We present a segmentation algorithm to detect low-level structure present in images. The algorithm is designed to partition a given image into regions, corresponding to image structures, regardless of their shapes, sizes, and levels of interior homogeneity. We model a region as a connected set of pixels that is surrounded by ramp edge discontinuities where the magnitude of these discontinuities is large compared to the variation inside the region.
Many computational imaging applications involve manipulating the incoming light beam in the aperture and image planes. However, accessing the aperture, which conventionally stands inside the imaging lens, is still challenging. In this paper, we present an approach that allows access to the aperture plane and enables dynamic control of its transmissivity, position, and orientation.
In this paper, we consider the problem of stereo matching using loopy belief propagation. Unlike previous methods which focus on the original spatial resolution, we hierarchically reduce the disparity search range. By fixing the number of disparity levels on the original resolution, our method solves the message updating problem in a time linear in the number of pixels contained in the image and requires only constant memory space.
Given multiple calibrated pictures of a real world object captured from different viewpoints, reconstruct a three-dimensional model of the object.
- T. Yu, N. Xu and N. Ahuja, Reconstructing a Dynamic Surface from Video Sequences Using Graph Cuts in 4D Space-Time, IEEE International Conference on Pattern Recognition, Cambridge, UK, August 2004, 245-248.
- D. Hougen and N. Ahuja, Integration of Stereo and Shape from Shading using Color, Proc. Second International Conf. on Automation, Robotics and Computer Vision, Vol 1, Singapore, September 15-18 1992, pp. CV-6.6.1 – CV-6.6.5.
- D. Hougen and N. Ahuja, Estimation of the Light Source Distribution and its Use in Shape Recovery from Stereo and Shading, 4th Int.
3D Surface Orientation from Texture Gradient computed in a single image of a homogeneously textured surface.
In an image containing texture elements at a range of scales, detect all elements, their relative locations and mutual containment relationships.
Given a slanted view of a planar, homogeneously textured surface, estimate the surface slant from the image texture gradient.