An Improved Colorimetric Invariants and RGB-Depth-Based Codebook Model for Background Subtraction Using Kinect
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | An Improved Colorimetric Invariants and RGB-Depth-Based Codebook Model for Background Subtraction Using Kinect |
| Type de publication | Conference Paper |
| Year of Publication | 2014 |
| Auteurs | Murgia J, Meurie C, Ruichek Y |
| Editor | Gelbukh A, Espinoza FC, GaliciaHaro SN |
| Conference Name | HUMAN-INSPIRED COMPUTING AND ITS APPLICATIONS, PT I |
| Publisher | Mexican Soc Artificial Intelligence; Govt Chiapas; Ist Tecnologico Tuxtla Gutierrez; Univ Autonoma Chiapas; Centro Investigac Computac Ist Politecnico Nacl; Univ Autonoma Estado Hidalgo; Univ Nacl Autonoma Mexico |
| Conference Location | HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY |
| ISBN Number | 978-3-319-13647-9; 978-3-319-13646-2 |
| Mots-clés | colorimetric invariants, fusion, Image color analysis, object detection, RGB-D, segmentation, subtraction techniques |
| Résumé | In this paper we propose to join the benefits of multiple invariant information into the well-know background subtraction method `` Codebook''. Indeed, this method mainly repose on a color model allowing a separate process of color and intensity distortion. In order to manage hard situations involving high illumination changes, we propose to enhance this model with the use of two supplementary steps: 1/transforming the input color image using a colorimetric invariant in order to obtain a color-invariant image whatever the illumination conditions; 2/using depth information as a new data inside the Codebook model, thus performing an RGB-D fusion during the segmentation process. |