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Andres Traumann; Gholamreza Anbarjafari; Sergio Escalera |
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Title |
Accurate 3D Measurement Using Optical Depth Information |
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2015 |
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Electronic Letters |
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EL |
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51 |
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18 |
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1420-1422 |
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A novel three-dimensional measurement technique is proposed. The methodology consists in mapping from the screen coordinates reported by the optical camera to the real world, and integrating distance gradients from the beginning to the end point, while also minimising the error through fitting pixel locations to a smooth curve. The results demonstrate accuracy of less than half a centimetre using Microsoft Kinect II. |
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HuPBA;MILAB |
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Admin @ si @ TAE2015 |
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2647 |
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Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera |
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Title |
Deep learning with self-supervision and uncertainty regularization to count fish in underwater images |
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2022 |
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PloS One |
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Plos |
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17 |
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5 |
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e0267759 |
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Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data. |
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Public Library of Science |
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HuPBA;MILAB |
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Admin @ si @ TCC2022 |
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3743 |
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Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund |
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Title |
Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields |
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Journal Article |
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2016 |
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Pattern Recognition Letters |
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PRL |
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80 |
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208–215 |
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This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset.
The approach shows that simple multi-modal features with the power of the MMSSL
paradigm can achieve better performance than state of the art results on the same dataset. |
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HuPBA; ISE;MILAB; 600.098; 600.119 |
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Admin @ si @ TEG2016 |
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2843 |
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Author |
Jun Wan; Sergio Escalera; Francisco Perales; Josef Kittler |
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Title |
Articulated Motion and Deformable Objects |
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2018 |
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Pattern Recognition |
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PR |
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79 |
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55-64 |
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This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field. |
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HUPBA; no proj;MILAB |
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no |
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Admin @ si @ WEP2018 |
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3126 |
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Author |
Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera |
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Title |
RGB-D-based Human Motion Recognition with Deep Learning: A Survey |
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Journal Article |
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2018 |
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Computer Vision and Image Understanding |
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CVIU |
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171 |
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118-139 |
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Human motion recognition; RGB-D data; Deep learning; Survey |
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Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research. |
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HUPBA; no proj;MILAB |
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Admin @ si @ WLO2018 |
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3123 |
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