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Laura Igual, Agata Lapedriza, & Ricard Borras. (2013). Robust Gait-Based Gender Classification using Depth Cameras. EURASIPJ - EURASIP Journal on Advances in Signal Processing, 37(1), 72–80.
Abstract: This article presents a new approach for gait-based gender recognition using depth cameras, that can run in real time. The main contribution of this study is a new fast feature extraction strategy that uses the 3D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and grouped. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. Then, final discriminative features are computed by first making a histogram of the projected points and then using linear discriminant analysis. To test the method we have used the DGait database, which is currently the only publicly available database for gait analysis that includes depth information. We have performed experiments on manually labeled cycles and over whole video sequences, and the results show that our method improves the accuracy significantly, compared with state-of-the-art systems which do not use depth information. Furthermore, our approach is insensitive to illumination changes, given that it discards the RGB information. That makes the method especially suitable for real applications, as illustrated in the last part of the experiments section.
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Antonio Hernandez, Miguel Reyes, Victor Ponce, & Sergio Escalera. (2012). GrabCut-Based Human Segmentation in Video Sequences. SENS - Sensors, 12(11), 15376–15393.
Abstract: In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology.
Keywords: segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field
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Michal Drozdzal, Santiago Segui, Carolina Malagelada, Fernando Azpiroz, & Petia Radeva. (2013). Adaptable image cuts for motility inspection using WCE. CMIG - Computerized Medical Imaging and Graphics, 37(1), 72–80.
Abstract: The Wireless Capsule Endoscopy (WCE) technology allows the visualization of the whole small intestine tract. Since the capsule is freely moving, mainly by the means of peristalsis, the data acquired during the study gives a lot of information about the intestinal motility. However, due to: (1) huge amount of frames, (2) complex intestinal scene appearance and (3) intestinal dynamics that make difficult the visualization of the small intestine physiological phenomena, the analysis of the WCE data requires computer-aided systems to speed up the analysis. In this paper, we propose an efficient algorithm for building a novel representation of the WCE video data, optimal for motility analysis and inspection. The algorithm transforms the 3D video data into 2D longitudinal view by choosing the most informative, from the intestinal motility point of view, part of each frame. This step maximizes the lumen visibility in its longitudinal extension. The task of finding “the best longitudinal view” has been defined as a cost function optimization problem which global minimum is obtained by using Dynamic Programming. Validation on both synthetic data and WCE data shows that the adaptive longitudinal view is a good alternative to the traditional motility analysis done by video analysis. The proposed novel data representation a new, holistic insight into the small intestine motility, allowing to easily define and analyze motility events that are difficult to spot by analyzing WCE video. Moreover, the visual inspection of small intestine motility is 4 times faster then by means of video skimming of the WCE.
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Simone Balocco, Carlo Gatta, Marina Alberti, Xavier Carrillo, Juan Rigla, & Petia Radeva. (2012). Relation between plaque type, plaque thickness, blood shear stress and plaque stress in coronary arteries assessed by X-ray Angiography and Intravascular Ultrasound. MEDPHYS - Medical Physics, 39(12), 7430–7445.
Abstract: PMID 23231293
PURPOSE:
Atheromatic plaque progression is affected, among others phenomena, by biomechanical, biochemical, and physiological factors. In this paper, the authors introduce a novel framework able to provide both morphological (vessel radius, plaque thickness, and type) and biomechanical (wall shear stress and Von Mises stress) indices of coronary arteries.
METHODS:
First, the approach reconstructs the three-dimensional morphology of the vessel from intravascular ultrasound (IVUS) and Angiographic sequences, requiring minimal user interaction. Then, a computational pipeline allows to automatically assess fluid-dynamic and mechanical indices. Ten coronary arteries are analyzed illustrating the capabilities of the tool and confirming previous technical and clinical observations.
RESULTS:
The relations between the arterial indices obtained by IVUS measurement and simulations have been quantitatively analyzed along the whole surface of the artery, extending the analysis of the coronary arteries shown in previous state of the art studies. Additionally, for the first time in the literature, the framework allows the computation of the membrane stresses using a simplified mechanical model of the arterial wall.
CONCLUSIONS:
Circumferentially (within a given frame), statistical analysis shows an inverse relation between the wall shear stress and the plaque thickness. At the global level (comparing a frame within the entire vessel), it is observed that heavy plaque accumulations are in general calcified and are located in the areas of the vessel having high wall shear stress. Finally, in their experiments the inverse proportionality between fluid and structural stresses is observed.
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Oscar Lopes, Miguel Reyes, Sergio Escalera, & Jordi Gonzalez. (2014). Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments. TSMCB - IEEE Transactions on Systems, Man and Cybernetics (Part B), 44(12), 2379–2390.
Abstract: The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios.
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