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| Author | Ivet Rafegas; Maria Vanrell | ||||
| Title | Color representation in CNNs: parallelisms with biological vision | Type | Conference Article | ||
| Year | 2017 | Publication | ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision | Abbreviated Journal | |
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| Abstract | Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image features
are efficiently represented. Here, we dissect a trained CNN [2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versions of them to quantify color tuning properties of artificial neurons to provide a classification of the network population. We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the first layer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT). |
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| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCV-MBCC | ||
| Notes | CIC; 600.087; 600.051 | Approved | no | ||
| Call Number | Admin @ si @ RaV2017 | Serial | 2984 | ||
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| Author | Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari | ||||
| Title | Training my car to see using virtual worlds | Type | Journal Article | ||
| Year | 2017 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 38 | Issue | Pages | 102-118 | |
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| Abstract | Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks that it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting of training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ LVS2017 | Serial | 2985 | ||
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| Author | Hana Jarraya; Oriol Ramos Terrades; Josep Llados | ||||
| Title | Learning structural loss parameters on graph embedding applied on symbolic graphs | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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| Abstract | We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. | ||||
| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ JRL2017b | Serial | 3073 | ||
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| Author | Xavier Soria; Angel Sappa; Arash Akbarinia | ||||
| Title | Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities | Type | Conference Article | ||
| Year | 2017 | Publication | 7th International Conference on Image Processing Theory, Tools & Applications | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset | ||||
| Abstract | 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. | ||||
| Address | Montreal; Canada; November 2017 | ||||
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| Area | Expedition | Conference | IPTA | ||
| Notes | NEUROBIT; MSIAU; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSA2017 | Serial | 3074 | ||
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| Author | Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun | ||||
| Title | CARLA: An Open Urban Driving Simulator | Type | Conference Article | ||
| Year | 2017 | Publication | 1st Annual Conference on Robot Learning. Proceedings of Machine Learning | Abbreviated Journal | |
| Volume | 78 | Issue | Pages | 1-16 | |
| Keywords | Autonomous driving; sensorimotor control; simulation | ||||
| Abstract | We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via reinforcement learning. The approaches are evaluated in controlled scenarios of increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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| Address | Mountain View; CA; USA; November 2017 | ||||
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| Area | Expedition | Conference | CORL | ||
| Notes | ADAS; 600.085; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DRC2017 | Serial | 2988 | ||
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| Author | Arash Akbarinia; C. Alejandro Parraga | ||||
| Title | Colour Constancy Beyond the Classical Receptive Field | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 40 | Issue | 9 | Pages | 2081 - 2094 |
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| Abstract | The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results might provide an insight on how dynamical adaptation mechanisms contribute to make object's colours appear constant to us. | ||||
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| Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
| Call Number | Admin @ si @ AkP2018a | Serial | 2990 | ||
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| Author | Arash Akbarinia; C. Alejandro Parraga | ||||
| Title | Feedback and Surround Modulated Boundary Detection | Type | Journal Article | ||
| Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 126 | Issue | 12 | Pages | 1367–1380 |
| Keywords | Boundary detection; Surround modulation; Biologically-inspired vision | ||||
| Abstract | 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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| Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
| Call Number | Admin @ si @ AkP2018b | Serial | 2991 | ||
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| Author | Arash Akbarinia; Raquel Gil Rodriguez; C. Alejandro Parraga | ||||
| Title | Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism | Type | Conference Article | ||
| Year | 2017 | Publication | 28th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | Pooling is a ubiquitous operation in image processing algorithms that allows for higher-level processes to collect relevant low-level features from a region of interest. Currently, max-pooling is one of the most commonly used operators in the computational literature. However, it can lack robustness to outliers due to the fact that it relies merely on the peak of a function. Pooling mechanisms are also present in the primate visual cortex where neurons of higher cortical areas pool signals from lower ones. The receptive fields of these neurons have been shown to vary according to the contrast by aggregating signals over a larger region in the presence of low contrast stimuli. We hypothesise that this contrast-variant-pooling mechanism can address some of the shortcomings of maxpooling. We modelled this contrast variation through a histogram clipping in which the percentage of pooled signal is inversely proportional to the local contrast of an image. We tested our hypothesis by applying it to the phenomenon of colour constancy where a number of popular algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and Double-Opponency). For each of these methods, we investigated the consequences of replacing their original max-pooling by the proposed contrast-variant-pooling. Our experiments on three colour constancy benchmark datasets suggest that previous results can significantly improve by adopting a contrast-variant-pooling mechanism. | ||||
