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Author | Hunor Laczko; Meysam Madadi; Sergio Escalera; Jordi Gonzalez | ||||
Title | A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping | Type | Conference Article | ||
Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 8709-8718 | ||
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Abstract | RGB cloth generation has been deeply studied in the related literature, however, 3D garment generation remains an open problem. In this paper, we build a conditional variational autoencoder for 3D garment generation and draping. We propose a pyramid network to add garment details progressively in a canonical space, i.e. unposing and unshaping the garments w.r.t. the body. We study conditioning the network on surface normal UV maps, as an intermediate representation, which is an easier problem to optimize than 3D coordinates. Our results on two public datasets, CLOTH3D and CAPE, show that our model is robust, controllable in terms of detail generation by the use of multi-resolution pyramids, and achieves state-of-the-art results that can highly generalize to unseen garments, poses, and shapes even when training with small amounts of data. | ||||
Address | Waikoloa; Hawai; USA; January 2024 | ||||
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 | WACV | ||
Notes | ISE; HUPBA | Approved | no | ||
Call Number | Admin @ si @ LME2024 | Serial | 3996 | ||
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Author | Joan M. Nuñez | ||||
Title | Computer vision techniques for characterization of finger joints in X-ray image | Type | Report | ||
Year | 2011 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume | 165 | Issue | Pages | ||
Keywords | Rheumatoid arthritis, X-ray, Sharp Van der Heijde, joint characterization, sclerosis detection, bone detection, edge, ridge | ||||
Abstract | Rheumatoid arthritis (RA) is an autoimmune inflammatory type of arthritis which mainly affects hands on its first stages. Though it is a chronic disease and there is no cure for it, treatments require an accurate assessment of illness evolution. Such assessment is based on evaluation of hand X-ray images by using one of the several available semi-quantitative methods. This task requires highly trained medical personnel. That is why the automation of the assessment would allow professionals to save time and effort. Two stages are involved in this task. Firstly, the joint detection, afterwards, the joint characterization. Unlike the little existing previous work, this contribution clearly separates those two stages and sets the foundations of a modular assessment system focusing on the characterization stage. A hand joint dataset is created and an accurate data analysis is achieved in order to identify relevant features. Since the sclerosis and the lower bone were decided to be the most important features, different computer vision techniques were used in order to develop a detector system for both of them. Joint space width measures are provided and their correlation with Sharp-Van der Heijde is verified | ||||
Address | Bellaterra (Barcelona) | ||||
Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
Publisher | Place of Publication | Editor | Dr. Fernando Vilariño and Dra. Debora Gil | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | MV;IAM; | Approved | no | ||
Call Number | IAM @ iam @ Nuñ2011 | Serial | 1795 | ||
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Author | Sergio Vera | ||||
Title | Finger joint modelling from hand X-ray images for assessing rheumatoid arthritis | Type | Report | ||
Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume | 164 | Issue | Pages | ||
Keywords | Rheumatoid arthritis; joint detection; X-ray; Van der Heijde score | ||||
Abstract | Rheumatoid arthritis is an autoimmune, systemic, inflammatory disorder that mainly af- fects bone joints. While there is no cure for this disease, continuous advances on palliative treatments require frequent verification of patient’s illness evolution. Such evolution is mea- sured through several available semi-quantitative methods that require evaluation of hand and foot X-ray images. Accurate assessment is a time consuming task that requires highly trained personnel. This hinders a generalized use in clinical practice for early diagnose and disease follow-up. In the context of the automatization of such evaluation methods we present a method for detection and characterization of finger joints in hand radiography images. Several measures for assessing the reduction of joint space width are proposed. We compare for the first time such measures to the Van der Heijde score, the gold standard method for rheumatoid arthritis assessment. The proposed method outperforms existing strategies with a detection rate above 95%. Our comparison to Van der Heijde index shows a promising correlation that encourages further research. | ||||
Address | |||||
Corporate Author | Thesis | Master's thesis | |||
Publisher | Place of Publication | Bellaterra 01893, Barcelona, Spain | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | IAM | Approved | no | ||
Call Number | IAM @ iam @ Ver2010 | Serial | 1661 | ||
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Author | Xavier Baro; Sergio Escalera; Petia Radeva; Jordi Vitria | ||||
Title | Visual Content Layer for Scalable Recognition in Urban Image Databases, Internet Multimedia Search and Mining | Type | Conference Article | ||
Year | 2009 | Publication | 10th IEEE International Conference on Multimedia and Expo | Abbreviated Journal | |
Volume | Issue | Pages | 1616–1619 | ||
