| Records |
| Author |
Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer |
| Title |
On Implicit Attribute Localization for Generalized Zero-Shot Learning |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Signal Processing Letters |
Abbreviated Journal |
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| Volume |
28 |
Issue |
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Pages |
872 - 876 |
| Keywords |
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Abstract  |
Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions. Since attributes are often related to specific parts of objects, many recent works focus on discovering discriminative regions. However, these methods usually require additional complex part detection modules or attention mechanisms. In this paper, 1) we show that common ZSL backbones (without explicit attention nor part detection) can implicitly localize attributes, yet this property is not exploited. 2) Exploiting it, we then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance when compared with more complex state-of-the-art methods. Our findings provide useful insight for designing future GZSL methods, and SELAR provides an easy to implement yet strong baseline. |
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| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
YWH2021 |
Serial |
3563 |
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| Author |
Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta |
| Title |
Diaphragm border detection in coronary X-ray angiographies: New method and applications |
Type |
Journal Article |
| Year |
2014 |
Publication |
Computerized Medical Imaging and Graphics |
Abbreviated Journal |
CMIG |
| Volume |
38 |
Issue |
4 |
Pages |
296-305 |
| Keywords |
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| Abstract |
X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation. |
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| Notes |
MILAB; LAMP; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ PCR2014 |
Serial |
2468 |
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| Author |
Albert Gordo; Alicia Fornes; Ernest Valveny |
| Title |
Writer identification in handwritten musical scores with bags of notes |
Type |
Journal Article |
| Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
46 |
Issue |
5 |
Pages |
1337-1345 |
| Keywords |
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| Abstract |
Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. |
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Edition |
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0031-3203 |
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| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ GFV2013 |
Serial |
2307 |
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| Author |
Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera |
| Title |
Automatic Digital Biometry Analysis based on Depth Maps |
Type |
Journal Article |
| Year |
2013 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
| Volume |
64 |
Issue |
9 |
Pages |
1316-1325 |
| Keywords |
Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis |
| Abstract |
World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments. |
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Elsevier |
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Conference |
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| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ RCR2013 |
Serial |
2252 |
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| Author |
Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |
| Title |
Evaluation of the Effect of Improper Segmentation on Word Spotting |
Type |
Journal Article |
| Year |
2019 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
| Volume |
22 |
Issue |
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Pages |
361-374 |
| Keywords |
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| Abstract |
Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods. |
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Conference |
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| Notes |
DAG; 600.097; 600.084; 600.121; 600.140; 600.129 |
Approved |
no |
| Call Number |
Admin @ si @ DNL2019 |
Serial |
3455 |
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| Author |
Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate |
| Title |
An overview of incremental feature extraction methods based on linear subspaces |
Type |
Journal Article |
| Year |
2018 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
| Volume |
145 |
Issue |
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Pages |
219-235 |
| Keywords |
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| Abstract |
With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind. |
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Edition |
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| ISSN |
0950-7051 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ DFH2018 |
Serial |
3090 |
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| Author |
Mohammad N. S. Jahromi; Pau Buch Cardona; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund; Gholamreza Anbarjafari |
| Title |
Privacy-Constrained Biometric System for Non-cooperative Users |
Type |
Journal Article |
| Year |
2019 |
Publication |
Entropy |
Abbreviated Journal |
ENTROPY |
| Volume |
21 |
Issue |
11 |
Pages |
1033 |
| Keywords |
biometric recognition; multimodal-based human identification; privacy; deep learning |
| Abstract |
With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance. |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ NBA2019 |
Serial |
3313 |
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| Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
| Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
Type |
Journal Article |
| Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
27 |
Issue |
8 |
Pages |
875–881 |
| Keywords |
ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
| Abstract |
Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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800 |
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Conference |
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| Notes |
MILAB;MV;SIAI |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
Serial |
647 |
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| Author |
Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
| Title |
Motility bar: a new tool for motility analysis of endoluminal videos |
Type |
Journal Article |
| Year |
2015 |
Publication |
Computers in Biology and Medicine |
Abbreviated Journal |
CBM |
| Volume |
65 |
Issue |
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Pages |
320-330 |
| Keywords |
Small intestine; Motility; WCE; Computer vision; Image classification |
| Abstract |
Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information. |
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| Notes |
MILAB;MV |
Approved |
no |
| Call Number |
Admin @ si @ DSR2015 |
Serial |
2635 |
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| Author |
Santiago Segui; Michal Drozdzal; Fernando Vilariño; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
