| Records |
| Author |
Laura Lopez-Fuentes; Joost Van de Weijer; Manuel Gonzalez-Hidalgo; Harald Skinnemoen; Andrew Bagdanov |
| Title |
Review on computer vision techniques in emergency situations |
Type |
Journal Article |
| Year |
2018 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
77 |
Issue |
13 |
Pages |
17069–17107 |
Keywords  |
Emergency management; Computer vision; Decision makers; Situational awareness; Critical situation |
| Abstract |
In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies. |
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LAMP; 600.068; 600.120 |
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Admin @ si @ LWG2018 |
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3041 |
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Andre Litvin; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Thomas B. Moeslund; Gholamreza Anbarjafari |
| Title |
A Novel Deep Network Architecture for Reconstructing RGB Facial Images from Thermal for Face Recognition |
Type |
Journal Article |
| Year |
2019 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
78 |
Issue |
18 |
Pages |
25259–25271 |
| Keywords |
Fully convolutional networks; FusionNet; Thermal imaging; Face recognition |
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This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face recognition scenarios. The proposed method is based on the FusionNet architecture and increases robustness against overfitting using dropout after bridge connections, randomised leaky ReLUs (RReLUs), and orthogonal regularization. Furthermore, we propose to use a decoding block with resize convolution instead of transposed convolution to improve final RGB face image generation. To validate our proposed network architecture, we train a face classifier and compare its face recognition rate on the reconstructed RGB images from the proposed architecture, to those when reconstructing images with the original FusionNet, as well as when using the original RGB images. As a result, we are introducing a new architecture which leads to a more accurate network. |
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HuPBA; no menciona |
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no |
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Admin @ si @ LNE2019 |
Serial |
3318 |
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Naveen Onkarappa; Angel Sappa |
| Title |
Synthetic sequences and ground-truth flow field generation for algorithm validation |
Type |
Journal Article |
| Year |
2015 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
74 |
Issue |
9 |
Pages |
3121-3135 |
| Keywords |
Ground-truth optical flow; Synthetic sequence; Algorithm validation |
| Abstract |
Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems. |
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Springer US |
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1380-7501 |
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ADAS; 600.055; 601.215; 600.076 |
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Admin @ si @ OnS2014b |
Serial |
2472 |
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Egils Avots; Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Baro; Paul Pallin; Gholamreza Anbarjafari |
| Title |
From 2D to 3D geodesic-based garment matching |
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Journal Article |
| Year |
2019 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
78 |
Issue |
18 |
Pages |
25829–25853 |
| Keywords |
Shape matching; Geodesic distance; Texture mapping; RGBD image processing; Gaussian mixture model |
| Abstract |
A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by root mean square error (RMS) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset. |
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HuPBA; ISE; 600.098; 600.119; 602.133 |
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Admin @ si @ AME2019 |
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3317 |
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Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
| Title |
Rendering ground truth data sets to detect shadows cast by static objects in outdoors |
Type |
Journal Article |
| Year |
2014 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
70 |
Issue |
1 |
Pages |
557-571 |
| Keywords |
Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection |
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In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically. |
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Springer US |
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1380-7501 |
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LAMP; |
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no |
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Admin @ si @ ISR2014 |
Serial |
2229 |
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