| Records |
| Author |
Miquel Angel Piera; Jose Luis Muñoz; Debora Gil; Gonzalo Martin; Jordi Manzano |
| Title |
A Socio-Technical Simulation Model for the Design of the Future Single Pilot Cockpit: An Opportunity to Improve Pilot Performance |
Type |
Journal Article |
| Year |
2022 |
Publication  |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
10 |
Issue |
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Pages |
22330-22343 |
| Keywords |
Human factors ; Performance evaluation ; Simulation; Sociotechnical systems ; System performance |
| Abstract |
The future deployment of single pilot operations must be supported by new cockpit computer services. Such services require an adaptive context-aware integration of technical functionalities with the concurrent tasks that a pilot must deal with. Advanced artificial intelligence supporting services and improved communication capabilities are the key enabling technologies that will render future cockpits more integrated with the present digitalized air traffic management system. However, an issue in the integration of such technologies is the lack of socio-technical analysis in the design of these teaming mechanisms. A key factor in determining how and when a service support should be provided is the dynamic evolution of pilot workload. This paper investigates how the socio-technical model-based systems engineering approach paves the way for the design of a digital assistant framework by formalizing this workload. The model was validated in an Airbus A-320 cockpit simulator, and the results confirmed the degraded pilot behavioral model and the performance impact according to different contextual flight deck information. This study contributes to practical knowledge for designing human-machine task-sharing systems. |
| Address |
Feb 2022 |
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IAM; |
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no |
| Call Number |
Admin @ si @ PMG2022 |
Serial |
3697 |
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| Author |
Debora Gil; Aura Hernandez-Sabate; Julien Enconniere; Saryani Asmayawati; Pau Folch; Juan Borrego-Carazo; Miquel Angel Piera |
| Title |
E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
10 |
Issue |
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Pages |
7489-7503 |
| Keywords |
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| Abstract |
More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.
This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches. |
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| Notes |
IAM; 600.139; 600.118; 600.145 |
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no |
| Call Number |
Admin @ si @ GHE2022 |
Serial |
3721 |
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| Author |
David Castells; Vinh Ngo; Juan Borrego-Carazo; Marc Codina; Carles Sanchez; Debora Gil; Jordi Carrabina |
| Title |
A Survey of FPGA-Based Vision Systems for Autonomous Cars |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACESS |
| Volume |
10 |
Issue |
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Pages |
132525-132563 |
| Keywords |
Autonomous automobile; Computer vision; field programmable gate arrays; reconfigurable architectures |
| Abstract |
On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges. |
| Address |
16 December 2022 |
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IEEE |
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| Notes |
IAM; 600.166 |
Approved |
no |
| Call Number |
Admin @ si @ CNB2022 |
Serial |
3760 |
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| Author |
Cristhian A. Aguilera-Carrasco; Luis Felipe Gonzalez-Böhme; Francisco Valdes; Francisco Javier Quitral Zapata; Bogdan Raducanu |
| Title |
A Hand-Drawn Language for Human–Robot Collaboration in Wood Stereotomy |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
11 |
Issue |
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Pages |
100975 - 100985 |
| Keywords |
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| Abstract |
This study introduces a novel, hand-drawn language designed to foster human-robot collaboration in wood stereotomy, central to carpentry and joinery professions. Based on skilled carpenters’ line and symbol etchings on timber, this language signifies the location, geometry of woodworking joints, and timber placement within a framework. A proof-of-concept prototype has been developed, integrating object detectors, keypoint regression, and traditional computer vision techniques to interpret this language and enable an extensive repertoire of actions. Empirical data attests to the language’s efficacy, with the successful identification of a specific set of symbols on various wood species’ sawn surfaces, achieving a mean average precision (mAP) exceeding 90%. Concurrently, the system can accurately pinpoint critical positions that facilitate robotic comprehension of carpenter-indicated woodworking joint geometry. The positioning error, approximately 3 pixels, meets industry standards. |
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LAMP |
Approved |
no |
| Call Number |
Admin @ si @ AGV2023 |
Serial |
3969 |
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| Author |
Armin Mehri; Parichehr Behjati; Angel Sappa |
| Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
11 |
Issue |
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Pages |
11529-11540 |
| Keywords |
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| Abstract |
Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to 0.19∼2.3dB , while it is memory efficient. |
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MSIAU |
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no |
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Admin @ si @ MBS2023 |
Serial |
3876 |
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| Author |
Armin Mehri; Parichehr Behjati; Dario Carpio; Angel Sappa |
| Title |
SRFormer: Efficient Yet Powerful Transformer Network for Single Image Super Resolution |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
11 |
Issue |
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Pages |
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| Abstract |
Recent breakthroughs in single image super resolution have investigated the potential of deep Convolutional Neural Networks (CNNs) to improve performance. However, CNNs based models suffer from their limited fields and their inability to adapt to the input content. Recently, Transformer based models were presented, which demonstrated major performance gains in Natural Language Processing and Vision tasks while mitigating the drawbacks of CNNs. Nevertheless, Transformer computational complexity can increase quadratically for high-resolution images, and the fact that it ignores the original structures of the image by converting them to the 1D structure can make it problematic to capture the local context information and adapt it for real-time applications. In this paper, we present, SRFormer, an efficient yet powerful Transformer-based architecture, by making several key designs in the building of Transformer blocks and Transformer layers that allow us to consider the original structure of the image (i.e., 2D structure) while capturing both local and global dependencies without raising computational demands or memory consumption. We also present a Gated Multi-Layer Perceptron (MLP) Feature Fusion module to aggregate the features of different stages of Transformer blocks by focusing on inter-spatial relationships while adding minor computational costs to the network. We have conducted extensive experiments on several super-resolution benchmark datasets to evaluate our approach. SRFormer demonstrates superior performance compared to state-of-the-art methods from both Transformer and Convolutional networks, with an improvement margin of 0.1∼0.53dB . Furthermore, while SRFormer has almost the same model size, it outperforms SwinIR by 0.47% and inference time by half the time of SwinIR. The code will be available on GitHub. |
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MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ MBC2023 |
Serial |
3887 |
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| Author |
Agata Lapedriza; David Masip; Jordi Vitria |
| Title |
On the Use of Independent Tasks for Face Recognition |
Type |
Conference Article |
| Year |
2008 |
Publication |
IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Issue |
