This paper addresses the problem of region-based color correction. Our solution begins with the image segmentation by marker-controlled watershed transformation, which is faster and produces more uniform regions with better adherence to object boundaries than the segmentation in previous works. Next, regions between two images are matched using point feature correspondences which are invariant to geometric transformation and illumination changes. Finally, the color-corrected image is generated from the color transfer functions of corresponding regions. We demonstrate the results of this approach using different sets of images.

This paper proposes a novel approach of line matching across images captured by different types of cameras, from perspective to omnidirectional ones. Based on the spherical mapping, this method utilizes spherical SIFT point features to boost line matching and searches line correspondences using an affine invariant measure of similarity. It permits to unify the commonest cameras and to process heterogeneous images with the least distortion of visual information.

We present a translation estimation method for single viewpoint (SVP) cameras using line features. Images captured by multiple central cameras such as perspective, central catadioptric and fisheye cameras are mapped to spherical images using the unified projection model. It is possible to recover the camera rotations using vanishing points of parallel line sets. We then estimate the translations from known rotations and line images on the spheres. The algorithm has been validated on simulated data and real images. This vision-based estimation approach can be applied in navigation of autonomous robots besides the conventional devices such as Global Positioning System (GPS) and Inertial Navigation System (INS). It helps vision-based localization of a single robot or recovery of relative positions among multiple robots equipped with different types of cameras.