This brief focuses on two main problems in the domain of optical flow and trajectory estimation: (i) The problem of finding convex optimization methods to apply sparsity to optical flow; and (ii) The problem of how to extend sparsity to ...

... flow; it is perpendicular to the object boundary (describing the local surface e). The gradient of e or ∇e, is then: 1 1 ) T ∇e(p,t) = vnx(x,y 0) , vny(x0 ,y) ( (5) The vector ∇e ... event-based time-to-contact Time-To-Contact.

... estimate L(p,t)ˆ ˆ filter ODE (11) outlined in Sect. 3 one may or frame timestamps). We propose L(p,t) at any time. In practice it is sufficient an at asynchronous the asynchronous update time scheme instances whereby ˆtpk (event new events ...

... anytime the line of sight between the camera and an object is broken . An ... optical flow due to objects entering and leaving the scene as well as from ... estimation methods we will look at next is the density of motion vectors ...

... optical flow for the change in detection of scene with different camera viewpoints in [7]. In [8], they have proposed a new method for facial expression recognition using local region specific mean optical flow and descriptor as local ...

The aim of this book is to analyze and explain them comprehensively.

This book offers a systematic and comprehensive introduction to the visual simultaneous localization and mapping (vSLAM) technology, which is a fundamental and essential component for many applications in robotics, wearable devices, and ...

Embedded vision is the integration of "computer vision" intomachines that use algorithms to decode meaning from observed images or video.It has a wide range of applications to machine learning, artificialintelligence, industrial, medical, ...