A Novel Multi-Source Image Registration of Porcine Body for Multi-Feature Detection

The safety of animal-related agricultural products has been a hot issue. To obtain a multi-feature representation of porcine bodies for detecting their health, visible and infrared imaging is valuable for exploiting multiple images of a porcine body from different modalities. However, the direct registration of visible and infrared porcine body images can easily cause the dislocation of structural information and spatial position, due to different resolutions and spectrums of multi-source images. To overcome the problem, a novel multi-source image feature representation method based on contour angle orientation is proposed and named Gabor-Ordinal-based Contour Angle Orientation (GOCAO). Moreover, a visible and infrared porcine body image registration method is described and named GOCAO-Rough to Fine (GOCAO-R2F). First, contour and texture features of the porcine body are acquired using a Gabor filter with variable scales and an ordinal operation. Second, feature points in contours are obtained by curvature scale space (CSS), and the main orientation of each feature point is determined by GOCAO. Third, modified scale-invariant feature transform (MSIFT) features are received on the main orientation and registered with bilateral matching. Finally, accurate registrations are extracted by R2F. Experimental results show that the proposed registration algorithm accurately matches multi-source images for porcine body multi-feature detection and is capable of achieving lower average root-mean-square error than current registration algorithms.