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Author C. Alejandro Parraga; Arash Akbarinia
Title Colour Constancy as a Product of Dynamic Centre-Surround Adaptation Type Conference Article
Year 2016 Publication 16th Annual meeting in Vision Sciences Society Abbreviated Journal
Volume 16 Issue 12 Pages
Keywords
Abstract Colour constancy refers to the human visual system's ability to preserve the perceived colour of objects despite changes in the illumination. Its exact mechanisms are unknown, although a number of systems ranging from retinal to cortical and memory are thought to play important roles. The strength of the perceptual shift necessary to preserve these colours is usually estimated by the vectorial distances from an ideal match (or canonical illuminant). In this work we explore how much of the colour constancy phenomenon could be explained by well-known physiological properties of V1 and V2 neurons whose receptive fields (RF) vary according to the contrast and orientation of surround stimuli. Indeed, it has been shown that both RF size and the normalization occurring between centre and surround in cortical neurons depend on the local properties of surrounding stimuli. Our stating point is the construction of a computational model which includes this dynamical centre-surround adaptation by means of two overlapping asymmetric Gaussian kernels whose variances are adjusted to the contrast of surrounding pixels to represent the changes in RF size of cortical neurons and the weights of their respective contributions are altered according to differences in centre-surround contrast and orientation. The final output of the model is obtained after convolving an image with this dynamical operator and an estimation of the illuminant is obtained by considering the contrast of the far surround. We tested our algorithm on naturalistic stimuli from several benchmark datasets. Our results show that although our model does not require any training, its performance against the state-of-the-art is highly competitive, even outperforming learning-based algorithms in some cases. Indeed, these results are very encouraging if we consider that they were obtained with the same parameters for all datasets (i.e. just like the human visual system operates).
Address Florida; USA; May 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) VSS
Notes NEUROBIT Approved no
Call Number Admin @ si @ PaA2016b Serial 2901
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Author Fadi Dornaika; Bogdan Raducanu
Title Simultaneous 3D face pose and person-specific shape estimation from a single image using a holistic approach Type Conference Article
Year 2009 Publication IEEE Workshop on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper presents a new approach for the simultaneous estimation of the 3D pose and specific shape of a previously unseen face from a single image. The face pose is not limited to a frontal view. We describe a holistic approach based on a deformable 3D model and a learned statistical facial texture model. Rather than obtaining a person-specific facial surface, the goal of this work is to compute person-specific 3D face shape in terms of a few control parameters that are used by many applications. The proposed holistic approach estimates the 3D pose parameters as well as the face shape control parameters by registering the warped texture to a statistical face texture, which is carried out by a stochastic and genetic optimizer. The proposed approach has several features that make it very attractive: (i) it uses a single grey-scale image, (ii) it is person-independent, (iii) it is featureless (no facial feature extraction is required), and (iv) its learning stage is easy. The proposed approach lends itself nicely to 3D face tracking and face gesture recognition in monocular videos. We describe extensive experiments that show the feasibility and robustness of the proposed approach.
Address Utah, USA
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1550-5790 ISBN 978-1-4244-5497-6 Medium
Area Expedition Conference (up) WACV
Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ DoR2009b Serial 1256
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Author Fadi Dornaika; Alireza Bosaghzadeh; Bogdan Raducanu
Title LSDA Solution Schemes for Modelless 3D Head Pose Estimation Type Conference Article
Year 2012 Publication IEEE Workshop on the Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 393-398
Keywords
Abstract
Address Breckenridge; USA;
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes OR;MV Approved no
Call Number Admin @ si @ DBR2012 Serial 1889
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Author Bogdan Raducanu; Fadi Dornaika
Title Appearance-based Face Recognition Using A Supervised Manifold Learning Framework Type Conference Article
Year 2012 Publication IEEE Workshop on the Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 465-470
Keywords
Abstract Many natural image sets, depicting objects whose appearance is changing due to motion, pose or light variations, can be considered samples of a low-dimension nonlinear manifold embedded in the high-dimensional observation space (the space of all possible images). The main contribution of our work is represented by a Supervised Laplacian Eigemaps (S-LE) algorithm, which exploits the class label information for mapping the original data in the embedded space. Our proposed approach benefits from two important properties: i) it is discriminative, and ii) it adaptively selects the neighbors of a sample without using any predefined neighborhood size. Experiments were conducted on four face databases and the results demonstrate that the proposed algorithm significantly outperforms many linear and non-linear embedding techniques. Although we've focused on the face recognition problem, the proposed approach could also be extended to other category of objects characterized by large variance in their appearance.
Address Breckenridge; CO; USA
Corporate Author Thesis
Publisher IEEE Xplore Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1550-5790 ISBN 978-1-4673-0233-3 Medium
Area Expedition Conference (up) WACV
Notes OR;MV Approved no
Call Number Admin @ si @ RaD2012d Serial 1890
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Author German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez
Title Vision-based Offline-Online Perception Paradigm for Autonomous Driving Type Conference Article
Year 2015 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 231 - 238
Keywords Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation
Abstract Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.
Address Hawaii; January 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area ACDC Expedition Conference (up) WACV
Notes ADAS; 600.076 Approved no
Call Number ADAS @ adas @ RRG2015 Serial 2499
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Author Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure
Title GPU-accelerated real-time stixel computation Type Conference Article
Year 2017 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 1054-1062
Keywords Autonomous Driving; GPU; Stixel
Abstract The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels. The goal of this work is to implement and evaluate a complete multi-stixel estimation pipeline on an embedded, energyefficient, GPU-accelerated device. This work presents a full GPU-accelerated implementation of stixel estimation that produces reliable results at 26 frames per second (real-time) on the Tegra X1 for disparity images of 1024×440 pixels and stixel widths of 5 pixels, and achieves more than 400 frames per second on a high-end Titan X GPU card.
Address Santa Rosa; CA; USA; March 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes ADAS; 600.118 Approved no
Call Number ADAS @ adas @ HEV2017b Serial 2812
Permanent link to this record
 
