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Cesar Isaza, Joaquin Salas, & Bogdan Raducanu. (2012). Synthetic ground truth dataset to detect shadow cast by static objects in outdoor. In 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications (art. 11). ACM.
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.
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Diego Cheda, Daniel Ponsa, & Antonio Lopez. (2012). Monocular Depth-based Background Estimation. In 7th International Conference on Computer Vision Theory and Applications (pp. 323–328).
Abstract: In this paper, we address the problem of reconstructing the background of a scene from a video sequence with occluding objects. The images are taken by hand-held cameras. Our method composes the background by selecting the appropriate pixels from previously aligned input images. To do that, we minimize a cost function that penalizes the deviations from the following assumptions: background represents objects whose distance to the camera is maximal, and background objects are stationary. Distance information is roughly obtained by a supervised learning approach that allows us to distinguish between close and distant image regions. Moving foreground objects are filtered out by using stationariness and motion boundary constancy measurements. The cost function is minimized by a graph cuts method. We demonstrate the applicability of our approach to recover an occlusion-free background in a set of sequences.
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Pedro Martins, Carlo Gatta, & Paulo Carvalho. (2012). Feature-driven Maximally Stable Extremal Regions. In 7th International Conference on Computer Vision Theory and Applications (pp. 490–497).
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Fadi Dornaika, Alireza Bosaghzadeh, & Bogdan Raducanu. (2012). LSDA Solution Schemes for Modelless 3D Head Pose Estimation. In IEEE Workshop on the Applications of Computer Vision (pp. 393–398).
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Bogdan Raducanu, & Fadi Dornaika. (2012). Appearance-based Face Recognition Using A Supervised Manifold Learning Framework. In IEEE Workshop on the Applications of Computer Vision (pp. 465–470). IEEE Xplore.
Abstract: Many natural image sets, depicting objects whose appearance is changing due to motion, pose or light variations, can be considered samples of a low-dimension nonlinear manifold embedded in the high-dimensional observation space (the space of all possible images). The main contribution of our work is represented by a Supervised Laplacian Eigemaps (S-LE) algorithm, which exploits the class label information for mapping the original data in the embedded space. Our proposed approach benefits from two important properties: i) it is discriminative, and ii) it adaptively selects the neighbors of a sample without using any predefined neighborhood size. Experiments were conducted on four face databases and the results demonstrate that the proposed algorithm significantly outperforms many linear and non-linear embedding techniques. Although we've focused on the face recognition problem, the proposed approach could also be extended to other category of objects characterized by large variance in their appearance.
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Miguel Angel Bautista, Antonio Hernandez, Victor Ponce, Xavier Perez Sala, Xavier Baro, Oriol Pujol, et al. (2012). Probability-based Dynamic TimeWarping for Gesture Recognition on RGB-D data. In 21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis (Vol. 7854, pp. 126–135). Springer Berlin Heidelberg.
Abstract: Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data.
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Miguel Reyes, Albert Clapes, Luis Felipe Mejia, Jose Ramirez, Juan R Revilla, & Sergio Escalera. (2012). Posture Analysis and Range of Movement Estimation using Depth Maps. In 21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis (Vol. 7854, pp. 97–105). Springer Berlin Heidelberg.
Abstract: World Health Organization estimates that 80% of the world population is affected of back pain during his life. Current practices to analyze back problems are expensive, subjective, and invasive. In this work, we propose a novel tool for posture and range of movement estimation based on the analysis of 3D information from depth maps. Given a set of keypoints defined by the user, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matching using a novel point-to-point fitting procedure, and accurate measurements about posture, spinal curvature, and range of movement are computed. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent musculoskeletal disorders, such as back pain, as well as tracking the posture evolution of patients in rehabilitation treatments.
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