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Author |
Xinhang Song; Luis Herranz; Shuqiang Jiang |
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Title |
Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs |
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2017 |
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31st AAAI Conference on Artificial Intelligence |
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RGB-D scene recognition; weakly supervised; fine tune; CNN |
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Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data. |
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San Francisco CA; February 2017 |
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AAAI |
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LAMP; 600.120 |
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Admin @ si @ SHJ2017 |
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2967 |
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Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
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Title |
Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold |
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2017 |
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IEEE Transactions on Image Processing |
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TIP |
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26 |
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6 |
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2721-2735 |
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Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top-down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy. |
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LAMP; 600.120 |
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Admin @ si @ SJH2017a |
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2963 |
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Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
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Title |
Combining Models from Multiple Sources for RGB-D Scene Recognition |
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2017 |
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26th International Joint Conference on Artificial Intelligence |
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4523-4529 |
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Robotics and Vision; Vision and Perception |
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Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities. |
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Melbourne; Australia; August 2017 |
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IJCAI |
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LAMP; 600.120 |
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Admin @ si @ SJH2017b |
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2966 |
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Author |
Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas |
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Title |
myStone: A system for automatic kidney stone classification |
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2017 |
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Expert Systems with Applications |
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ESA |
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89 |
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41-51 |
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Kidney stone; Optical device; Computer vision; Image classification |
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Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size. |
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ADAS; MSIAU; 603.046; 600.122; 600.118 |
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Call Number |
Admin @ si @ SLB2017 |
Serial |
3026 |
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Author |
Xavier Soria; Angel Sappa; Arash Akbarinia |
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Title |
Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities |
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Conference Article |
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2017 |
Publication |
7th International Conference on Image Processing Theory, Tools & Applications |
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Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset |
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Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images. |
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Montreal; Canada; November 2017 |
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IPTA |
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NEUROBIT; MSIAU; 600.122 |
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Admin @ si @ SSA2017 |
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3074 |
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Author |
Cristina Sanchez Montes; F. Javier Sanchez; Cristina Rodriguez de Miguel; Henry Cordova; Jorge Bernal; Maria Lopez Ceron; Josep Llach; Gloria Fernandez Esparrach |
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Title |
Histological Prediction Of Colonic Polyps By Computer Vision. Preliminary Results |
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Conference Article |
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2017 |
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25th United European Gastroenterology Week |
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polyps; histology; computer vision |
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during colonoscopy, clinicians perform visual inspection of the polyps to predict histology. Kudo’s pit pattern classification is one of the most commonly used for optical diagnosis. These surface patterns present a contrast with respect to their neighboring regions and they can be considered as bright regions in the image that can attract the attention of computational methods. |
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Barcelona; October 2017 |
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ESGE |
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MV; no menciona |
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Admin @ si @ SSR2017 |
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2979 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Cross-Spectral Image Patch Similarity using Convolutional Neural Network |
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Conference Article |
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2017 |
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IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics |
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The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach. |
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San Sebastian; Spain; May 2017 |
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ECMSM |
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ADAS; 600.086; 600.118 |
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Admin @ si @ SSV2017a |
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2916 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture |
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Conference Article |
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2017 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time. |
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Honolulu; Hawaii; USA; July 2017 |
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CVPRW |
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ADAS; 600.086; 600.118 |
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Admin @ si @ SSV2017b |
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2920 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture |
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Conference Article |
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2017 |
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19th international conference on image analysis and processing |
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CNN in Multispectral Imaging; Image Colorization |
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This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
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Catania; Italy; September 2017 |
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ICIAP |
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ADAS; MSIAU; 600.086; 600.122; 600.118 |
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Admin @ si @ SSV2017c |
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3016 |
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Author |
Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados |
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Handwriting Recognition by Attribute embedding and Recurrent Neural Networks |
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Conference Article |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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1038-1043 |
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Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model |
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ICDAR |
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DAG; 600.097; 601.225; 600.121 |
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Admin @ si @ TDF2017 |
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3055 |
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Oriol Vicente; Alicia Fornes; Ramon Valdes |
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La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades |
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2017 |
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3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional |
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281-383 |
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978-84-697-5692-8 |
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HDH |
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DAG; 600.121 |
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Admin @ si @ VFV2017 |
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3060 |
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Author |
Fernando Vilariño |
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Title |
Citizen experience as a powerful communication tool: Open Innovation and the role of Living Labs in EU |
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2017 |
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European Conference of Science Journalists |
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The Open Innovation 2.0 model spearheaded by the European Commission introduces conceptual changes in how innovation processes should be developed. The notion of an innovation ecosystem, and the active participation of the citizens (and all the different actors of the quadruple helix) in innovation processes, opens up new channels for scientific communication, where the citizens (and all actors) can be naturally reached and facilitate the spread of the scientific message in their communities. Unleashing the power of such mechanisms, while maintaining control over the scientific communication done through such channels presents an opportunity and a challenge at the same time.
This workshop will look into key concepts that the Open Innovation 2.0 EU model introduces, and what new opportunities for communication they bring about. Specifically, we will focus on Living Labs, as a key instrument for implementing this innovation model at the regional level, and their potential in creating scientific dissemination spaces. |
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Copenhagen; June 2017 |
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ECSJ |
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MV; 600.097;SIAI |
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Admin @ si @ Vil2017a |
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3032 |
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Author |
Fernando Vilariño |
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Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation |
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2017 |
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Open Living Lab Days Report |
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Krakow; August 2017 |
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MV; no menciona;SIAI |
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Admin @ si @ Vil2017b |
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3033 |
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Fernando Vilariño; Dan Norton |
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Using mutimedia tools to spread poetry collections |
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2017 |
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Internet librarian International Conference |
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London; UK; October 2017 |
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ILI |
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MV; 600.097;SIAI |
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Admin @ si @ ViN2017 |
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3031 |
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Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa |
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A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers |
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2017 |
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IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics |
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In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point
that minimizes the vacuum force needed to guarantee a grasp.
Experimental results in real scenarios are presented to show the validity of the proposed approach. |
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San Sebastian; Spain; May 2017 |
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ADAS; 600.086; 600.118 |
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Admin @ si @ VIS2017 |
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2917 |
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