An automated parallel image registration technique based on the correlation of wavelet features
ABSTRACT With the increasing importance of multiple multiplatform remote sensing missions, fast and automatic integration of digital data from disparate sources has become critical to the success of these endeavors. Our work utilizes maxima of wavelet coefficients to form the basic features of a correlation-based automatic registration algorithm. Our wavelet-based registration algorithm is tested successfully with data from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and the Landsat Thematic Mapper (TM), which differ by translation and/or rotation. By the choice of high-frequency wavelet features, this method is similar to an edge-based correlation method, but by exploiting the multiresolution nature of a wavelet decomposition, our method achieves higher computational speeds for comparable accuracies. This algorithm has been implemented on a single-instruction multiple-data (SIMD) massively parallel computer, the MasPar MP-2, as well as on the CrayT3D, the Cray T3E, and a Beowulf cluster of Pentium workstations.
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ABSTRACT: Reconfigurable Computers (RCs) with hardware (FPGA) co-processors can achieve significant performance improvement compared with traditional microprocessor (μP)-based computers for many scientific applications. The potential amount of speedup depends on the intrinsic parallelism of the target application as well as the characteristics of the target platform. In this work, we use image processing applications as a case study to demonstrate how hardware designs are parameterized by the co-processor architecture, particularly the data I/O, i.e., the local memory of the FPGA device and the interconnect between the FPGA and the μP. The local memory has to be used by applications that access data randomly. A typical case belonging to this category is image registration. On the other hand, an application such as edge detection can directly read data through the interconnect in a sequential fashion. Two different algorithms of image registration, the exhaustive search algorithm and the Discrete Wavelet Transform (DWT)-based search algorithm, are implemented on hardware, i.e., Xilinx Vertex-IIPro 50 on the Cray XD1 reconfigurable computer. The performance improvements of hardware implementations are 10× and 2×, respectively. Regarding the category of applications that directly access the interconnect, the hardware implementation of Canny edge detection can achieve 544× speedup.International Journal of Reconfigurable Computing. 01/2010;
Article: Automatic Registration of Airborne and Spaceborne Images by Topology Map Matching with SURF Processor Algorithm[show abstract] [hide abstract]
ABSTRACT: Image registration is widely used in remote-sensing applications. The existing automatic image registration techniques fall into two categories: Intensity-based and feature-based; the latter (which extracts structures from both images) being more suitable for multi-sensor fusion, detection of temporal changes and image mosaicking. Conventional image registration algorithms have proven to be inaccurate, time-consuming, and unfeasible due to image complexity which makes it cumbersome or even impossible to discern the appropriate control points. In this study, we propose a novel method for automatic image registration based on topology (AIRTop) for change detection and multi‑sensor (airborne and spaceborne) fusion. In this algorithm, we first apply image‑processing methods (SURF—Speeded-Up Robust Features) to extract the landmark structures (roads and buildings) and convert them to a features (vector) map. The following stages are applied in GIS (Geographic Information System), where topology rules, which define the permissible spatial relationships between features, are defined. The relationships between features are established by weight-based topological map-matching algorithm (tMM). The suggested algorithm presents a robust method for image registration. The main focus in this study is on scale and image rotation, when the quality of the scanning system is constant. These seem to offer a good compromise between feature complexity and robustness to commonly occurring deformations. The skew and the anisotropic scaling are assumed to be second-order effects that are covered to some degree by the overall robustness of the sensor.Remote Sensing. 01/2011;
Article: Improving the Performance of Hyperspectral Image and Signal Processing Algorithms Using Parallel, Distributed and Specialized Hardware-Based Systems.Signal Processing Systems. 01/2010; 61:293-315.