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Abstract | Rich online map interaction represents a useful tool to get multimedia information related to physical places. With this type of systems, users can automatically compute the optimal route for a trip or to look for entertainment places or hotels near their actual position. Standard maps are defined as a fusion of layers, where each one contains specific data such height, streets, or a particular business location. In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (> 500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. This allows an efficient and scalable way of accessing maps by visual content. | ||||
Address | New York (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 | 978-1-4244-4291-1 | Medium | ||
Area | Expedition | Conference | ICME | ||
Notes | OR;MILAB;HuPBA;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ BER2009 | Serial | 1189 | ||
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Author | Jose Manuel Alvarez; Felipe Lumbreras; Theo Gevers; Antonio Lopez | ||||
Title | Geographic Information for vision-based Road Detection | Type | Conference Article | ||
Year | 2010 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
Volume | Issue | Pages | 621–626 | ||
Keywords | road detection | ||||
Abstract | Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach. | ||||
Address | San Diego; CA; 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 | IV | ||
Notes | ADAS;ISE | Approved | no | ||
Call Number | ADAS @ adas @ ALG2010 | Serial | 1428 | ||
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Author | Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez | ||||
Title | Road Scene Segmentation from a Single Image | Type | Conference Article | ||
Year | 2012 | Publication | 12th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 7578 | Issue | VII | Pages | 376-389 |
Keywords | road detection | ||||
Abstract | Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images. From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined |
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Address | Florence, Italy | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-33785-7 | Medium | |
Area | Expedition | Conference | ECCV | ||
Notes | ADAS;ISE | Approved | no | ||
Call Number | Admin @ si @ AGL2012; ADAS @ adas @ agl2012a | Serial | 2022 | ||
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Author | Jose Manuel Alvarez | ||||
Title | Combining Context and Appearance for Road Detection | Type | Book Whole | ||
Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Road traffic crashes have become a major cause of death and injury throughout the world.
Hence, in order to improve road safety, the automobile manufacture is moving towards the development of vehicles with autonomous functionalities such as keeping in the right lane, safe distance keeping between vehicles or regulating the speed of the vehicle according to the traffic conditions. A key component of these systems is vision–based road detection that aims to detect the free road surface ahead the moving vehicle. Detecting the road using a monocular vision system is very challenging since the road is an outdoor scenario imaged from a mobile platform. Hence, the detection algorithm must be able to deal with continuously changing imaging conditions such as the presence ofdifferent objects (vehicles, pedestrians), different environments (urban, highways, off–road), different road types (shape, color), and different imaging conditions (varying illumination, different viewpoints and changing weather conditions). Therefore, in this thesis, we focus on vision–based road detection using a single color camera. More precisely, we first focus on analyzing and grouping pixels according to their low–level properties. In this way, two different approaches are presented to exploit color and photometric invariance. Then, we focus the research of the thesis on exploiting context information. This information provides relevant knowledge about the road not using pixel features from road regions but semantic information from the analysis of the scene. In this way, we present two different approaches to infer the geometry of the road ahead the moving vehicle. Finally, we focus on combining these context and appearance (color) approaches to improve the overall performance of road detection algorithms. The qualitative and quantitative results presented in this thesis on real–world driving sequences show that the proposed method is robust to varying imaging conditions, road types and scenarios going beyond the state–of–the–art. |
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Address | |||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Theo Gevers | |
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-937261-8-8 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Alv2010 | Serial | 1454 | ||
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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 | |
Volume | Issue | Pages | |||
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 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
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 | Yipeng Sun; Zihan Ni; Chee-Kheng Chng; Yuliang Liu; Canjie Luo; Chun Chet Ng; Junyu Han; Errui Ding; Jingtuo Liu; Dimosthenis Karatzas; Chee Seng Chan; Lianwen Jin | ||||
Title | ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling – RRC-LSVT | Type | Conference Article | ||
Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1557-1562 | ||
