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Author Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde edit  url
openurl 
  Title Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization Type Journal Article
  Year 2021 Publication International Journal of Imaging Systems and Technology Abbreviated Journal IMA  
  Volume 31 Issue (down) 4 Pages 2170-2188  
  Keywords  
  Abstract Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods.  
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  Notes MILAB; no menciona Approved no  
  Call Number Admin @ si @ DGR2021a Serial 3630  
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Author F. Pla; Petia Radeva; Jordi Vitria edit  openurl
  Title Non-parametric distance-based classification techniques and their applications Type Journal
  Year 2008 Publication Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications Abbreviated Journal  
  Volume 11 Issue (down) 3-4 Pages 223–225  
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  Publisher Springer Place of Publication Editor  
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  Notes OR;MILAB;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ PRV2008 Serial 999  
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Author Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title Texture Segmentation by Statistical Deformable Models Type Journal
  Year 2004 Publication International Journal of Image and Graphics Abbreviated Journal IJIG  
  Volume 4 Issue (down) 3 Pages 433-452  
  Keywords Texture segmentation, parametric active contours, statistic snakes  
  Abstract Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PuR2004a Serial 505  
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Author Jaume Amores; N. Sebe; Petia Radeva edit  doi
openurl 
  Title Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier Type Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 27 Issue (down) 3 Pages 201–209  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ ASR2006 Serial 643  
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Author Sergio Escalera; Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes Type Journal Article
  Year 2009 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 30 Issue (down) 3 Pages 285–297  
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  Abstract Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied.  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2009a Serial 1153  
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