17555851
Sensor Transforms for Invariant Image Enhancement
The invariant image [1,2]formed from an RGB image taken under light that can be approximated as Planckian solves the colour constancy problem at a single pixel. The invariant is a very useful tool for possible use in a large number of computer vision problems,such as removal of shadows from images [3].This image is formed by projecting log-log chromaticity coordinates into a 1D direction determined by a calibration of the imaging camera. The invariant can be formed whether or not gammacorrection is applied to images and thus can work for ordinary webcam images,for example,once a self-calibration is carried out [3].As such,the invariant image is an important new mechanism for image understanding.Since the resulting greyscale image is approximately independent of illumination,it is impervious to lighting change and hence to the presence of shadows.However,in forming the invariant image,it can sometimes happen that shadows are not completely removed.Here,we consider the problem of simple matrixing of sensor values so that the resulting invariant image is improved.To do so,we consider the calibration images and apply an optimization routine for establishing a 3 x 3 matrix to apply to the sensors, prior to forming the invariant,with an eye to improving lighting invariance.We find that an optimization does indeed improve the invariant.The resulting image generally has smaller entropy value because the invariant value is smoothed out across former shadow boundaries;thus the new invariant more smoothly captures the underlying intrinsic reflectance properties in the scene.
2319896
Range Image Registration Based on Circular Features
Range sensing technology allows the photo-realistic modeling of large-scale scenes, such as urban structures. A major bottleneck in the process of 3D scene acquisition is the automated registration of a large number of geometrically complex 3D range scans in a common frame of reference. The generated 3D representations, after automated registration, are useful for urban planning, historical preservation, or virtual reality applications. Man-made urban scenes provide an abundance of linear features that can be used for the solution of the problem. Many scenes though require the utilization of non-linear primitives. This paper provides a solution of the registration problem based on the robust detection and matching of circular features from the range data sets. We present results from experiments with complex range scans from the interior of a large-scale landmark urban structure (Grand Central Terminal, NYC), where traditional methods would fail. This work is part of a larger range registration system that is based on extracted features of multiple geometric types.
7571930
Using CBIR and pathfinder networks for image database visualisation
Digital images and videos have an increasingly important role in today's telecommunication and our everyday life in modern information society. In this paper, we explore the synergy between content-based image retrieval (CBIR) techniques and Pathfinder networks. Salient image features, based on colour, texture, edge orientation, and edge distance, are extracted. The structural modelling capabilities of Pathfinder are then applied to simplify and visualise the strongest interrelationships in the image database.
12948988
Parallelization of Multiple Genome Alignment
In this work, we implement a genome alignment system which applies parallelization schemes to the ClustalW algorithm and the interface of database querying. Parallel construction of the distance matrices and parallelization of progressive alignment in the ClustalW algorithm are performed on PC-based Linux cluster with message-passing interface libraries. Achieved experiments show good speedup and significant parallel performance.
46218447
Parallelizing Progressive Computation for Skyline Queries in Multi-disk Environment
Given a set of d-dimensional points, skyline query returns the points that are not dominated by any other point on all dimensions. In this paper, we study an interesting scenario of skyline retrieval, where multi-dimensional points are distributed among multiple disks. Efficient algorithms for parallelizing progressive skyline computation are developed, using the parallel R-trees. The core of our scheme is to visit more entries from some disks simultaneously and enable effective pruning strategies with dominance checking to prune away the non-qualifying entries. Extensive experiments with synthetic data confirm that our proposed algorithms are both efficient and scalable.
6045454
Visualizing a Knowledge Domain's Intellectual Structure
To make knowledge visualizations clear and easy to interpret, we have developed a method that extends and transforms traditional author co-citation analysis by extracting structural patterns from the scientific literature and representing them in a 3D knowledge landscape.
5472476
Image Stitching - Comparisons and New Techniques
This work describes the steps involved in the generation of a panoramic image, i.e., image acquisition, image registration and image merging. Different approaches for each of these steps are discussed and compared. The resultant images from image registration and image merging are quantitatively evaluated to provide an indication to the performance of the different methods. The methods provided in this work can be used to generate panoramic images for the use of interactive panoramic viewing of images, architectural walk-through, and other applications associated with the modelling of 3D environments.