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| Author | Mariella Dimiccoli | ||||
| Title | Figure-ground segregation: A fully nonlocal approach | Type | Journal Article | ||
| Year | 2016 | Publication | Vision Research | Abbreviated Journal | VR |
| Volume | 126 | Issue | Pages | 308-317 | |
| Keywords | Figure-ground segregation; Nonlocal approach; Directional linear voting; Nonlinear diffusion | ||||
| Abstract | We present a computational model that computes and integrates in a nonlocal fashion several configural cues for automatic figure-ground segregation. Our working hypothesis is that the figural status of each pixel is a nonlocal function of several geometric shape properties and it can be estimated without explicitly relying on object boundaries. The methodology is grounded on two elements: multi-directional linear voting and nonlinear diffusion. A first estimation of the figural status of each pixel is obtained as a result of a voting process, in which several differently oriented line-shaped neighborhoods vote to express their belief about the figural status of the pixel. A nonlinear diffusion process is then applied to enforce the coherence of figural status estimates among perceptually homogeneous regions. Computer simulations fit human perception and match the experimental evidence that several cues cooperate in defining figure-ground segregation. The results of this work suggest that figure-ground segregation involves feedback from cells with larger receptive fields in higher visual cortical areas. | ||||
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| Notes | MILAB; | Approved | no | ||
| Call Number | Admin @ si @ Dim2016b | Serial | 2623 | ||
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| Author | Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez | ||||
| Title | Hierarchical Adaptive Structural SVM for Domain Adaptation | Type | Journal Article | ||
| Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 119 | Issue | 2 | Pages | 159-178 |
| Keywords | Domain Adaptation; Pedestrian Detection | ||||
| Abstract | A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains. Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM). As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition. |
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ XRV2016 | Serial | 2669 | ||
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| Author | Tadashi Araki; Sumit K. Banchhor; Narendra D. Londhe; Nobutaka Ikeda; Petia Radeva; Devarshi Shukla; Luca Saba; Antonella Balestrieri; Andrew Nicolaides; Shoaib Shafique; John R. Laird; Jasjit S. Suri | ||||
| Title | Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos | Type | Journal Article | ||
| Year | 2016 | Publication | Journal of Medical Systems | Abbreviated Journal | JMS |
| Volume | 40 | Issue | 3 | Pages | 51:1-51:20 |
| Keywords | Interventional cardiology; Atherosclerosis; Coronary arteries; IVUS; calcium volume; Soft computing; Performance Reliability; Accuracy | ||||
| Abstract | Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm3, 27.79 ± 10.94 mm3, 46.44 ± 19.13 mm3 and 35.92 ± 16.44 mm3 respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student’s t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80 %. Out procedure and protocol is along the line with method previously published clinically. | ||||
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| Notes | MILAB; | Approved | no | ||
| Call Number | Admin @ si @ ABL2016 | Serial | 2729 | ||
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| Author | Jean-Pascal Jacob; Mariella Dimiccoli; Lionel Moisan | ||||
| Title | Active skeleton for bacteria modeling | Type | Journal Article | ||
| Year | 2016 | Publication | Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization | Abbreviated Journal | CMBBE |
| Volume | 5 | Issue | 4 | Pages | 274-286 |
| Keywords | Bacteria modelling; medial axis; active contours; active skeleton; shape contraints | ||||
| Abstract | The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modeling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness, orientation), an improved boundary accuracy in noisy images, and a natural bacteria-centered coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimizing an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at this http URL | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ JDM2016 | Serial | 2711 | ||
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| Author | Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera | ||||
| Title | Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History | Type | Journal Article | ||
| Year | 2016 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 28 | Issue | 8 | Pages | 1548-1568 |
| Keywords | Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal | ||||
| Abstract | Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research. | ||||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ COC2016 | Serial | 2718 | ||
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| Author | Antonio Hernandez; Sergio Escalera; Stan Sclaroff | ||||
| Title | Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures | Type | Journal Article | ||
| Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 118 | Issue | 1 | Pages | 49–64 |
| Keywords | Contextual rescoring; Poselets; Human pose estimation | ||||
| Abstract | In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ HES2016 | Serial | 2719 | ||
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| Author | Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas | ||||
| Title | Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices | Type | Journal Article | ||
| Year | 2016 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 175 | Issue | B | Pages | 866–876 |
| Keywords | Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices | ||||
| Abstract | During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset. | ||||
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| Notes | LAMP; 600.072; 600.068; | Approved | no | ||
| Call Number | Admin @ si @ TRM2016 | Serial | 2721 | ||
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| Author | Adriana Romero; Carlo Gatta; Gustavo Camps-Valls | ||||
| Title | Unsupervised Deep Feature Extraction for Remote Sensing Image Classification | Type | Journal Article | ||
| Year | 2016 | Publication | IEEE Transaction on Geoscience and Remote Sensing | Abbreviated Journal | TGRS |
| Volume | 54 | Issue | 3 | Pages | 1349 - 1362 |
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| Abstract | This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. | ||||
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| ISSN | 0196-2892 | ISBN | Medium | ||
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| Notes | LAMP; 600.079;MILAB | Approved | no | ||
