Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

The role of the lateral occipital cortex in aesthetic appreciation of representational and abstract paintings: a TMS study.

Neuroimaging studies of aesthetic appreciation have shown that activity in the lateral occipital area (LO)—a key node in the object recognition pathway—is modulated by the extent to which visual artworks are liked or found beautiful. However, the available evidence is only correlational. Here we used transcranial magnetic stimulation (TMS) to investigate the putative causal role of LO in the aesthetic appreciation of paintings. In our first experiment, we found that interfering with LO activity during aesthetic appreciation selectively reduced evaluation of representational paintings, leaving appreciation of abstract paintings unaffected. A second experiment demonstrated that, although the perceived clearness of the images overall positively correlated with liking, the detrimental effect of LO TMS on aesthetic appreciation does not owe to TMS reducing perceived clearness. Taken together, our findings suggest that object-recognition mechanisms mediated by LO play a causal role in aesthetic appreciation of representational art.

Image, video and 3D data registration: medical, satellite and video processing applications with quality metrics

Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods. This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state–of–the–art registration methodologies used in a variety of targeted applications.