Image rectification is the process of warping a pair of stereo images in order to align the epipolar lines with the scan-lines of the images. Once a pair of images is rectified, stereo matching can be implemented in an efficient manner. Given the epipolar geometry, it is straightforward to define a rectifying transformation, however, many transformations will lead to unwanted image distortions....

Real world scenes that contain high dynamic range illumination present a special challenge for stereo matching algorithms due to a lack of texture in overor under-exposed image regions. In this paper, we discuss possibilities for combining state-of-the-art stereo matching algorithms with High Dynamic Range (HDR) imaging techniques, in order to exploit a set of multi-exposed input images of both...

Leaf area measurements are commonly obtained by destructive and laborious practice. This paper shows how stereo and Time-of-Flight (ToF) images can be combined for non-destructive automatic leaf area measurements. We focus on some challenging plant images captured in a greenhouse environment, and show that even the state-of-the-art stereo methods produce unsatisfactory results. By transforming ...

Stereo image matching is reconsidered from the viewpoint of full-information exploitation via a uniform transformation of information through scale space. We consider the general stereo situation where both interior and relative orientation of two images are unknown. It is shown that wavelet multiresolution analysis provides an adequate transformation and representation of image signal informat...

The process that creates stereo images typically takes from 1.5 to 2 times longer than the algorithm for mono images. This paper presents a fast image-based algorithm for computing stereo images, that needs a little more time than the algorithm for mono images. The presented algorithm uses a single image with depth information (e.g. z-buffer) as an input and produces two images for left and rig...

In this paper, a compression algorithm is introduced which allows the efficient storage and transmission of stereo images. The coder uses a block-based disparity estimation/ compensation technique to decorrelate the image pair. To code both images progressively, we have adapted the wellknown SPIHT coder to stereo images. The results presented in this paper are better than any other results publ...

Stereo Vision is an area of study in the field of Machine Vision that attempts to recreate the human vision system by using two or more 2D views of the same scene to derive 3D depth information about the scene. Depth information can be used to track moving objects in 3D space, gather distance information for scene features, or to construct a 3D spatial model of a scene. As an emerging technolog...

Disparity map generation is a significant component of vision-based driver assistance systems. This report describes an efficient implementation of a belief propagation algorithm on GPUs that can be used to speed up stereo image processing. For evaluation purposes, different kinds of images have been used: reference images from the Middlebury stereo website, and real-world stereo sequences, sel...

This paper proposes a discrete wavelet-based stereo matching technique. A discrete wavelet transform is first applied to a pair of stereo images to decorrelate the images into a number of approximations. Information in the basebands is less sensitive to shift variability of the wavelet transform. A self-adapting dissimilarity measure is employed to generate a disparity map of the stereo pairs. ...

This paper deals with stereo correspondence search, using graph cuts and belief propagation, for estimating depth maps. The results following different preprocessing steps are evaluated, using the quality of the disparity map. Running times are also investigated. For evaluation purposes, different kinds of images have been used: reference images from the Middlebury Stereo website, synthetic dri...