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Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva |
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SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation |
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2017 |
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Computer Vision and Image Understanding |
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CVIU |
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155 |
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55-69 |
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While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods. |
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MILAB; 601.235 |
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Admin @ si @ DBT2017 |
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2714 |
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Author |
Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera |
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Title |
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History |
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Journal Article |
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2016 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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28 |
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8 |
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1548-1568 |
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Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal |
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Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research. |
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HuPBA;MILAB; |
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Admin @ si @ COC2016 |
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2718 |
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Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan |
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Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach |
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2016 |
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Journal of Machine Vision and Applications |
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MVAP |
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27 |
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511-527 |
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particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging |
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In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor
quality data, particles and trajectories can be characterized by an a-contrario
model, that estimates the probability of observing the structures of interest
in random data. This approach, first introduced in the modeling of human visual
perception and then successfully applied in many image processing tasks, leads
to algorithms that do not require a previous learning stage, nor a tedious
parameter tuning and are very robust to noise. Comparative evaluations against
a well established baseline show that the proposed approach outperforms the
state of the art. |
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MILAB; |
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no |
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Admin @ si @ DJM2016 |
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2735 |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Title |
Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams |
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Journal Article |
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Year |
2016 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
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Volume |
149 |
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146-156 |
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Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness. |
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MILAB; |
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no |
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Admin @ si @ ADR2016b |
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2742 |
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Author |
Maria Oliver; G. Haro; Mariella Dimiccoli; B. Mazin; C. Ballester |
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Title |
A Computational Model for Amodal Completion |
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Journal Article |
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Year |
2016 |
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Journal of Mathematical Imaging and Vision |
Abbreviated Journal |
JMIV |
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Volume |
56 |
Issue |
3 |
Pages |
511–534 |
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Keywords |
Perception; visual completion; disocclusion; Bayesian model;relatability; Euler elastica |
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This paper presents a computational model to recover the most likely interpretation
of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth.
Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. |
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MILAB; 601.235 |
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Admin @ si @ OHD2016b |
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2745 |
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