Anjan Dutta, Umapada Pal, & Josep Llados. (2016). Compact Correlated Features for Writer Independent Signature Verification. In 23rd International Conference on Pattern Recognition.
Abstract: This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300.
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Lluis Gomez, & Dimosthenis Karatzas. (2016). A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction. IJDAR - International Journal on Document Analysis and Recognition, 19(4), 335–349.
Abstract: Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios.
Keywords: scene text; segmentation; detection; hierarchical grouping; perceptual organisation
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Mariella Dimiccoli, Jean-Pascal Jacob, & Lionel Moisan. (2016). Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach. MVAP - Journal of Machine Vision and Applications, 27, 511–527.
Abstract: In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor
quality data, particles and trajectories can be characterized by an a-contrario
model, that estimates the probability of observing the structures of interest
in random data. This approach, first introduced in the modeling of human visual
perception and then successfully applied in many image processing tasks, leads
to algorithms that do not require a previous learning stage, nor a tedious
parameter tuning and are very robust to noise. Comparative evaluations against
a well established baseline show that the proposed approach outperforms the
state of the art.
Keywords: particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging
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Marc Oliu, Ciprian Corneanu, Kamal Nasrollahi, Olegs Nikisins, Sergio Escalera, Yunlian Sun, et al. (2016). Improved RGB-D-T based Face Recognition. BIO - IET Biometrics, 5(4), 297–303.
Abstract: Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes.
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Jiaolong Xu, David Vazquez, Krystian Mikolajczyk, & Antonio Lopez. (2016). Hierarchical online domain adaptation of deformable part-based models. In IEEE International Conference on Robotics and Automation (pp. 5536–5541).
Abstract: We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance detector is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted detector achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated targetdomain data), and improves the source detector more than 10 percentage points.
Keywords: Domain Adaptation; Pedestrian Detection
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Wenjuan Gong, Xuena Zhang, Jordi Gonzalez, Andrews Sobral, Thierry Bouwmans, Changhe Tu, et al. (2016). Human Pose Estimation from Monocular Images: A Comprehensive Survey. SENS - Sensors, 16(12), 1966.
Abstract: Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.
Keywords: human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2016). Sparse representation over learned dictionary for symbol recognition. SP - Signal Processing, 125, 36–47.
Abstract: In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.
Keywords: Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points
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Miguel Oliveira, Victor Santos, Angel Sappa, P. Dias, & A. Moreira. (2016). Incremental texture mapping for autonomous driving. RAS - Robotics and Autonomous Systems, 84, 113–128.
Abstract: Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
Keywords: Scene reconstruction; Autonomous driving; Texture mapping
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Victor Campmany, Sergio Silva, Antonio Espinosa, Juan Carlos Moure, David Vazquez, & Antonio Lopez. (2016). GPU-based pedestrian detection for autonomous driving. In 16th International Conference on Computational Science (Vol. 80, pp. 2377–2381).
Abstract: We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study.
Keywords: Pedestrian detection; Autonomous Driving; CUDA
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Daniel Hernandez, Alejandro Chacon, Antonio Espinosa, David Vazquez, Juan Carlos Moure, & Antonio Lopez. (2016). Embedded real-time stereo estimation via Semi-Global Matching on the GPU. In 16th International Conference on Computational Science (Vol. 80, pp. 143–153).
Abstract: Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.
Keywords: Autonomous Driving; Stereo; CUDA; 3d reconstruction
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Mikkel Thogersen, Sergio Escalera, Jordi Gonzalez, & Thomas B. Moeslund. (2016). Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields. PRL - Pattern Recognition Letters, 80, 208–215.
Abstract: This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset.
The approach shows that simple multi-modal features with the power of the MMSSL
paradigm can achieve better performance than state of the art results on the same dataset.
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H. Martin Kjer, Jens Fagertun, Sergio Vera, Debora Gil, Miguel Angel Gonzalez Ballester, & Rasmus R. Paulsena. (2016). Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior. PRL - Patter Recognition Letters, 76(1), 76–82.
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Santiago Segui, Michal Drozdzal, Guillem Pascual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, et al. (2016). Generic Feature Learning for Wireless Capsule Endoscopy Analysis. CBM - Computers in Biology and Medicine, 79, 163–172.
Abstract: The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase).
Keywords: Wireless capsule endoscopy; Deep learning; Feature learning; Motility analysis
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Marta Diez-Ferrer, Debora Gil, Elena Carreño, Susana Padrones, Samantha Aso, Vanesa Vicens, et al. (2016). Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation. CHEST - Chest Journal, 150(4), 1003A.
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Anastasios Doulamis, Nikolaos Doulamis, Marco Bertini, Jordi Gonzalez, & Thomas B. Moeslund. (2016). Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams. MTAP - Multimedia Tools and Applications, 75(22), 14985–14990.
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