| Call Number | Admin @ si @ RGC2016 | Serial | 2723 | ||
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| Author | Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez | ||||
| Title | Hierarchical online domain adaptation of deformable part-based models | Type | Conference Article | ||
| Year | 2016 | Publication | IEEE International Conference on Robotics and Automation | Abbreviated Journal | |
| Volume | Issue | Pages | 5536-5541 | ||
| Keywords | Domain Adaptation; Pedestrian Detection | ||||
| 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. | ||||
| Address | Stockholm; Sweden; May 2016 | ||||
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| Area | Expedition | Conference | ICRA | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ XVM2016 | Serial | 2728 | ||
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| Author | Carlos David Martinez Hinarejos; Josep Llados; Alicia Fornes; Francisco Casacuberta; Lluis de Las Heras; Joan Mas; Moises Pastor; Oriol Ramos Terrades; Joan Andreu Sanchez; Enrique Vidal; Fernando Vilariño | ||||
| Title | Context, multimodality, and user collaboration in handwritten text processing: the CoMUN-HaT project | Type | Conference Article | ||
| Year | 2016 | Publication | 3rd IberSPEECH | Abbreviated Journal | |
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| Abstract | Processing of handwritten documents is a task that is of wide interest for many
purposes, such as those related to preserve cultural heritage. Handwritten text recognition techniques have been successfully applied during the last decade to obtain transcriptions of handwritten documents, and keyword spotting techniques have been applied for searching specific terms in image collections of handwritten documents. However, results on transcription and indexing are far from perfect. In this framework, the use of new data sources arises as a new paradigm that will allow for a better transcription and indexing of handwritten documents. Three main different data sources could be considered: context of the document (style, writer, historical time, topics,. . . ), multimodal data (representations of the document in a different modality, such as the speech signal of the dictation of the text), and user feedback (corrections, amendments,. . . ). The CoMUN-HaT project aims at the integration of these different data sources into the transcription and indexing task for handwritten documents: the use of context derived from the analysis of the documents, how multimodality can aid the recognition process to obtain more accurate transcriptions (including transcription in a modern version of the language), and integration into a userin-the-loop assisted text transcription framework. This will be reflected in the construction of a transcription and indexing platform that can be used by both professional and nonprofessional users, contributing to crowd-sourcing activities to preserve cultural heritage and to obtain an accessible version of the involved corpus. |
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| Address | Lisboa; Portugal; November 2016 | ||||
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| Area | Expedition | Conference | IberSPEECH | ||
| Notes | DAG; MV; 600.097;SIAI | Approved | no | ||
| Call Number | Admin @ si @MLF2016 | Serial | 2813 | ||
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| Author | Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan | ||||
| Title | Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach | Type | Journal Article | ||
| Year | 2016 | Publication | Journal of Machine Vision and Applications | Abbreviated Journal | MVAP |
| Volume | 27 | Issue | Pages | 511-527 | |
| Keywords | particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging | ||||
| 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. |
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| Notes | MILAB; | Approved | no | ||
| Call Number | Admin @ si @ DJM2016 | Serial | 2735 | ||
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| Author | Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez | ||||
| Title | GPU-based pedestrian detection for autonomous driving | Type | Conference Article | ||
| Year | 2016 | Publication | GPU Technology Conference | Abbreviated Journal | |
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| Keywords | Pedestrian Detection; GPU | ||||
| Abstract | Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results. | ||||
| Address | Silicon Valley; San Francisco; USA; April 2016 | ||||
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| Area | Expedition | Conference | GTC | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ CSM2016 | Serial | 2737 | ||
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| Author | Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez | ||||
| Title | Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching | Type | Conference Article | ||
| Year | 2016 | Publication | GPU Technology Conference | Abbreviated Journal | |
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| Keywords | Stereo; Autonomous Driving; GPU; 3d reconstruction | ||||
| Abstract | Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms | ||||
| Address | Silicon Valley; San Francisco; USA; April 2016 | ||||
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| Area | Expedition | Conference | GTC | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ HME2016 | Serial | 2738 | ||
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| Author | German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez | ||||
| Title | The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes | Type | Conference Article | ||
| Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3234-3243 | ||
| Keywords | Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation | ||||
| Abstract | Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task | ||||
| Address | Las Vegas; USA; June 2016 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ RSM2016 | Serial | 2739 | ||
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| Author | Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez | ||||
| Title | Embedded real-time stereo estimation via Semi-Global Matching on the GPU | Type | Conference Article | ||
| Year | 2016 | Publication | 16th International Conference on Computational Science | Abbreviated Journal | |
| Volume | 80 | Issue | Pages | 143-153 | |
| Keywords | Autonomous Driving; Stereo; CUDA; 3d reconstruction | ||||
| 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. | ||||
| Address | San Diego; CA; USA; June 2016 | ||||
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| Area | Expedition | Conference | ICCS | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ HCE2016a | Serial | 2740 | ||
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