 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Support Vector Machines with Time Series Distance Kernels for Action Classification Type Conference Article
Year 2016 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 1-7
Keywords
Abstract Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function.
Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are
employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-ofthe-art on the considered datasets.
Address Lake Placid; NY (USA); March 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes HuPBA;MILAB; Approved no
Call Number Admin @ si @ BGE2016a Serial 2773
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Author Laura Lopez-Fuentes; Andrew Bagdanov; Joost Van de Weijer; Harald Skinnemoen
Title Bandwidth Limited Object Recognition in High Resolution Imagery Type Conference Article
Year 2017 Publication IEEE Winter conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance.
Address Santa Rosa; CA; USA; March 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes LAMP; 600.068; 600.109; 600.084; 600.106; 600.079; 600.120 Approved no
Call Number Admin @ si @ LBW2017 Serial 2973
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Author Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas
Title Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition Type Conference Article
Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step.
Address Aspen; Colorado; USA; March 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes DAG; 600.129; 600.140; 601.302; 601.312; 600.121 Approved no
Call Number Admin @ si @ KRF2020 Serial 3446
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Author Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas
Title Exploring Hate Speech Detection in Multimodal Publications Type Conference Article
Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research.
Address Aspen; March 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ GGG2020a Serial 3280
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Author Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski
Title Kornia: an Open Source Differentiable Computer Vision Library for PyTorch Type Conference Article
Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Aspen; Colorado; USA; March 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes MSIAU; 600.122; 600.130 Approved no
Call Number Admin @ si @ RMP2020 Serial 3291
Permanent link to this record
 
 
Author Parichehr Behjati Ardakani; Pau Rodriguez; Armin Mehri; Isabelle Hupont; Carles Fernandez; Jordi Gonzalez
Title OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network Type Conference Article
Year 2021 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 2693-2702
Keywords
Abstract Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More- over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements.
Address Virtual; January 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes ISE; 600.119; 600.098 Approved no
Call Number Admin @ si @ BRM2021 Serial 3512
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Author Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas
Title Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features Type Conference Article
Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval.
Address Aspen; Colorado; USA; March 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ MDB2020 Serial 3334
Permanent link to this record
 
 
Author Xavier Soria; Edgar Riba; Angel Sappa
Title Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection Type Conference Article
Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered.
Address Aspen; USA; March 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes MSIAU; 600.130; 601.349; 600.122 Approved no
Call Number Admin @ si @ SRS2020 Serial 3434
Permanent link to this record
 
 
Author Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas
Title Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval Type Conference Article
Year 2021 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 4022-4032
Keywords
Abstract
Address Virtual; January 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) WACV
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ MDB2021 Serial 3491
Permanent link to this record