| Records |
Author  |
Chengyi Zou; Shuai Wan; Tiannan Ji; Marc Gorriz Blanch; Marta Mrak; Luis Herranz |
| Title |
Chroma Intra Prediction with Lightweight Attention-Based Neural Networks |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Transactions on Circuits and Systems for Video Technology |
Abbreviated Journal |
TCSVT |
| Volume |
34 |
Issue |
1 |
Pages |
549 - 560 |
| Keywords |
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| Abstract |
Neural networks can be successfully used for cross-component prediction in video coding. In particular, attention-based architectures are suitable for chroma intra prediction using luma information because of their capability to model relations between difierent channels. However, the complexity of such methods is still very high and should be further reduced, especially for decoding. In this paper, a cost-effective attention-based neural network is designed for chroma intra prediction. Moreover, with the goal of further improving coding performance, a novel approach is introduced to utilize more boundary information effectively. In addition to improving prediction, a simplification methodology is also proposed to reduce inference complexity by simplifying convolutions. The proposed schemes are integrated into H.266/Versatile Video Coding (VVC) pipeline, and only one additional binary block-level syntax flag is introduced to indicate whether a given block makes use of the proposed method. Experimental results demonstrate that the proposed scheme achieves up to −0.46%/−2.29%/−2.17% BD-rate reduction on Y/Cb/Cr components, respectively, compared with H.266/VVC anchor. Reductions in the encoding and decoding complexity of up to 22% and 61%, respectively, are achieved by the proposed scheme with respect to the previous attention-based chroma intra prediction method while maintaining coding performance. |
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Conference |
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| Notes |
MACO; LAMP |
Approved |
no |
| Call Number |
Admin @ si @ ZWJ2023 |
Serial |
3875 |
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| Author |
Chengyi Zou; Shuai Wan; Marta Mrak; Marc Gorriz Blanch; Luis Herranz; Tiannan Ji |
| Title |
Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding |
Type |
Conference Article |
| Year |
2022 |
Publication |
29th IEEE International Conference on Image Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
Video coding; Quantization (signal); Computational modeling; Neural networks; Predictive models; Video compression; Syntactics |
| Abstract |
In video compression the luma channel can be useful for predicting chroma channels (Cb, Cr), as has been demonstrated with the Cross-Component Linear Model (CCLM) used in Versatile Video Coding (VVC) standard. More recently, it has been shown that neural networks can even better capture the relationship among different channels. In this paper, a new attention-based neural network is proposed for cross-component intra prediction. With the goal to simplify neural network design, the new framework consists of four branches: boundary branch and luma branch for extracting features from reference samples, attention branch for fusing the first two branches, and prediction branch for computing the predicted chroma samples. The proposed scheme is integrated into VVC test model together with one additional binary block-level syntax flag which indicates whether a given block makes use of the proposed method. Experimental results demonstrate 0.31%/2.36%/2.00% BD-rate reductions on Y/Cb/Cr components, respectively, on top of the VVC Test Model (VTM) 7.0 which uses CCLM. |
| Address |
Bordeaux; France; October 2022 |
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Conference |
ICIP |
| Notes |
MACO |
Approved |
no |
| Call Number |
Admin @ si @ ZWM2022 |
Serial |
3790 |
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| Author |
Chen Zhang; Maria del Mar Vila Muñoz; Petia Radeva; Roberto Elosua; Maria Grau; Angels Betriu; Elvira Fernandez-Giraldez; Laura Igual |
| Title |
Carotid Artery Segmentation in Ultrasound Images |
Type |
Conference Article |
| Year |
2015 |
Publication |
Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT2015), Joint MICCAI Workshops |
Abbreviated Journal |
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Issue |
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Pages |
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| Address |
Munich; Germany; October 2015 |
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Conference |
CVII-STENT |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ZVR2015 |
Serial |
2675 |
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| Author |
Chee-Kheng Chng; Yuliang Liu; Yipeng Sun; Chun Chet Ng; Canjie Luo; Zihan Ni; ChuanMing Fang; Shuaitao Zhang; Junyu Han; Errui Ding; Jingtuo Liu; Dimosthenis Karatzas; Chee Seng Chan; Lianwen Jin |
| Title |
ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT |
Type |
Conference Article |
| Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1571-1576 |
| Keywords |
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| Abstract |
This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 – 82.65%, ii) T2.1 – 74.3%, iii) T2.2 – 85.32%, iv) T3.1 – 53.86%, and v) T3.2 – 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website. |
| Address |
Sydney; Australia; September 2019 |
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Medium |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG; 600.121; 600.129 |
Approved |
no |
| Call Number |
Admin @ si @ CLS2019 |
Serial |
3340 |
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| Author |
Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
| Title |
Toward the Detection of Urban Infrastructures Edge Shadows |
Type |
Conference Article |
| Year |
2010 |
Publication |
12th International Conference on Advanced Concepts for Intelligent Vision Systems |
Abbreviated Journal |
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| Volume |
6474 |
Issue |
I |
Pages |
30–37 |
| Keywords |
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| Abstract |
