| Full text | |
| Author(s): |
Ribas, Lucas C.
;
Scabini, Leonardo
;
Bruno, Odemir M.
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2022 ELEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA); v. N/A, p. 5-pg., 2022-01-01. |
| Abstract | |
Fish otolith recognition is an essential task to study the evolution and food chains in paleontological and ecological sciences. One of the approaches to this problem is to automatically analyze the shape of otolith contour present in images. In this paper, we explore a state-of-the-art shape analysis method called "angular descriptors of complex networks (ADCN)" applied to the classification of otolith images for fish species recognition. The ADCN method models the otolith contour as a graph, or complex network, and computes angular properties from its connections for shape characterization. The ADCN method is evaluated in an otolith image dataset composed of 14 fish species from three families. Up to 95.71% of accuracy is achieved, which surpasses other literature methods and confirms that the ADCN method can be an important tool for such biological problems. (AU) | |
| FAPESP's process: | 21/07289-2 - Learning Representations using artificial neural networks and complex networks with applications in sensors and biosensors |
| Grantee: | Lucas Correia Ribas |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 16/18809-9 - Deep learning and complex networks applied to computer vision |
| Grantee: | Odemir Martinez Bruno |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis |
| Grantee: | Osvaldo Novais de Oliveira Junior |
| Support Opportunities: | Research Projects - Thematic Grants |