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Abstract | Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavily relies on the amount of fully annotated training data, which is costly and in-efficient to obtain. To scale up the amount of training data while keeping the labeling procedure cost-effective, this competition introduces a new challenge on Large-scale Street View Text with Partial Labeling (LSVT), providing 50, 000 and 400, 000 images in full and weak annotations, respectively. This competition aims to explore the abilities of state-of-the-art methods to detect and recognize text instances from large-scale street view images, closing the gap between research benchmarks and real applications. During the competition period, a total of 41 teams participated in the two proposed tasks with 132 valid submissions, ie, text detection and end-to-end text spotting. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of the ICDAR 2019-LSVT challenge. | ||||
Address | Sydney; Australia; September 2019 | ||||
Corporate Author | Thesis | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.129; 600.121 | Approved | no | ||
Call Number | Admin @ si @ SNC2019 | Serial | 3339 | ||
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Author | German Ros; J. Guerrero; Angel Sappa; Daniel Ponsa; Antonio Lopez | ||||
Title | Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios | Type | Conference Article | ||
Year | 2013 | Publication | 24th British Machine Vision Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | SLAM | ||||
Abstract | Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC. Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach. |
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Address | Bristol; UK; September 2013 | ||||
Corporate Author | Thesis | ||||
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Area | Expedition | Conference | BMVC | ||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ RGS2013b; ADAS @ adas @ | Serial | 2274 | ||
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Author | Justine Giroux; Mohammad Reza Karimi Dastjerdi; Yannick Hold-Geoffroy; Javier Vazquez; Jean François Lalonde | ||||
Title | Towards a Perceptual Evaluation Framework for Lighting Estimation | Type | Conference Article | ||
Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | rogress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms. | ||||
Address | Seattle; USA; June 2024 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | MACO; CIC | Approved | no | ||
Call Number | Admin @ si @ GDH2024 | Serial | 3999 | ||
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Author | Mohammad Momeny; Ali Asghar Neshat; Ahmad Jahanbakhshi; Majid Mahmoudi; Yiannis Ampatzidis; Petia Radeva | ||||
Title | Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN | Type | Journal Article | ||
Year | 2023 | Publication | Food Control | Abbreviated Journal | FC |
Volume | 147 | Issue | Pages | 109554 | |
Keywords | |||||
Abstract | Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU. | ||||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ MNJ2023 | Serial | 3882 | ||
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Author | Carola Figueroa Flores; David Berga; Joost Van de Weijer; Bogdan Raducanu | ||||
Title | Saliency for free: Saliency prediction as a side-effect of object recognition | Type | Journal Article | ||
Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 150 | Issue | Pages | 1-7 | |
Keywords | Saliency maps; Unsupervised learning; Object recognition | ||||
Abstract | Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods. | ||||
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Notes | LAMP; 600.147; 600.120 | Approved | no | ||
Call Number | Admin @ si @ FBW2021 | Serial | 3559 | ||
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Author | Rahma Kalboussi; Aymen Azaza; Joost Van de Weijer; Mehrez Abdellaoui; Ali Douik | ||||
Title | Object proposals for salient object segmentation in videos | Type | Journal Article | ||
Year | 2020 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume | 79 | Issue | 13 | Pages | 8677-8693 |
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Abstract | Salient object segmentation in videos is generally broken up in a video segmentation part and a saliency assignment part. Recently, object proposals, which are used to segment the image, have had significant impact on many computer vision applications, including image segmentation, object detection, and recently saliency detection in still images. However, their usage has not yet been evaluated for salient object segmentation in videos. Therefore, in this paper, we investigate the application of object proposals to salient object segmentation in videos. In addition, we propose a new motion feature derived from the optical flow structure tensor for video saliency detection. Experiments on two standard benchmark datasets for video saliency show that the proposed motion feature improves saliency estimation results, and that object proposals are an efficient method for salient object segmentation. Results on the challenging SegTrack v2 and Fukuchi benchmark data sets show that we significantly outperform the state-of-the-art. | ||||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | KAW2020 | Serial | 3504 | ||
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Author | Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras | ||||
Title | Intrinsic Image Evaluation On Synthetic Complex Scenes | Type | Conference Article | ||
Year | 2013 | Publication | 20th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | 285 - 289 | ||
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Abstract | Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth
procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes. |
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Address | Melbourne; Australia; September 2013 | ||||
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Area | Expedition | Conference | ICIP | ||
Notes | CIC; 600.048; 600.052; 600.051 | Approved | no | ||
Call Number | Admin @ si @ BSW2013 | Serial | 2264 | ||
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