Vivek Kumar Singh; Syeda Furruka Banu; Forhad U H Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig; Mohamed Abdel-Nasser |
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SLSNet: Skin lesion segmentation using a lightweight generative adversarial network |
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2021 |
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Expert Systems With Applications |
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ESWA |
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183 |
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115433 |
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The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications. |
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Notes |
MILAB; no proj |
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no |
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Call Number |
Admin @ si @ SRA2021 |
Serial |
3633 |
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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 |
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Title |
Introduction to NIPS 2017 Competition Track |
Type |
Book Chapter |
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Year |
2018 |
Publication |
The NIPS ’17 Competition: Building Intelligent Systems |
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1-23 |
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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. |
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Springer |
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Sergio Escalera; Markus Weimer |
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978-3-319-94042-7 |
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Notes |
HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ EWB2018 |
Serial |
3200 |
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Author |
Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
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Title |
Lost in Transcription of Graphic Signs in Ciphers |
Type |
Conference Article |
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Year |
2022 |
Publication |
International Conference on Historical Cryptology (HistoCrypt 2022) |
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Pages |
153-158 |
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Keywords |
transcription of ciphers; hand-written text recognition of symbols; graphic signs |
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Abstract |
Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. |
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Amsterdam, Netherlands, June 20-22, 2022 |
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HystoCrypt |
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Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
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Call Number |
Admin @ si @ MBS2022 |
Serial |
3731 |
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Permanent link to this record |
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Author |
Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados |
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Title |
Table detection in business document images by message passing networks |
Type |
Journal Article |
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Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
127 |
Issue |
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Pages |
108641 |
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Abstract |
Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches. |
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July 2022 |
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Elsevier |
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Notes |
DAG; 600.162; 600.121 |
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no |
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Call Number |
Admin @ si @ RGR2022 |
Serial |
3729 |
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Permanent link to this record |