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Fadi Dornaika, & Bogdan Raducanu. (2009). Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application. TSMCB - IEEE Transactions on Systems, Man and Cybernetics part B, 39(4), 935–944.
Abstract: Recently, we have proposed a real-time tracker that simultaneously tracks the 3-D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods.
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Bogdan Raducanu, & Fadi Dornaika. (2012). A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning. PR - Pattern Recognition, 45(6), 2432–2444.
Abstract: IF= 2.61
IF=2.61 (2010)
In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other.
Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance.
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F. Moreso, D. Seron, Jordi Vitria, J.M. Grinyo, F.M. Colome-Serra, N. Pares, et al. (1994). Quantification of Interstitial Chronic Renal Damage by means of Texture Analysis. Kidney International, 46(6), 1721–1727.
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Laura Igual, Xavier Perez Sala, Sergio Escalera, Cecilio Angulo, & Fernando De la Torre. (2014). Continuous Generalized Procrustes Analysis. PR - Pattern Recognition, 47(2), 659–671.
Abstract: PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the
standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects.
To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA).
CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA.
Keywords: Procrustes analysis; 2D shape model; Continuous approach
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Juan Ramon Terven Salinas, Joaquin Salas, & Bogdan Raducanu. (2014). New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired. COMP - Computer, 47(4), 52–58.
Abstract: Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices.
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