In this paper, we propose a novel technique to detect the shadows cast by urban infrastructure, such as buildings, billboards, and traffic signs, using a sequence of images taken from a fixed camera. In our approach, we compute two different background models in parallel: one for the edges and one for the reflected light intensity. An algorithm is proposed to train the system to distinguish between moving edges in general and edges that belong to static objects, creating an edge background model. Then, during operation, a background intensity model allow us to separate between moving and static objects. Those edges included in the moving objects and those that belong to the edge background model are subtracted from the current image edges. The remaining edges are the ones cast by urban infrastructure. Our method is tested on a typical crossroad scene and the results show that the approach is sound and promising. |
| Address |
Sydney, Australia |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
eds. Blanc–Talon et al |
| 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 |
978-3-642-17687-6 |
Medium |
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| Area |
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Expedition |
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Conference |
ACIVS |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ ISR2010 |
Serial |
1458 |
| Permanent link to this record |
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| Author |
Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
| Title |
Synthetic ground truth dataset to detect shadow cast by static objects in outdoor |
Type |
Conference Article |
| Year |
2012 |
Publication |
1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
art. 11 |
| Keywords |
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| Abstract |
In this paper, we propose a precise synthetic ground truth dataset to study the problem of detection of the shadows cast by static objects in outdoor environments during extended periods of time (days). For our dataset, we have created a virtual scenario using a rendering software. To increase the realism of the simulated environment, we have defined the scenario in a precise geographical location. In our dataset the sun is by far the main illumination source. The sun position during the simulation time takes into consideration factors related to the geographical location, such as the latitude, longitude, elevation above sea level, and precise image capturing day and time. In our simulation the camera remains fixed. The dataset consists of seven days of simulation, from 10:00am to 5:00pm. Images are captured every 10 seconds. The shadows' ground truth is automatically computed by the rendering software. |
| Address |
Capri, Italy |
| Corporate Author |
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Thesis |
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| Publisher |
ACM |
Place of Publication |
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Edition |
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ISBN |
978-1-4503-1405-3 |
Medium |
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Expedition |
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Conference |
VIGTA |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ ISR2012a |
Serial |
2037 |
| Permanent link to this record |
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| Author |
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 |
| Abstract |
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. |
| Address |
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Thesis |
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| Publisher |
Springer US |
Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1380-7501 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
LAMP; |
Approved |
no |
| Call Number |
Admin @ si @ ISR2014 |
Serial |
2229 |
| Permanent link to this record |
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| Author |
Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
| Title |
Evaluation of Intrinsic Image Algorithms to Detect the Shadows Cast by Static Objects Outdoors |
Type |
Journal Article |
| Year |
2012 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
12 |
Issue |
10 |
Pages |
13333-13348 |
| Keywords |
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| Abstract |
In some automatic scene analysis applications, the presence of shadows becomes a nuisance that is necessary to deal with. As a consequence, a preliminary stage in many computer vision algorithms is to attenuate their effect. In this paper, we focus our attention on the detection of shadows cast by static objects outdoors, as the scene is viewed for extended periods of time (days, weeks) from a fixed camera and considering daylight intervals where the main source of light is the sun. In this context, we report two contributions. First, we introduce the use of synthetic images for which ground truth can be generated automatically, avoiding the tedious effort of manual annotation. Secondly, we report a novel application of the intrinsic image concept to the automatic detection of shadows cast by static objects in outdoors. We make both a quantitative and a qualitative evaluation of several algorithms based on this image representation. For the quantitative evaluation, we used the synthetic data set, while for the qualitative evaluation we used both data sets. Our experimental results show that the evaluated methods can partially solve the problem of shadow detection. |
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Expedition |
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Conference |
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| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ ISR2012b |
Serial |
2173 |
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| Author |
Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez |
| Title |
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models |
Type |
Journal Article |
| Year |
2020 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
128 |
Issue |
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Pages |
1505–1536 |
| Keywords |
Procedural generation; Human action recognition; Synthetic data; Physics |
| Abstract |
Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos. |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.124; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ SGC2019 |
Serial |
3303 |
| Permanent link to this record |
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| Author |
Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez |
| Title |
Procedural Generation of Videos to Train Deep Action Recognition Networks |
Type |
Conference Article |
| Year |
2017 |
Publication |
30th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2594-2604 |
| Keywords |
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| Abstract |
Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
| Address |
Honolulu; Hawaii; July 2017 |