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Pages |
1–6 |
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CVPR |
| Notes |
OR; MV |
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no |
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BCNPCL @ bcnpcl @ LMV2008b |
Serial |
1043 |
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| Author |
Michal Drozdzal; Laura Igual; Petia Radeva; Jordi Vitria; Carolina Malagelada; Fernando Azpiroz |
| Title |
Aligning Endoluminal Scene Sequences in Wireless Capsule Endoscopy |
Type |
Conference Article |
| Year |
2010 |
Publication |
IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis |
Abbreviated Journal |
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Issue |
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Pages |
117–124 |
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| Abstract |
Intestinal motility analysis is an important examination in detection of various intestinal malfunctions. One of the big challenges of automatic motility analysis is how to compare sequence of images and extract dynamic paterns taking into account the high deformability of the intestine wall as well as the capsule motion. From clinical point of view the ability to align endoluminal scene sequences will help to find regions of similar intestinal activity and in this way will provide a valuable information on intestinal motility problems. This work, for first time, addresses the problem of aligning endoluminal sequences taking into account motion and structure of the intestine. To describe motility in the sequence, we propose different descriptors based on the Sift Flow algorithm, namely: (1) Histograms of Sift Flow Directions to describe the flow course, (2) Sift Descriptors to represent image intestine structure and (3) Sift Flow Magnitude to quantify intestine deformation. We show that the merge of all three descriptors provides robust information on sequence description in terms of motility. Moreover, we develop a novel methodology to rank the intestinal sequences based on the expert feedback about relevance of the results. The experimental results show that the selected descriptors are useful in the alignment and similarity description and the proposed method allows the analysis of the WCE. |
| Address |
San Francisco; CA; USA; June 2010 |
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2160-7508 |
ISBN |
978-1-4244-7029-7 |
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MMBIA |
| Notes |
OR;MILAB;MV |
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no |
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BCNPCL @ bcnpcl @ DIR2010 |
Serial |
1316 |
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| Author |
Jaume Amores; N. Sebe; Petia Radeva |
| Title |
Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors |
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Miscellaneous |
| Year |
2005 |
Publication |
IEEE Computer Society, International Conference on Computer Vision and Pattern Recognition (CVPR’05), 2(2):769–774 |
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San Diego, CA (USA) |
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MILAB |
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no |
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ADAS @ adas @ ASR2005a |
Serial |
541 |
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| Author |
Gemma Roig; Xavier Boix; F. de la Torre; Joan Serrat; C. Vilella |
| Title |
Hierarchical CRF with product label spaces for parts-based Models |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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Pages |
657-664 |
| Keywords |
Shape; Computational modeling; Principal component analysis; Random variables; Color; Upper bound; Facial features |
| Abstract |
Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset. |
| Address |
Santa Barbara, CA, USA, 2011 |
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FG |
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ADAS |
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no |
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Admin @ si @ RBT2011 |
Serial |
1862 |
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| Author |
Ivo Everts; Jan van Gemert; Theo Gevers |
| Title |
Evaluation of Color STIPs for Human Action Recognition |
Type |
Conference Article |
| Year |
2013 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
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2850-2857 |
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This paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. This enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF~sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition. |
| Address |
Portland; oregon; June 2013 |
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1063-6919 |
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CVPR |
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ALTRES;ISE |
Approved |
no |
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Admin @ si @ EGG2013 |
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2364 |
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| Author |
Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga |
| Title |
Saliency Estimation Using a Non-Parametric Low-Level Vision Model |
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Conference Article |
| Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
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433-440 |
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Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms |
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Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks. |
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Colorado Springs |
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1063-6919 |
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978-1-4577-0394-2 |
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CVPR |
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CIC |
Approved |
no |
| Call Number |
Admin @ si @ MVO2011 |
Serial |
1757 |
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| Author |
Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez |
| Title |
A Coarse-to-fine Approach for fast Deformable Object Detection |
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Conference Article |
| Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
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1353-1360 |
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Colorado Springs; USA |
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CVPR |
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ISE |
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no |
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Admin @ si @ PVG2011 |
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1764 |
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| Author |
Miguel Oliveira; Angel Sappa; V.Santos |
| Title |
Unsupervised Local Color Correction for Coarsely Registered Images |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
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201-208 |
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The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time. |
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Colorado Springs |
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1063-6919 |
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978-1-4577-0394-2 |
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CVPR |
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ADAS |
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no |
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Admin @ si @ OSS2011; ADAS @ adas @ |
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1766 |
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| Author |
Albert Gordo; Florent Perronnin |
| Title |
Asymmetric Distances for Binary Embeddings |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
729 - 736 |
| Keywords |
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| Abstract |
In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH. |
| Address |
Providence, RI |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-1-4577-0394-2 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ GoP2011; IAM @ iam @ GoP2011 |
Serial |
1817 |
| Permanent link to this record |
