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| Author |
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 |
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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 |
| Abstract |
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 |
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3318 |
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Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund |
| Title |
Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams |
Type |
Journal Article |
| Year |
2016 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
75 |
Issue |
22 |
Pages |
14985-14990 |
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ISE; HUPBA |
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no |
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Admin @ si @ DDB2016 |
Serial |
2934 |
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Svebor Karaman; Andrew Bagdanov; Lea Landucci; Gianpaolo D'Amico; Andrea Ferracani; Daniele Pezzatini; Alberto del Bimbo |
| Title |
Personalized multimedia content delivery on an interactive table by passive observation of museum visitors |
Type |
Journal Article |
| Year |
2016 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
75 |
Issue |
7 |
Pages |
3787-3811 |
| Keywords |
Computer vision; Video surveillance; Cultural heritage; Multimedia museum; Personalization; Natural interaction; Passive profiling |
| Abstract |
The amount of multimedia data collected in museum databases is growing fast, while the capacity of museums to display information to visitors is acutely limited by physical space. Museums must seek the perfect balance of information given on individual pieces in order to provide sufficient information to aid visitor understanding while maintaining sparse usage of the walls and guaranteeing high appreciation of the exhibit. Moreover, museums often target the interests of average visitors instead of the entire spectrum of different interests each individual visitor might have. Finally, visiting a museum should not be an experience contained in the physical space of the museum but a door opened onto a broader context of related artworks, authors, artistic trends, etc. In this paper we describe the MNEMOSYNE system that attempts to address these issues through a new multimedia museum experience. Based on passive observation, the system builds a profile of the artworks of interest for each visitor. These profiles of interest are then used to drive an interactive table that personalizes multimedia content delivery. The natural user interface on the interactive table uses the visitor’s profile, an ontology of museum content and a recommendation system to personalize exploration of multimedia content. At the end of their visit, the visitor can take home a personalized summary of their visit on a custom mobile application. In this article we describe in detail each component of our approach as well as the first field trials of our prototype system built and deployed at our permanent exhibition space at LeMurate (http://www.lemurate.comune.fi.it/lemurate/) in Florence together with the first results of the evaluation process during the official installation in the National Museum of Bargello (http://www.uffizi.firenze.it/musei/?m=bargello). |
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Springer US |
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1380-7501 |
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LAMP; 601.240; 600.079 |
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no |
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Admin @ si @ KBL2016 |
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2520 |
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Naveen Onkarappa; Angel Sappa |
| Title |
Synthetic sequences and ground-truth flow field generation for algorithm validation |
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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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no |
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Admin @ si @ OnS2014b |
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2472 |
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Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados |
| Title |
Classification of aesthetic natural scene images using statistical and semantic features |
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Journal Article |
| Year |
2023 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
82 |
Issue |
9 |
Pages |
13507-13532 |
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Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images. |
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DAG |
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Admin @ si @ BSP2023 |
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3873 |
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