![Loading...](https://link.springer.com/static/c4a417b97a76cc2980e3c25e2271af3129e08bbe/images/pdf-preview/spacer.gif)
-
Chapter and Conference Paper
Calibrationless Sensor Fusion Using Linear Optimization for Depth Matching
Recently the observation of surveillanced areas scanned by multi-camera systems is getting more and more popular. The newly developed sensors give new opportunities for exploiting novel features.
-
Chapter and Conference Paper
Orthogonality Based Stop** Condition for Iterative Image Deconvolution Methods
Deconvolution techniques are widely used for image enhancement from microscopy to astronomy. The most effective methods are based on some iteration techniques, including Bayesian blind methods or Greedy algori...
-
Chapter and Conference Paper
Markovian Framework for Foreground-Background-Shadow Separation of Real World Video Scenes
In this paper we give a new model for foreground-back-ground-shadow separation. Our method extracts the faithful silhouettes of foreground objects even if they have partly background like colors and shadows ar...
-
Chapter and Conference Paper
Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods
We have developed a new stochastic image rendering method for the compression, description and segmentation of images. This paintbrush-like image transformation is based on a random searching to insert brush-s...