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Author |
Maedeh Aghaei; Petia Radeva |
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Title |
Bag-of-Tracklets for Person Tracking in Life-Logging Data |
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Conference Article |
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Year |
2014 |
Publication |
17th International Conference of the Catalan Association for Artificial Intelligence |
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Volume |
269 |
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Pages |
35-44 |
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By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data. |
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978-1-61499-451-0 |
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CCIA |
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MILAB |
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no |
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Admin @ si @ AgR2015 |
Serial |
2607 |
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Author |
Agata Lapedriza; David Masip; David Sanchez |
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Title |
Emotions Classification using Facial Action Units Recognition |
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Conference Article |
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Year |
2014 |
Publication |
17th International Conference of the Catalan Association for Artificial Intelligence |
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269 |
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55-64 |
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In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection. |
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978-1-61499-451-0 |
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CCIA |
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OR;MV |
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no |
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Call Number |
Admin @ si @ LMS2014 |
Serial |
2622 |
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Author |
G. de Oliveira; Mariella Dimiccoli; Petia Radeva |
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Title |
Egocentric Image Retrieval With Deep Convolutional Neural Networks |
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Conference Article |
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Year |
2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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71-76 |
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Barcelona; Spain; October 2016 |
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MILAB |
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no |
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Call Number |
Admin @ si @ODR2016 |
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2790 |
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Author |
Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
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Title |
Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light |
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Conference Article |
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2016 |
Publication |
19th International Conference of the Catalan Association for Artificial Intelligence |
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computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus |
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Abstract |
Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments
for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior.
This emergent behavior is modulated by the day-night cycle, but their social
interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community. |
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Barcelona; Spain; October 2016 |
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CCIA |
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OR;MV; |
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no |
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Call Number |
Admin @ si @ GMS2016 |
Serial |
2816 |
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Author |
Petia Radeva |
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Title |
Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better? |
Type |
Conference Article |
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Year |
2016 |
Publication |
19th International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
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Volume |
4 |
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Address |
Barcelona; October 2016 |
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MILAB |
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no |
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Call Number |
Admin @ si @ Rad2016 |
Serial |
2832 |
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Author |
Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
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Title |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network |
Type |
Conference Article |
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Year |
2018 |
Publication |
21st International Conference of the Catalan Association for Artificial Intelligence |
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365-372 |
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Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
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Roses; catalonia; October 2018 |
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CCIA |
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Notes |
MILAB; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ SJR2018 |
Serial |
3113 |
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Author |
Md. Mostafa Kamal Sarker; Syeda Furruka Banu; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Sylvie Chambon; Petia Radeva; Domenec Puig |
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Title |
Food Places Classification in Egocentric Images Using Siamese Neural Networks |
Type |
Conference Article |
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Year |
2019 |
Publication |
22nd International Conference of the Catalan Association of Artificial Intelligence |
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145-151 |
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Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2. |
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Illes Balears; October 2019 |
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CCIA |
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Notes |
MILAB; no proj |
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no |
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Call Number |
Admin @ si @ SBR2019 |
Serial |
3368 |
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Author |
Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil |
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Title |
Mental Workload Detection Based on EEG Analysis |
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Conference Article |
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Year |
2021 |
Publication |
Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. |
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Volume |
339 |
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268-277 |
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Keywords |
Cognitive states; Mental workload; EEG analysis; Neural Networks. |
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The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training.