| Address | London; September 2017 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
| Call Number | Admin @ si @ AGP2017 | Serial | 2992 | ||
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| Author | Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen | ||||
| Title | Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | 107241 | ||
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| Abstract | This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori. |
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| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ CCG2023 | Serial | 3855 | ||
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| Author | Hans Stadthagen-Gonzalez; Luis Lopez; M. Carmen Parafita; C. Alejandro Parraga | ||||
| Title | Using two-alternative forced choice tasks and Thurstone law of comparative judgments for code-switching research | Type | Book Chapter | ||
| Year | 2018 | Publication | Linguistic Approaches to Bilingualism | Abbreviated Journal | |
| Volume | Issue | Pages | 67-97 | ||
| Keywords | two-alternative forced choice and Thurstone's law; acceptability judgment; code-switching | ||||
| Abstract | This article argues that 2-alternative forced choice tasks and Thurstone’s law of comparative judgments (Thurstone, 1927) are well suited to investigate code-switching competence by means of acceptability judgments. We compare this method with commonly used Likert scale judgments and find that the 2-alternative forced choice task provides granular details that remain invisible in a Likert scale experiment. In order to compare and contrast both methods, we examined the syntactic phenomenon usually referred to as the Adjacency Condition (AC) (apud Stowell, 1981), which imposes a condition of adjacency between verb and object. Our interest in the AC comes from the fact that it is a subtle feature of English grammar which is absent in Spanish, and this provides an excellent springboard to create minimal code-switched pairs that allow us to formulate a clear research question that can be tested using both methods. | ||||
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| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SLP2018 | Serial | 2994 | ||
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| Author | Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu | ||||
| Title | Can biological solutions help computers detect symmetry? | Type | Conference Article | ||
| Year | 2017 | Publication | 40th European Conference on Visual Perception | Abbreviated Journal | |
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| Address | Berlin; Germany; August 2017 | ||||
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ APE2017 | Serial | 2995 | ||
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| Author | Marçal Rusiñol; J. Chazalon; Katerine Diaz | ||||
| Title | Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness | Type | Journal Article | ||
| Year | 2018 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 77 | Issue | 11 | Pages | 13773-13798 |
| Keywords | Augmented reality; Document image matching; Educational applications | ||||
| Abstract | This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here. | ||||
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| Notes | DAG; ADAS; 600.084; 600.121; 600.118; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RCD2018 | Serial | 2996 | ||
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| Author | J. Chazalon; P. Gomez-Kramer; Jean-Christophe Burie; M.Coustaty; S.Eskenazi; Muhammad Muzzamil Luqman; N.Nayef; Marçal Rusiñol; N. Sidere; Jean-Marc Ogier | ||||
| Title | SmartDoc 2017 Video Capture: Mobile Document Acquisition in Video Mode | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International Workshop on Open Services and Tools for Document Analysis | Abbreviated Journal | |
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| Abstract | As mobile document acquisition using smartphones is getting more and more common, along with the continuous improvement of mobile devices (both in terms of computing power and image quality), we can wonder to which extent mobile phones can replace desktop scanners. Modern applications can cope with perspective distortion and normalize the contrast of a document page captured with a smartphone, and in some cases like bottle labels or posters, smartphones even have the advantage of allowing the acquisition of non-flat or large documents. However, several cases remain hard to handle, such as reflective documents (identity cards, badges, glossy magazine cover, etc.) or large documents for which some regions require an important amount of detail. This paper introduces the SmartDoc 2017 benchmark (named “SmartDoc Video Capture”), which aims at
assessing whether capturing documents using the video mode of a smartphone could solve those issues. The task under evaluation is both a stitching and a reconstruction problem, as the user can move the device over different parts of the document to capture details or try to erase highlights. The material released consists of a dataset, an evaluation method and the associated tool, a sample method, and the tools required to extend the dataset. All the components are released publicly under very permissive licenses, and we particularly cared about maximizing the ease of understanding, usage and improvement. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR-OST | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CGB2017 | Serial | 2997 | ||
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| Author | Laura Lopez-Fuentes; Joost Van de Weijer; Manuel Gonzalez-Hidalgo; Harald Skinnemoen; Andrew Bagdanov | ||||
| Title | Review on computer vision techniques in emergency situations | Type | Journal Article | ||
| Year | 2018 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 77 | Issue | 13 | Pages | 17069–17107 |
| Keywords | Emergency management; Computer vision; Decision makers; Situational awareness; Critical situation | ||||
| Abstract | In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies. | ||||
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| Notes | LAMP; 600.068; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LWG2018 | Serial | 3041 | ||
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| Author | Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas | ||||
| Title | LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.
We show how such a representation produces a more semantically interpretable retrieval from the user’s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington dataset. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GRK2017 | Serial | 2999 | ||
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