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Patricia Suarez, Angel Sappa, & Boris X. Vintimilla. (2018). Vegetation Index Estimation from Monospectral Images. In 15th International Conference on Images Analysis and Recognition (Vol. 10882, pp. 353–362). LNCS.
Abstract: 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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Debora Gil, Oriol Ramos Terrades, Elisa Minchole, Carles Sanchez, Noelia Cubero de Frutos, Marta Diez-Ferrer, et al. (2017). Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer. In 6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging (Vol. 10550, pp. 151–159). LNCS.
Abstract: Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.
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Pau Riba, Josep Llados, & Alicia Fornes. (2017). Error-tolerant coarse-to-fine matching model for hierarchical graphs. In Pasquale Foggia, Cheng-Lin Liu, & Mario Vento (Eds.), 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition (Vol. 10310, pp. 107–117). Springer International Publishing.
Abstract: Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting.
Keywords: Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching
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Muhammad Anwer Rao, Fahad Shahbaz Khan, Joost Van de Weijer, & Jorma Laaksonen. (2017). Top-Down Deep Appearance Attention for Action Recognition. In 20th Scandinavian Conference on Image Analysis (Vol. 10269, pp. 297–309). LNCS.
Abstract: Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches.
Keywords: Action recognition; CNNs; Feature fusion
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Veronica Romero, Alicia Fornes, Enrique Vidal, & Joan Andreu Sanchez. (2017). Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology. In L.A. Alexandre, J.Salvador Sanchez, & Joao M. F. Rodriguez (Eds.), 8th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 10255, pp. 287–294). LNCS.
Abstract: Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach.
Keywords: Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model
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Marco Bellantonio, Mohammad A. Haque, Pau Rodriguez, Kamal Nasrollahi, Taisi Telve, Sergio Escalera, et al. (2016). Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images. In 23rd International Conference on Pattern Recognition (Vol. 10165). LNCS.
Abstract: Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution.
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Iiris Lusi, Sergio Escalera, & Gholamreza Anbarjafari. (2016). Human Head Pose Estimation on SASE database using Random Hough Regression Forests. In 23rd International Conference on Pattern Recognition Workshops (Vol. 10165). LNCS.
Abstract: In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests.
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Antoni Gurgui, Debora Gil, Enric Marti, & Vicente Grau. (2016). Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain. In 7th International Workshop on Statistical Atlases & Computational Modelling of the Heart (Vol. 10124, pp. 163–171). LNCS.
Abstract: Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries.
Keywords: Laplacian; Constrained maps; Parameterization; Basal ring
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Marc Oliu, Ciprian Corneanu, Laszlo A. Jeni, Jeffrey F. Cohn, Takeo Kanade, & Sergio Escalera. (2016). Continuous Supervised Descent Method for Facial Landmark Localisation. In 13th Asian Conference on Computer Vision (Vol. 10112, pp. 121–135). LNCS.
Abstract: Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.
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Aura Hernandez-Sabate, Lluis Albarracin, Daniel Calvo, & Nuria Gorgorio. (2016). EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game. In 5th International Conference Games and Learning Alliance (Vol. 10056, pp. 50–59). LNCS.
Abstract: Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
Keywords: Simulation environment; Automated Driving; Driver-Vehicle interaction
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Juan Ignacio Toledo, Sebastian Sudholt, Alicia Fornes, Jordi Cucurull, A. Fink, & Josep Llados. (2016). Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) (Vol. 10029, pp. 543–552). LNCS. Springer International Publishing.
Abstract: The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results.
Keywords: Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection
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Saad Minhas, Aura Hernandez-Sabate, Shoaib Ehsan, Katerine Diaz, Ales Leonardis, Antonio Lopez, et al. (2016). LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode. In 14th European Conference on Computer Vision Workshops (Vol. 9915, pp. 894–900). LNCS.
Abstract: Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
Keywords: Simulation environment; Automated Driving; Driver-Vehicle interaction
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Maedeh Aghaei, Mariella Dimiccoli, & Petia Radeva. (2015). Towards social interaction detection in egocentric photo-streams. In Proceedings of SPIE, 8th International Conference on Machine Vision , ICMV 2015 (Vol. 9875).
Abstract: Detecting social interaction in videos relying solely on visual cues is a valuable task that is receiving increasing attention in recent years. In this work, we address this problem in the challenging domain of egocentric photo-streams captured by a low temporal resolution wearable camera (2fpm). The major difficulties to be handled in this context are the sparsity of observations as well as unpredictability of camera motion and attention orientation due to the fact that the camera is worn as part of clothing. Our method consists of four steps: multi-faces localization and tracking, 3D localization, pose estimation and analysis of f-formations. By estimating pair-to-pair interaction probabilities over the sequence, our method states the presence or absence of interaction with the camera wearer and specifies which people are more involved in the interaction. We tested our method over a dataset of 18.000 images and we show its reliability on our considered purpose. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2016). Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels. In 29th Canadian Conference on Artificial Intelligence (Vol. 9673, pp. 3–14). Springer International Publishing.
Abstract: In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dis(similarity) measures (such as Dynamic Time Warping). For these general dis(similarity) measures, k-nearest neighbor algorithms are a natural choice. However, k-NN classifiers are known to be sensitive to noise and outliers. In this paper, a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping (DTW) distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite (PSD) kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably, we obtained 89 % accuracy on the well-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect
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Hana Jarraya, Muhammad Muzzamil Luqman, & Jean-Yves Ramel. (2017). Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition. In B. Lamiroy, & R Dueire Lins (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 9657). LNCS. Springer.
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