In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation. |
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Virtual; October 20-22 2021 |
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CCIA |
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Notes |
IAM; 600.139; 600.118; 600.145 |
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no |
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Call Number |
Admin @ si @ |
Serial |
3723 |
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Author |
David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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Title |
Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion |
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Conference Article |
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2007 |
Publication |
Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA |
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163 |
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197–204 |
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978-1-58603-798-7 |
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CCIA’07 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ RRF2007a |
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831 |
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Author |
Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Using the Average Landmark Vector Method for Robot Homing |
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Conference Article |
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2007 |
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Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA |
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163 |
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331–338 |
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978–1–58603–798–7 |
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CCIA’07 |
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RV;ADAS |
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no |
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Admin @ si @ GRL2007 |
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899 |
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Author |
Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich |
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Title |
Perception Based Representations for Computational Colour |
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Conference Article |
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2011 |
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3rd International Workshop on Computational Color Imaging |
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6626 |
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16-30 |
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colour perception, induction, naming, psychophysical data, saliency, segmentation |
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The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space. |
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Milan, Italy |
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Springer-Verlag |
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Raimondo Schettini, Shoji Tominaga, Alain Trémeau |
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LNCS |
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978-3-642-20403-6 |
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CCIW |
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CIC |
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Admin @ si @ VMB2011 |
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1733 |
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Author |
Joost Van de Weijer; Fahad Shahbaz Khan |
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Title |
Fusing Color and Shape for Bag-of-Words Based Object Recognition |
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Conference Article |
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2013 |
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4th Computational Color Imaging Workshop |
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7786 |
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25-34 |
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Object Recognition; color features; bag-of-words; image classification |
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In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research. |
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Chiba; Japan; March 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-36699-4 |
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CCIW |
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CIC; 600.048 |
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Admin @ si @ WeK2013 |
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2283 |
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Author |
Joost Van de Weijer; Fahad Shahbaz Khan |
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Title |
An Overview of Color Name Applications in Computer Vision |
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Conference Article |
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2015 |
Publication |
Computational Color Imaging Workshop |
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color features; color names; object recognition |
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In this article we provide an overview of color name applications in computer vision. Color names are linguistic labels which humans use to communicate color. Computational color naming learns a mapping from pixels values to color names. In recent years color names have been applied to a wide variety of computer vision applications, including image classification, object recognition, texture classification, visual tracking and action recognition. Here we provide an overview of these results which show that in general color names outperform photometric invariants as a color representation. |
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Saint Etienne; France; March 2015 |
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Notes |
LAMP; 600.079; 600.068 |
Approved |
no |
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Call Number |
Admin @ si @ WeK2015 |
Serial |
2586 |
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Author |
Miguel Reyes; Gabriel Dominguez; Sergio Escalera |
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Title |
Feature Weighting in Dynamic Time Warping for Gesture Recognition in Depth Data |
Type |
Conference Article |
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Year |
2011 |
Publication |
1st IEEE Workshop on Consumer Depth Cameras for Computer Vision |
Abbreviated Journal |
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Pages |
1182-1188 |
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Abstract |
We present a gesture recognition approach for depth video data based on a novel Feature Weighting approach within the Dynamic Time Warping framework. Depth features from human joints are compared through video sequences using Dynamic Time Warping, and weights are assigned to features based on inter-intra class gesture variability. Feature Weighting in Dynamic Time Warping is then applied for recognizing begin-end of gestures in data sequences. The obtained results recognizing several gestures in depth data show high performance compared with classical Dynamic Time Warping approach. |
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978-1-4673-0062-9 |
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Conference |
CDC4CV |
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Notes |
HuPBA;MILAB |
Approved |
no |
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Call Number |
Admin @ si @ RDE2011 |
Serial |
1893 |
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Author |
Ferran Poveda; Debora Gil ;Albert Andaluz ;Enric Marti |
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Title |
Multiscale Tractography for Representing Heart Muscular Architecture |
Type |
Conference Article |
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Year |
2011 |
Publication |
In MICCAI 2011 Workshop on Computational Diffusion MRI |
Abbreviated Journal |
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Abstract |
Deep understanding of myocardial structure of the heart would unravel crucial knowledge for clinical and medical procedures. Although the muscular architecture of the heart has been debated by countless researchers, the controversy is still alive. Diffusion Tensor MRI, DT-MRI, is a unique imaging technique for computational validation of the muscular structure of the heart. By the complex arrangement of myocites, existing techniques can not provide comprehensive descriptions of the global muscular architecture. In this paper we introduce a multiresolution reconstruction technique based on DT-MRI streamlining for simplified global myocardial model generation. Our reconstructions can restore the most complex myocardial structures and indicate a global helical organization |
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English |
Summary Language |
english |
Original Title |
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CDRMI |
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Notes |
IAM |
Approved |
no |
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Call Number |
IAM @ iam @ PGA2011 |
Serial |
1681 |
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| Permanent link to this record |
