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Author |
Pau Rodriguez; Josep M. Gonfaus; Guillem Cucurull; Xavier Roca; Jordi Gonzalez |
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Title |
Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery |
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Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
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11212 |
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357-372 |
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Deep Learning; Convolutional Neural Networks; Attention |
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We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100. |
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Munich; September 2018 |
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ECCV |
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ISE; 600.098; 602.121; 600.119 |
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no |
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Admin @ si @ RGC2018 |
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3139 |
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Author |
Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa |
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Title |
Color Naming for Multi-Color Fashion Items |
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Conference Article |
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Year |
2018 |
Publication |
6th World Conference on Information Systems and Technologies |
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747 |
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64-73 |
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Deep learning; Color; Multi-label |
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Abstract |
There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results. |
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Naples; March 2018 |
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WORLDCIST |
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LAMP; 600.109; 601.309; 600.120 |
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no |
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Admin @ si @ YWR2018 |
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3161 |
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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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Year |
2018 |
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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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SITIS |
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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 |
Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora |
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Title |
Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts |
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Conference Article |
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Year |
2018 |
Publication |
16th International Conference on Frontiers in Handwriting Recognition |
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528-533 |
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Keywords |
Crowdsourcing; Gamification; Handwritten documents; Performance evaluation |
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Abstract |
Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance. |
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Niagara Falls, USA; August 2018 |
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ICFHR |
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DAG; 600.097; 603.057; 600.121 |
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no |
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Admin @ si @ CRF2018 |
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3169 |
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Author |
Patrick Brandao; O. Zisimopoulos; E. Mazomenos; G. Ciutib; Jorge Bernal; M. Visentini-Scarzanell; A. Menciassi; P. Dario; A. Koulaouzidis; A. Arezzo; D.J. Hawkes; D. Stoyanov |
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Title |
Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks |
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Journal |
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Year |
2018 |
Publication |
Journal of Medical Robotics Research |
Abbreviated Journal |
JMRR |
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Volume |
3 |
Issue |
2 |
Pages |
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Keywords |
convolutional neural networks; colonoscopy; computer aided diagnosis |
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Abstract |
Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp
detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance. |
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MV; no menciona |
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no |
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BZM2018 |
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2976 |
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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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Year |
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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Call Number |
Admin @ si @ SSV2018b |
Serial |
3195 |
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Author |
Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi |
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Title |
WiCV 2018: The Fourth Women In Computer Vision Workshop |
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Conference Article |
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Year |
2018 |
Publication |
4th Women in Computer Vision Workshop |
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1941-19412 |
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Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning |
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Abstract |
We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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Salt Lake City; USA; June 2018 |
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WiCV |
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DAG; 600.121; 600.129 |
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no |
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Admin @ si @ DBR2018 |
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3222 |
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Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera |
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Title |
Deep Structure Inference Network for Facial Action Unit Recognition |
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Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
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11216 |
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309-324 |
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Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference |
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Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for general facial expression analysis. Recently, efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between AUs. We propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively. |
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Munich; September 2018 |
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ECCV |
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HUPBA; no proj |
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no |
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Admin @ si @ CME2018 |
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3205 |
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Jose M. Armingol; Jorge Alfonso; Nourdine Aliane; Miguel Clavijo; Sergio Campos-Cordobes; Arturo de la Escalera; Javier del Ser; Javier Fernandez; Fernando Garcia; Felipe Jimenez; Antonio Lopez; Mario Mata |
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Title |
Environmental Perception for Intelligent Vehicles |
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2018 |
Publication |
Intelligent Vehicles. Enabling Technologies and Future Developments |
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23–101 |
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Computer vision; laser techniques; data fusion; advanced driver assistance systems; traffic monitoring systems; intelligent vehicles |
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Environmental perception represents, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in road environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost, or computational load obtained by these works are different. In this chapter some systems based on computer vision and laser techniques are presented. Fusion methods are also introduced in order to provide advanced and reliable perception systems. |
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ADAS; 600.118 |
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Admin @ si @AAA2018 |
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3046 |
