Kernelized structural classification for 3D dogs body parts detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

A frontal view gait recognition based on 3D imaging using a time of flight camera

Studies have been carried out to recognize individuals from a frontal view using their gait patterns. In previous work, gait sequences were captured using either single or stereo RGB camera systems or the Kinect 1.0 camera system. In this research, we used a new frontal view gait recognition method using a laser based Time of Flight (ToF) camera. In addition to the new gait data set, other contributions include enhancement of the silhouette segmentation, gait cycle estimation and gait image representations. We propose four new gait image representations namely Gait Depth Energy Image (GDE), Partial GDE (PGDE), Discrete Cosine Transform GDE (DGDE) and Partial DGDE (PDGDE). The experimental results show that all the proposed gait image representations produce better accuracy than the previous methods. In addition, we have also developed Fusion GDEs (FGDEs) which achieve better overall accuracy and outperform the previous methods.

3D Curvature Contrast - a geometric or brightness illusion?

Simultaneous contrast effects have been found across a wide range of visual dimensions. We describe a simultaneous contrast effect - three-dimensional curvature contrast - in which the apparent curvature of a surface defined by shading and texture information is influenced by the curvature of a surrounding surface. The effect is strong and easily measurable. We asked whether the effect depends upon the presence of contrast at the level of the internal representation of surface curvature or whether it could be better explained in terms of local changes in the apparent brightness of regions within the test patches induced by luminance transition at the borders. The experimental results suggest that, whicle these luminance-contrast-induced effects do contribute to the observed changes in perceived curvature, there are additional influences. In particular changes in perceived curvature induced by a pattern of curved patches were eliminated or considerably weakened when the inducing pattern was transformed into a photographic negative, a procedure which disrupts the apparent three-dimensional structure of the surface patches without changing their brightness contrast. This suggests a component of the illusion involves comparisons at the level of representation of surface curvature. The observation that three-dimensional curvature contrast presists when the inducing surfaces are spatially separate from the test surface suggests that shape perception involves global, as well as local, operations.