|
Ernest Valveny, & Miquel Ferrer. (2008). Application of Graph Embedding to Solve Graph Matchin Problems. In Colloque International Francophone sur l’Ecrit et le Document (13–18).
|
Ernest Valveny, & Enric Marti. (1999). Application of deformable template matching to symbol recognition in hand-written architectural draw. In Proceedings of the Fifth International Conference on. Bangalore (India).
Abstract: We propose to use deformable template matching as a new approach to recognize characters and lineal symbols in hand-written line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity to the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. Lineal nature of symbols can be exploited to define a suitable representation of models and the set of deformations to be applied to them. Matching, however, is done over the original binary image to avoid losing relevant features during vectorization. We have applied this method to hand-written architectural drawings and experimental results demonstrate that symbols with high distortions from ideal shape can be accurately identified.
|
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.
|
|
Fadi Dornaika, & Angel Sappa. (2005). Appearance-based 3D Face Tracker: An Evaluation Study.
|
|
Javier Varona, Jordi Gonzalez, Xavier Roca, & Juan J. Villanueva. (2003). Appearance Tracking for Video Surveillance.
|
|
Javier Varona, Jordi Gonzalez, Xavier Roca, & Juan J. Villanueva. (2003). Appearance Tracking for Video Surveillance.
|
Eirikur Agustsson, Radu Timofte, Sergio Escalera, Xavier Baro, Isabelle Guyon, & Rasmus Rothe. (2017). Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods.
The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks.
|
|
Antonio Lopez, Felipe Lumbreras, A. Martinez, Joan Serrat, Xavier Roca, Javier Varona, et al. (1997). Aplicaciones de la vision por computador a la industria..
|
|
C. Padres. (2000). Aplicaciones al modelo de formas activas para un entorno aumentado.
|
|
Joan Serrat. (1995). Aplicacion del analisis de imagenes en radiologia..
|
|
Enric Marti. (2008). Aplicació de la metodología d’Aprenentatge basat en Proyectes en l’assignatura de Gràfics per Computador d’enginyeria Informàtica. Balanç de Quatre anys d’experiència (Vol. 6).
|
Aura Hernandez-Sabate, Debora Gil, Petia Radeva, & E.N.Nofrerias. (2004). Anisotropic processing of image structures for adventitia detection in intravascular ultrasound images. In Proc. Computers in Cardiology (Vol. 31, pp. 229–232). Chicago (USA).
Abstract: The adventitia layer appears as a weak edge in IVUS images with a non-uniform grey level, which difficulties its detection. In order to enhance edges, we apply an anisotropic filter that homogenizes the grey level along the image significant structures (ridges, valleys and edges). A standard edge detector applied to the filtered image yields a set of candidate points prone to be unconnected. The final model is obtained by interpolating the former line segments along the tangent direction to the level curves of the filtered image with an anisotropic contour closing technique based on functional extension principles
|
Panagiota Spyridonos, Fernando Vilariño, Jordi Vitria, Fernando Azpiroz, & Petia Radeva. (2006). Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions. In and J. Sporring M. N. R. Larsen (Ed.), 9th International Conference on Medical Image Computing and Computer–Assisted Intervention (Vol. 4191, 161–168). LNCS. Berlin Heidelberg: Springer Verlag.
Abstract: Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.
|
Debora Gil, Petia Radeva, & Fernando Vilariño. (2003). Anisotropic Contour Completion. In Proceedings of the IEEE International Conference on Image Processing. Barcelona, Spain.
Abstract: In this paper we introduce a novel application of the diffusion tensor for anisotropic image processing. The Anisotropic Contour Completion (ACC) we suggest consists in extending the characteristic function of the open curve by means of a degenerated diffusion tensor that prevents any diffusion in the normal direction. We show that ACC is equivalent to a dilation with a continuous elliptic structural element that takes into account the local orientation of the contours to be closed. Experiments on contours extracted from real images show that ACC produces shapes able to adapt to any curve in an active contour framework. 1.
|
|
A.F. Sole, S. Ngan, G. Sapiro, X. Hu, & Antonio Lopez. (2001). Anisotropic 2-D and 3-D Averaging of fMRI Signals. IEEE Transactions on Medical Imaging, 20(2): 86–93 (IF: 3.142), .
|
