|
O. Rodriguez, J. Mauri, E Fernandez-Nofrerias, J. Lopez, A. Tovar, R. Villuendas, et al. (2003). Modelo fisico para la simulacion de imagenes de ecografia intracoronaria. Revista Española de Cardiologia (IF: 0.959), 56(2), Congreso de las Enfermedades Cardiovasculares.
|
|
Gemma Sanchez, & Josep Llados. (2003). Syntactic models to represent perceptually regular repetitive patterns in graphic documents.
|
|
Angel Sappa, Niki Aifanti, Sotiris Malassiotis, & Michael G. Strintzis. (2003). Monocular 3D Human Body Reconstruction Towards Depth Augmentation of Television Sequences. In IEEE International Conference on Image Processing, Barcelona, Spain, September 2003 (pp. 325–328).
|
|
J. Suri, S. Singh, S. Laxminarayan, R. Cesar, H. Jelinek, Petia Radeva, et al. (2003). A Note on Future Research in Vascular and Plaque Segmentation.
|
|
Anna Salvatella, Maria Vanrell, & Ramon Baldrich. (2003). Subtexture Components for Texture Description.
|
|
Anna Salvatella, Maria Vanrell, & Juan J. Villanueva. (2003). Texture Description based on Subtexture Components, 3rd International Workshop on Texture Syntesis and Analysis. In 3rd International Workshop on Texture Synthesis and Analysis, (77–82).
|
|
Ernest Valveny, & Antonio Lopez. (2003). Numeral Recognition for Quality Control of Surgical Sachets.
|
|
J. Varona, Jordi Gonzalez, Xavier Roca, & Juan J. Villanueva. (2003). Appearance Tracking for Video Surveillance.
|
Fernando Vilariño, & Petia Radeva. (2003). Cardiac Segmentation with Discriminant Active Contours. (211–217). IOS Press.
Abstract: Dynamic tracking of heart moving is one relevant target in medical imag- ing and can be helpful for analyzing heart dynamics in the study of several cardiac diseases. For this aim, a previous segmentation problem of such structures is stated, based on certain relevant features (like edges or intensity levels, textures, etc.) Clas- sical active models have been used, but they fail when overlapping structures or not well-defined contours are present. Automatic feature learning systems may be a pow- erful tool. Discriminant active contours present optimal results in this kind of problem. They are a kind of deformable models that converge to an optimal object segmenta- tion that dynamically adapts to the object contour. The feature space is designed from a filter bank in order to guarantee the search and learning of the set of relevant fea- tures for optimal classification on each part of the object. Tracking of target evolution is obtained through the whole set of images, using information from the actual and previous stages. Feedback systems are implemented to guarantee the minimum well- separable classification set in each segmentation step. Our implementation has been proved with several series of Magnetic Resonance with improved results in segmenta- tion in comparison to previous methods.
|
|
I. King, & Zhong Jin. (2003). Integrated Probability Function and Its Application to Content-Based Image Retrieval By Relevance Feedback. Pattern Recognition, 36(9): 2177–2186 (IF: 1.611).
|
|
X. Jing, David Zhang, & Zhong Jin. (2003). Improvements on the uncorrelated optimal discriminant vectors. Pattern Recognition, 36(8): 1921–1923 (IF: 1.611).
|
|
X. Jing, David Zhang, & Zhong Jin. (2003). Improved algorithm and generalized theory. Pattern Recognition, 36(11): 2593–2602 (IF: 1.611).
|
|
Yong Xu, Jing-Yu Yang, & Zhong Jin. (2003). Theory analysis on FSLDA and ULDA. Pattern Recognition, 36(12): 3031–3033 (IF: 1.611).
|
|
Zhong Jin, Franck Davoine, & Zhen Lou. (2003). Facial expression analysis by using KPCA.
|
|
Oriol Pujol, & Petia Radeva. (2003). Texture Segmentation by Statistic Deformable Models. International Journal of Image and Graphics (IJIG).
|
