High‐Throughput Object Recognition and Sizing in Disperse Systems

Object Recognition based on Local Steering Kernel and SVM

The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...

Rheology of Disperse Systems

The rheology of disperse systems is an important processing parameter. Being able to characterize and manipulate the flow behavior of dispersions one can ensure their optimal performance. Waterborne automotive coatings, for example, should exhibit a distinct low-shear viscosity necessary to provide good leveling but to avoid sagging at the same time. Then, a strong degree of shear thinning is n...

Architecture and Performance of Large and Disperse Distributed Object Base Systems

Object Recognition in Swarm Systems: Preliminary Results

Object recognition is a fundamental topic for the development of robotic systems able to interact with the environment. Most existing methods are based on vision systems and assume a broad point of view over the objects, which are observed in their entirety. This assumption is sometimes difficult to fulfill in practice, and in particular in swarm systems, constituted by a multitude of small rob...

Constraint-Based Object Recognition in Multisensor Systems