| Records |
| Author |
Partha Pratim Roy; Josep Llados; Umapada Pal |
| Title |
Text/Graphics Separation in Color Maps |
Type |
Conference Article |
| Year |
2007 |
Publication |
International Conference on Computing: Theory and Applications |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
545–551 |
| Keywords |
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| Abstract |
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| Address |
Kolkata (India) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
|
Conference  |
ICCTA |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RLP2007a |
Serial |
806 |
| Permanent link to this record |
| |
|
| |
| Author |
Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
| Title |
Embedded real-time stereo estimation via Semi-Global Matching on the GPU |
Type |
Conference Article |
| Year |
2016 |
Publication |
16th International Conference on Computational Science |
Abbreviated Journal |
|
| Volume |
80 |
Issue |
|
Pages |
143-153 |
| Keywords |
Autonomous Driving; Stereo; CUDA; 3d reconstruction |
| Abstract |
Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. |
| Address |
San Diego; CA; USA; June 2016 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICCS |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ HCE2016a |
Serial |
2740 |
| Permanent link to this record |
| |
|
| |
| Author |
Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez |
| Title |
GPU-based pedestrian detection for autonomous driving |
Type |
Conference Article |
| Year |
2016 |
Publication |
16th International Conference on Computational Science |
Abbreviated Journal |
|
| Volume |
80 |
Issue |
|
Pages |
2377-2381 |
| Keywords |
Pedestrian detection; Autonomous Driving; CUDA |
| Abstract |
We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study. |
| Address |
San Diego; CA; USA; June 2016 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICCS |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ CSE2016 |
Serial |
2741 |
| Permanent link to this record |
| |
|
| |
| Author |
Partha Pratim Roy; Josep Llados |
| Title |
Multi-Oriented Character Recognition from Graphical Documents |
Type |
Conference Article |
| Year |
2008 |
Publication |
2nd International Conference on Cognition and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
30–35 |
| Keywords |
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| Abstract |
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| Address |
Mandya (India) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICCR |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RLP2008 |
Serial |
965 |
| Permanent link to this record |
| |
|
| |
| Author |
Maria Salamo; Sergio Escalera; Petia Radeva |
| Title |
Quality Enhancement based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender |
Type |
Conference Article |
| Year |
2009 |
Publication |
8th International Conference on Case-Based Reasoning |
Abbreviated Journal |
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| Volume |
5650 |
Issue |
|
Pages |
298–312 |
| Keywords |
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| Abstract |
Personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems. Conversational case-based recommender systems help users to navigate through product spaces, alternatively making product suggestions and eliciting users feedback. Critiquing is a common form of feedback and incremental critiquing-based recommender system has shown its efficiency to personalize products based primarily on a quality measure. This quality measure influences the recommendation process and it is obtained by the combination of compatibility and similarity scores. In this paper, we describe new compatibility strategies whose basis is on reinforcement learning and a new feature weighting technique which is based on the user’s history of critiques. Moreover, we show that our methodology can significantly improve recommendation efficiency in comparison with the state-of-the-art approaches. |
| Address |
Seattle, USA |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-02998-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICCBR |
| Notes |
HuPBA; MILAB |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ SER2009 |
Serial |
1187 |
| Permanent link to this record |
| |
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| |
| Author |
Bogdan Raducanu; Jordi Vitria; D. Gatica-Perez |
| Title |
You are Fired! Nonverbal Role Analysis in Competitive Meetings |
Type |
Conference Article |
| Year |
2009 |
Publication |
IEEE International Conference on Audio, Speech and Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1949–1952 |
| Keywords |
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| Abstract |
This paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. For our study, we made use of ldquoThe Apprenticerdquo reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. The current study was carried out using nonverbal audio cues. Results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. |
| Address |
Taipei, Taiwan |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1520-6149 |
ISBN |
978-1-4244-2353-8 |
Medium |
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| Area |
|
Expedition |
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Conference |
ICASSP |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ RVG2009 |
Serial |
1154 |
| Permanent link to this record |
| |
|
| |
| Author |
Saiping Zhang; Luis Herranz; Marta Mrak; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang |
| Title |
DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video |
Type |
Conference Article |
| Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
| Address |
Virtual; May 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICASSP |
| Notes |
MACO; 600.161; 601.379 |
Approved |
no |
| Call Number |
Admin @ si @ ZHM2022a |
Serial |
3765 |
| Permanent link to this record |
| |
|
| |
| Author |
Guillem Martinez; Maya Aghaei; Martin Dijkstra; Bhalaji Nagarajan; Femke Jaarsma; Jaap van de Loosdrecht; Petia Radeva; Klaas Dijkstra |
| Title |
Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation |
Type |
Conference Article |
| Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
Hyper-spectral imaging; plastic sorting; multi-label segmentation; bitfield encoding |
| Abstract |
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
| Address |
Singapore; May 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
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Medium |
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| Area |
|
Expedition |
|
Conference |
ICASSP |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MAD2022 |
Serial |
3767 |
| Permanent link to this record |
| |
|
| |
| Author |
Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang |
| Title |
Burst Perception-Distortion Tradeoff: Analysis and Evaluation |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
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| Volume |
|
Issue |
|
Pages |
|
| Keywords |
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| Abstract |
Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best option regarding reconstruction quality and efficiency, as the images acquired by handheld imaging devices suffer from degradation and misalignment caused by the camera noise and shake. In this paper, we extend the perception-distortion tradeoff theory by introducing multiple-frame information. We propose the area of the unattainable region as a new metric for perception-distortion tradeoff evaluation and comparison. Based on this metric, we analyse the performance of burst restoration from the perspective of the perception-distortion tradeoff under both aligned bursts and misaligned bursts situations. Our analysis reveals the importance of inter-frame alignment for burst restoration and shows that the optimal burst length for the restoration model depends both on the degree of degradation and misalignment. |
| Address |
Rodhes Islands; Greece; June 2023 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICASSP |
| Notes |
CIC; MACO |
Approved |
no |
| Call Number |
Admin @ si @ XHV2023 |
Serial |
3909 |
| Permanent link to this record |
| |
|
| |
| Author |
Yifan Wang; Luka Murn; Luis Herranz; Fei Yang; Marta Mrak; Wei Zhang; Shuai Wan; Marc Gorriz Blanch |
| Title |
Efficient Super-Resolution for Compression Of Gaming Videos |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Due to the increasing demand for game-streaming services, efficient compression of computer-generated video is more critical than ever, especially when the available bandwidth is low. This paper proposes a super-resolution framework that improves the coding efficiency of computer-generated gaming videos at low bitrates. Most state-of-the-art super-resolution networks generalize over a variety of RGB inputs and use a unified network architecture for frames of different levels of degradation, leading to high complexity and redundancy. Since games usually consist of a limited number of fixed scenarios, we specialize one model for each scenario and assign appropriate network capacities for different QPs to perform super-resolution under the guidance of reconstructed high-quality luma components. Experimental results show that our framework achieves a superior quality-complexity trade-off compared to the ESRnet baseline, saving at most 93.59% parameters while maintaining comparable performance. The compression efficiency compared to HEVC is also improved by more than 17% BD-rate gain. |
| Address |
|
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICASSP |
| Notes |
LAMP; MACO |
Approved |
no |
| Call Number |
Admin @ si @ WMH2023 |
Serial |
3911 |
| Permanent link to this record |
| |
|
| |
| Author |
Mingyi Yang; Luis Herranz; Fei Yang; Luka Murn; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang; Marta Mrak |
| Title |
Semantic Preprocessor for Image Compression for Machines |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Visual content is being increasingly transmitted and consumed by machines rather than humans to perform automated content analysis tasks. In this paper, we propose an image preprocessor that optimizes the input image for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. To achieve a better trade-off between the accuracy of the machine analysis task and bitrate, we propose leveraging pre-extracted semantic information to improve the preprocessor’s ability to accurately identify and filter out task-irrelevant information. Furthermore, we propose a two-part loss function to optimize the preprocessor, consisted of a rate-task performance loss and a semantic distillation loss, which helps the reconstructed image obtain more information that contributes to the accuracy of the task. Experiments show that the proposed preprocessor can save up to 48.83% bitrate compared with the method without the preprocessor, and save up to 36.24% bitrate compared to existing preprocessors for machine vision. |
| Address |
Rodhes Islands; Greece; June 2023 |
| Corporate Author |
|
Thesis |
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| Publisher |
|
Place of Publication |
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Editor |
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| Language |
|
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICASSP |
| Notes |
MACO; LAMP |
Approved |
no |
| Call Number |
Admin @ si @ YHY2023 |
Serial |
3912 |
| Permanent link to this record |
| |
|
| |
| Author |
Lei Kang; Lichao Zhang; Dazhi Jiang |
| Title |
Learning Robust Self-Attention Features for Speech Emotion Recognition with Label-Adaptive Mixup |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods. |
| Address |
Rodhes Islands; Greece; June 2023 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICASSP |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ KZJ2023 |
Serial |
3984 |
| Permanent link to this record |
| |
|
| |
| Author |
Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny |
| Title |
Optimal Linear Combination for Two-class Classifiers |
Type |
Conference Article |
| Year |
2007 |
Publication |
Proceedings of the International Conference on Advances in Pattern Recognition |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
|
| Address |
Kolkata (India) |
| 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 |
ICAPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RTV2007a |
Serial |
894 |
| Permanent link to this record |
| |
|
| |
| Author |
Alvaro Peris; Marc Bolaños; Petia Radeva; Francisco Casacuberta |
| Title |
Video Description Using Bidirectional Recurrent Neural Networks |
Type |
Conference Article |
| Year |
2016 |
Publication |
25th International Conference on Artificial Neural Networks |
Abbreviated Journal |
|
| Volume |
2 |
Issue |
|
Pages |
3-11 |
| Keywords |
Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks |
| Abstract |
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames. |
| Address |
Barcelona; September 2016 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
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Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICANN |
| Notes |
MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ PBR2016 |
Serial |
2833 |
| Permanent link to this record |
| |
|
| |
| Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
| Title |
Opponent Colors for Human Detection |
Type |
Conference Article |
| Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
6669 |
Issue |
|
Pages |
363-370 |
| Keywords |
Pedestrian Detection; Color; Part Based Models |
| Abstract |
Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. |
| Address |
Las Palmas de Gran Canaria. Spain |
| Corporate Author |
|
Thesis |
|
| Publisher |
Springer |
Place of Publication |
Berlin Heidelberg |
Editor |
J. Vitria; J.M. Sanches; M. Hernandez |
| Language |
English |
Summary Language |
English |
Original Title |
Opponent Colors for Human Detection |
| Series Editor |
|
Series Title |
Lecture Notes on Computer Science |
Abbreviated Series Title |
LNCS |
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
IbPRIA |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RVL2011a |
Serial |
1666 |
| Permanent link to this record |