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Author |
Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga |
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Title |
Beyond Eleven Color Names for Image Understanding |
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Journal Article |
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2018 |
Publication |
Machine Vision and Applications |
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MVAP |
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29 |
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2 |
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361-373 |
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Color name; Discriminative descriptors; Image classification; Re-identification; Tracking |
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Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification. |
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LAMP; NEUROBIT; 600.068; 600.109; 600.120 |
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Admin @ si @ YYW2018 |
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3087 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Color encoding in biologically-inspired convolutional neural networks |
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2018 |
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Vision Research |
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VR |
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151 |
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7-17 |
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Color coding; Computer vision; Deep learning; Convolutional neural networks |
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Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. |
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CIC; 600.051; 600.087 |
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Admin @ si @RaV2018 |
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3114 |
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Author |
Debora Gil; Rosa Maria Ortiz; Carles Sanchez; Antoni Rosell |
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Title |
Objective endoscopic measurements of central airway stenosis. A pilot study |
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Journal Article |
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2018 |
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Respiration |
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RES |
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95 |
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63–69 |
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Bronchoscopy; Tracheal stenosis; Airway stenosis; Computer-assisted analysis |
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Endoscopic estimation of the degree of stenosis in central airway obstruction is subjective and highly variable. Objective: To determine the benefits of using SENSA (System for Endoscopic Stenosis Assessment), an image-based computational software, for obtaining objective stenosis index (SI) measurements among a group of expert bronchoscopists and general pulmonologists. Methods: A total of 7 expert bronchoscopists and 7 general pulmonologists were enrolled to validate SENSA usage. The SI obtained by the physicians and by SENSA were compared with a reference SI to set their precision in SI computation. We used SENSA to efficiently obtain this reference SI in 11 selected cases of benign stenosis. A Web platform with three user-friendly microtasks was designed to gather the data. The users had to visually estimate the SI from videos with and without contours of the normal and the obstructed area provided by SENSA. The users were able to modify the SENSA contours to define the reference SI using morphometric bronchoscopy. Results: Visual SI estimation accuracy was associated with neither bronchoscopic experience (p = 0.71) nor the contours of the normal and the obstructed area provided by the system (p = 0.13). The precision of the SI by SENSA was 97.7% (95% CI: 92.4-103.7), which is significantly better than the precision of the SI by visual estimation (p < 0.001), with an improvement by at least 15%. Conclusion: SENSA provides objective SI measurements with a precision of up to 99.5%, which can be calculated from any bronchoscope using an affordable scalable interface. Providing normal and obstructed contours on bronchoscopic videos does not improve physicians' visual estimation of the SI. |
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IAM; 600.075; 600.096; 600.145 |
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Admin @ si @ GOS2018 |
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3043 |
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Spyridon Bakas; Mauricio Reyes; Andras Jakab; Stefan Bauer; Markus Rempfler; Alessandro Crimi; Russell Takeshi Shinohara; Christoph Berger; Sung Min Ha; Martin Rozycki; Marcel Prastawa; Esther Alberts; Jana Lipkova; John Freymann; Justin Kirby; Michel Bilello; Hassan Fathallah-Shaykh; Roland Wiest; Jan Kirschke; Benedikt Wiestler; Rivka Colen; Aikaterini Kotrotsou; Pamela Lamontagne; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Marc-Andre Weber; Abhishek Mahajan; Ujjwal Baid; Dongjin Kwon; Manu Agarwal; Mahbubul Alam; Alberto Albiol; Antonio Albiol; Varghese Alex; Tuan Anh Tran; Tal Arbel; Aaron Avery; Subhashis Banerjee; Thomas Batchelder; Kayhan Batmanghelich; Enzo Battistella; Martin Bendszus; Eze Benson; Jose Bernal; George Biros; Mariano Cabezas; Siddhartha Chandra; Yi-Ju Chang; Joseph Chazalon; Shengcong Chen; Wei Chen; Jefferson Chen; Kun Cheng; Meinel Christoph; Roger Chylla; Albert Clérigues; Anthony Costa; Xiaomeng Cui; Zhenzhen Dai; Lutao Dai; Eric Deutsch; Changxing Ding; Chao Dong; Wojciech Dudzik; Theo Estienne; Hyung Eun Shin; Richard Everson; Jonathan Fabrizio; Longwei Fang; Xue Feng; Lucas Fidon; Naomi Fridman; Huan Fu; David Fuentes; David G Gering; Yaozong Gao; Evan Gates; Amir Gholami; Mingming Gong; Sandra Gonzalez-Villa; J Gregory Pauloski; Yuanfang Guan; Sheng Guo; Sudeep Gupta; Meenakshi H Thakur; Klaus H Maier-Hein; Woo-Sup Han; Huiguang He; Aura Hernandez-Sabate; Evelyn Herrmann; Naveen Himthani; Winston Hsu; Cheyu Hsu; Xiaojun Hu; Xiaobin Hu; Yan Hu; Yifan Hu; Rui Hua |
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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge |
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Miscellaneous |
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2018 |
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Arxiv |
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BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST |
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Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multiparametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. |
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ADAS; 600.118 |
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Admin @ si @ BRJ2018 |
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3252 |
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Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska |
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Title |
Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation |
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Conference Article |
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2018 |
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International MICCAI Brainlesion Workshop |
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11384 |
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393-405 |
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Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution |
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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. |
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MICCAIW |
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ADAS; 600.118 |
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Admin @ si @ PSH2018 |
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3251 |
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Arash Akbarinia; C. Alejandro Parraga |
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Feedback and Surround Modulated Boundary Detection |
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2018 |
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International Journal of Computer Vision |
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IJCV |
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126 |
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12 |
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1367–1380 |
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Boundary detection; Surround modulation; Biologically-inspired vision |
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Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. |
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NEUROBIT; 600.068; 600.072 |
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Admin @ si @ AkP2018b |
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2991 |
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