4-Picoline-N-oxide complexes of rare-earth bromides

In continuation of our work on the effect of the anion on the coordination chemistry of the rare-earth metal ions, we have now extended our studies to 4-picoline-N-oxide (4-Pie NO) complexes of rare-earth bromides. By ohangi~ the method of preparation Harrison and Watsom (1) have prepared two types of Sm(IIl) complexes and three types of Eu(III) complexes of 4-pioollne-N-Oxide in the presence of perchlorate ions. We have isolated two types of pyridine-N-Oxide complexes of rare-earth bromides, also by changing the method of preparation (2). The effect of the change of the preparative method on the composition of the lanthanide complexes is exhibited in the case of other complexes also (3-6). But our attempts to prepare 4-picoline-N-Oxide of rare-earth bromides having different stoichiometries were unsucessful . The composition of the complexes is the same for all the complexes prepared. The results of the physico-chemical studies on these 4-Pic NO complexes of rare-earth bromides are discussed in the present paper.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.