| Title |
Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy |
Type |
Journal Article |
| Year |
2012 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
| Volume |
16 |
Issue |
6 |
Pages |
1341-1352 |
| Keywords |
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| Abstract |
Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content – clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media. |
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Edition |
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| ISSN |
1089-7771 |
ISBN |
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Medium |
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| Area |
800 |
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| Notes |
MILAB; MV; OR;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ SDV2012 |
Serial |
2124 |
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| Author |
Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio |
| Title |
Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source |
Type |
Journal Article |
| Year |
2015 |
Publication |
Journal of Medical Imaging and Health Informatics |
Abbreviated Journal |
JMIHI |
| Volume |
5 |
Issue |
2 |
Pages |
192-201 |
| Keywords |
CONTEXTUAL CLASSIFICATION; PET/CT; SUPERVISED LEARNING; TUMOR SEGMENTATION; WHOLE BODY |
| Abstract |
Whole-body 3D PET/CT tumoral volume segmentation provides relevant diagnostic and prognostic information in clinical oncology and nuclear medicine. Carrying out this procedure manually by a medical expert is time consuming and suffers from inter- and intra-observer variabilities. In this paper, a completely automatic approach to this task is presented. First, the problem is stated and described both in clinical and technological terms. Then, a novel supervised learning segmentation framework is introduced. The segmentation by learning approach is defined within a Cascade of Adaboost classifiers and a 3D contextual proposal of Multiscale Stacked Sequential Learning. Segmentation accuracy results on 200 Breast Cancer whole body PET/CT volumes show mean 49% sensitivity, 99.993% specificity and 39% Jaccard overlap Index, which represent good performance results both at the clinical and technological level. |
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| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ SED2015 |
Serial |
2584 |
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| Author |
Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva |
| Title |
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation |
Type |
Journal Article |
| Year |
2017 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
155 |
Issue |
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Pages |
55-69 |
| Keywords |
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| Abstract |
While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods. |
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| Notes |
MILAB; 601.235 |
Approved |
no |
| Call Number |
Admin @ si @ DBT2017 |
Serial |
2714 |
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| Author |
Jaume Amores |
| Title |
MILDE: multiple instance learning by discriminative embedding |
Type |
Journal Article |
| Year |
2015 |
Publication |
Knowledge and Information Systems |
Abbreviated Journal |
KAIS |
| Volume |
42 |
Issue |
2 |
Pages |
381-407 |
| Keywords |
Multi-instance learning; Codebook; Bag of words |
| Abstract |
While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. |
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Springer London |
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| ISSN |
0219-1377 |
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| Notes |
ADAS; 601.042; 600.057; 600.076 |
Approved |
no |
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Admin @ si @ Amo2015 |
Serial |
2383 |
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| Author |
Javier Vazquez; J. Kevin O'Regan; Maria Vanrell; Graham D. Finlayson |
| Title |
A new spectrally sharpened basis to predict colour naming, unique hues, and hue cancellation |
Type |
Journal Article |
| Year |
2012 |
Publication |
Journal of Vision |
Abbreviated Journal |
VSS |
| Volume |
12 |
Issue |
6 (7) |
Pages |
1-14 |
| Keywords |
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| Abstract |
When light is reflected off a surface, there is a linear relation between the three human photoreceptor responses to the incoming light and the three photoreceptor responses to the reflected light. Different colored surfaces have different linear relations. Recently, Philipona and O'Regan (2006) showed that when this relation is singular in a mathematical sense, then the surface is perceived as having a highly nameable color. Furthermore, white light reflected by that surface is perceived as corresponding precisely to one of the four psychophysically measured unique hues. However, Philipona and O'Regan's approach seems unrelated to classical psychophysical models of color constancy. In this paper we make this link. We begin by transforming cone sensors to spectrally sharpened counterparts. In sharp color space, illumination change can be modeled by simple von Kries type scalings of response values within each of the spectrally sharpened response channels. In this space, Philipona and O'Regan's linear relation is captured by a simple Land-type color designator defined by dividing reflected light by incident light. This link between Philipona and O'Regan's theory and Land's notion of color designator gives the model biological plausibility. We then show that Philipona and O'Regan's singular surfaces are surfaces which are very close to activating only one or only two of such newly defined spectrally sharpened sensors, instead of the usual three. Closeness to zero is quantified in a new simplified measure of singularity which is also shown to relate to the chromaticness of colors. As in Philipona and O'Regan's original work, our new theory accounts for a large variety of psychophysical color data. |
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Admin @ si @ VOV2012 |
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1998 |
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| Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira |
| Title |
Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives |
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Journal Article |
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2016 |
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Robotics and Autonomous Systems |
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RAS |
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83 |
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312-325 |
| Keywords |
Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives |
| Abstract |
When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. |
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Elsevier B.V. |
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ADAS; 600.086, 600.076 |
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Admin @ si @OSS2016a |
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2806 |
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