Prachya Chalermwat

2-phase GA-based image registration on parallel clusters

Abstract Genetic algorithms (GAs) are known to be robust for search and optimization problems. Image registration can take advantage of the robustness of GAs in finding best transformation between two images, of the same location with slightly different orientation, produced by moving spaceborne remote sensing instruments. In this paper, we present 2-phase sequential and coarse-grained parallel image registration algorithms using GAs as optimization mechanism. In its first phase, the algorithm finds a small set of good solutions using low-resolution versions of the images. Based on these candidate low-resolution solutions, the algorithm uses the full resolution image data to refine the final registration results in the second phase. Experimental results are presented and revealed that our algorithms yield very accurate registration results for LandSat Thematic Mapper images, and the parallel algorithm scales quite well on the Beowulf parallel cluster.

Multi-resolution image registration using genetics

In remote sensing, image registration is to find the best transform between a reference and input images that may be different due to changes in position or altitude of or noise in the sensors. Image registration is one of the first steps in the analysis of remotely sensed images and requires high computational resources. The computation time is affected by two factors: search data size and search space. This paper describes an efficient image registration algorithm that uses multi-resolution wavelet decomposed images to reduce the search data size, and Genetic Algorithms to optimize the search solution space. Experimental results have shown subpixel accuracy and high efficiency over conventional methods.

First evaluation of automatic image registration methods

As the need for automating registration techniques is recognized, the authors feel that there is a need to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. The authors present the first steps towards this quantitative evaluation: a few automatic image registration algorithms are described and first results of their evaluation are presented for three different datasets.

Wavelet-Based Image Registration on Parallel Computers

Digital image registration is very important in many applications, such as medical imagery, robotics, visual inspection, and remotely sensed data processing. NASA s Mission To Planet Earth (MTPE) program will be producing enormous Earth global change data, reaching hundreds of Gigabytes per day, that are collected form different spacecrafts and different perspectives using many sensors with diverse resolutions and characteristics. The analysis of such data requires integration, therefore, accurate registration of these data. Image registration is defined as the process which determines the most accurate relative orientation between two or more images, acquired at the same or different times by different or identical sensors. Registration can also provide the absolute orientation between an image and a map.Digital image registration is very important in many applications, such as medical imagery, robotics, visual inspection, and remotely sensed data processing. NASA s Mission To Planet Earth (MTPE) program will be producing enormous Earth global change data, reaching hundreds of Gigabytes per day, that are collected form different spacecrafts and different perspectives using many sensors with diverse resolutions and characteristics. The analysis of such data requires integration, therefore, accurate registration of these data. Image registration is defined as the process which determines the most accurate relative orientation between two or more images, acquired at the same or different times by different or identical sensors. Registration can also provide the absolute orientation between an image and a map.

Parallel image processing in heterogeneous computing network systems

We present a uniform construct of parallel programming for a set of image processing tasks based on our distributed computing primitive (DCP) concept. Our target architecture is a heterogeneous computing network system consisting of various high performance workstations connected through a local area network. We show that DCP has advantages over non-primitive PVM-based parallel approaches in three aspects: ease-of-use, automation, and optimization.

GA-Based Parallel Image Registration on Parallel Clusters

Genetic Algorithms (GAs) have been known to be robust for search and optimization problems. Image registration can take advantage of the robustness of GAs in finding best transformation between two images, of the same location with slightly different orientation, produced by moving spaceborne remote sensing instruments. In this paper, we have developed sequential and coarse-grained parallel image registration algorithms using GA as an optimization mechanism. In its first phase the algorithm finds a small set of good solutions using low-resolution versions of the images. Based on the results from the first phase, the algorithm uses full resolution image data to refine the final registration results in the second phase. Experimental results are presented and we found that our algorithms yield very accurate registration results and the parallel algorithms scales quite well on the Beowulf parallel cluster.

Image partitioning using system characteristics in heterogeneous computing systems

Many image processing tasks can be computed efficiently in a single program multiple data (SPMD) fashion on massively parallel systems. Although executing SPMD tasks on coarse-grained heterogeneous systems yields a cost-effective solution, heterogeneity introduces more complexity in data partitioning, mapping, and scheduling problems. In this paper, three image data partitioning schemes for parallel image processing in heterogeneous systems are investigated and implemented using the parallel virtual machine message passing library. The partitioning schemes are based on the system characteristics (processing capability) incorporated within the distributed computing primitives (DCP) environment using SpecInt92 benchmark and our DCP-based benchmark. We compare the results with the baseline (Eq-based) scheme that equally partitions images regardless of processing speed. The results from the experiments show that the DCP-based partitioning scheme outperforms the Eq-based and Spec-based schemes.