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Medium |
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Expedition |
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Conference |
CVPR |
| Notes |
ADAS; 600.076; 600.085; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ SGC2017 |
Serial |
3051 |
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| Author |
Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez |
| Title |
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition |
Type |
Conference Article |
| Year |
2016 |
Publication |
14th European Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
697-716 |
| Keywords |
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| Abstract |
Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos. |
| Address |
Amsterdam; The Netherlands; October 2016 |
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LNCS |
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Edition |
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Medium |
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Expedition |
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Conference |
ECCV |
| Notes |
ADAS; 600.076; 600.085 |
Approved |
no |
| Call Number |
Admin @ si @ SGV2016 |
Serial |
2824 |
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| Author |
Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez |
| Title |
System and method for video classification using a hybrid unsupervised and supervised multi-layer architecture |
Type |
Patent |
| Year |
2018 |
Publication |
US9946933B2 |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
US9946933B2 |
| Abstract |
A computer-implemented video classification method and system are disclosed. The method includes receiving an input video including a sequence of frames. At least one transformation of the input video is generated, each transformation including a sequence of frames. For the input video and each transformation, local descriptors are extracted from the respective sequence of frames. The local descriptors of the input video and each transformation are aggregated to form an aggregated feature vector with a first set of processing layers learned using unsupervised learning. An output classification value is generated for the input video, based on the aggregated feature vector with a second set of processing layers learned using supervised learning. |
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Place of Publication |
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Summary Language |
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Series Issue |
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Edition |
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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 |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ SGV2018 |
Serial |
3255 |
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| Author |
Cesar de Souza |
| Title |
Action Recognition in Videos: Data-efficient approaches for supervised learning of human action classification models for video |
Type |
Book Whole |
| Year |
2018 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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 dissertation, we explore different ways to perform human action recognition in video clips. We focus on data efficiency, proposing new approaches that alleviate the need for laborious and time-consuming manual data annotation. In the first part of this dissertation, we start by analyzing previous state-of-the-art models, comparing their differences and similarities in order to pinpoint where their real strengths come from. Leveraging this information, we then proceed to boost the classification accuracy of shallow models to levels that rival deep neural networks. We introduce hybrid video classification architectures based on carefully designed unsupervised representations of handcrafted spatiotemporal features classified by supervised deep networks. We show in our experiments that our hybrid model combine the best of both worlds: it is data efficient (trained on 150 to 10,000 short clips) and yet improved significantly on the state of the art, including deep models trained on millions of manually labeled images and videos. In the second part of this research, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We then introduce deep multi-task representation learning architectures to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
| Address |
April 2018 |
| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Antonio Lopez;Naila Murray |
| Language |
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Summary Language |
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Original Title |
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Series Title |
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Series Issue |
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Medium |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ Sou2018 |
Serial |
3127 |
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| Author |
Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz |
| Title |
Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis |
Type |
Journal Article |
| Year |
2015 |
Publication |
American Journal of Physiology-Gastrointestinal and Liver Physiology |
Abbreviated Journal |
AJPGI |
| Volume |
309 |
Issue |
6 |
Pages |
G413--G419 |
| Keywords |
capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning |
| Abstract |
We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function. |
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American Physiological Society |
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MILAB; OR;MV |
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Admin @ si @ MDS2015 |
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2666 |
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| Author |
Carolina Malagelada; Fosca De Iorio; Fernando Azpiroz; Anna Accarino; Santiago Segui; Petia Radeva; Juan R. Malagelada |
| Title |
New Insight Into Intestinal Motor Function via Noninvasive Endoluminal Image Analysis |
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2008 |
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Gastroenterology |
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135 |
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4 |
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1155–1162 |
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BCNPCL @ bcnpcl @ MDA2008 |
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1040 |
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