Geometric Computer Vision

Geometric reasoning for computer vision

Some general principles are formulated about geometric reasoning in the context of model-based computer vision. Such reasoning tries to draw inferences about the spatial relationships between objects in a scene based on the fragmentary and uncertain geometric evidence provided by an image. The paper discusses the tasks the reasoner is to perform for the vision program, the basic competences it ...

Applications of Geometric Algebra in Robot Vision

In this tutorial paper we will report on our experience in the use of geometric algebra (GA) in robot vision. The results could be reached in a long term research programme on modelling the perceptionaction cycle within geometric algebra. We will pick up three important applications from image processing, pattern recognition and computer vision. By presenting the problems and their solutions fr...

Computer assisted instruction during quarantine and computer vision syndrome

Computer vision syndrome (CVS) is a set of visual, ocular, and musculoskeletal symptoms that result from long-term computer use. These symptoms include eyestrain, dry eyes, burning, pain, redness, blurred vision, etc, which increase with the duration of computer use. Currently, with the closure of schools and universities due to the continued COVID19 pandemic many universities have taken the pr...

Idiap Geometric Matching in Computer Vision Algorithms and Open Problems

In this paper I review and discuss recent advances in the devel opment of algorithms for solving the kinds of geometric match ing problems that occur in computer vision in particular under bounded error models I examine both the one model case recog nition and the many model case indexing and I discuss some open problems and directions for research

An Investigation of Structural-Mechanical Properties of Spun-Bonded Non-Woven Using Computer Vision Method

gvnn: Neural Network Library for Geometric Computer Vision

We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of Spatial Transformer Networks, we propose several new layers which are often used as parametric transformations on the data in geometric computer vision. These layers can be inserted within a neural network much in the ...