Development of a fine-grained parallel Karhunen–Loève transform

Fleury, Martin; Self, R. P.; Downton, A.C.

doi:10.1016/j.jpdc.2004.03.003

Cited by 8 publications

(10 citation statements)

References 22 publications

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“…responds to the requirements of many applications, where the goal is the detection of new objects in the scene, even in those cases where, for a variety of reasons, (changes in lighting for example) a continuous update of the background model is required. The proposed solution is a significant improvement on other hybrid solutions based on the use of a PC and an FPGA [5]. The complete integrated development of the PCA algorithm on an FPGA was a task that until now had not been achieved or performed, at least according to our thorough review of related work done on this topic.…”

Section: Discussionmentioning

confidence: 99%

“…In [5] for example, all of the PCA is implemented on the FPGA, however the calculation of eigenvalues is implemented on a PC due to it is mathematically too complex to be implemented on the FPGA. In [6] on the other hand, a variant of PCA called a Modular PCA, applied to face recognition, has been implemented on an FPGA, as this version of PCA has a much lower volume of mathematical operations than the conventional PCA algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, executing PCA is normally divided between an FPGA and a PC or microprocessor [5], so that normally an ad-hoc HW/SW partition of the system is made, without adequately exploring the design space (HW/SW co-design methods).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis

Bravo

Mazo

Lázaro

et al. 2010

Sensors

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This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis

Bravo

Mazo

Lázaro

et al. 2010

Sensors

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“…A previous related work developed a KLT computing system on an FPGA-based SoC platform [3], where a parallel approach was proposed for the mapping of the KLT algorithm. However, in [3] only hyperspectral data with a limited number of bands were considered and low power consumption was not targeted.…”

Section: Introductionmentioning

confidence: 99%

Adaptive hyperspectral image compression using the KLT and integer KLT algorithms

Egho

Vladimirova

2014

2014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)

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The use of the Karhunen-Loéve Transform (KLT) for spectral decorrelation in compression of hyperspectral satellite images results in improved performance. However, the KLT algorithm consists of sequential processes, which are computationally intensive, such as the covariance matrix computation, eigenvector evaluation and matrix multiplications. These processes slow down the overall computation of the KLT transform significantly. The traditional KLT can only offer lossy compression; therefore, a reversible KLT algorithm, named the Integer KLT, is used for lossless compression. The Integer KLT includes more computational processes and, hence, it requires a longer processing time. The acceleration of these processes within the context of limited power and hardware budgets is the main objective of this paper. The computations of each of these processes are investigated thoroughly. Subsequently, a novel adaptive architecture for the computation of the KLT and the Integer KLT is proposed. The proposed system improves the traditional KLT performance compared with a previous architecture, and offers significant improvement for hyperspectral data with a larger spectral dimension. The experiments showed an overall improvement of up to 4.9%, 11.8% and 18.4% for 8, 16 and 32 spectral bands, respectively. In addition, this paper addresses novel hardware aspects of the Integer KLT implementation. The scalability of this hardware architecture can offer much higher level of parallel computing than processor platforms

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“…For PCA based on matrix operations, systolic arrays have been used to compute eigenvectors via singular value decomposition (SVD) (Schreiber 1986). Additionally, parallel processing (Subramanian, Gat, Ratcliff, and Eismann 2000;Fleury, Self, and Downton 2004;Kumar, Kamakoti, and Das 2007) has been employed; in such schemes, the covariance-matrix computation and PC transform permit fine-grain parallelism but the eigenvector generation does not, leading to an offline computation of the latter in many cases. PCA based on neural networks is generally more suitable to VLSI implementation because of topology regularity.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Principal Component Analysis for Hyperspectral Image Compression

Fowler

2008

The International Journal of High Performance Computing Applica

110

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Abstract-Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in conjunction with JPEG2000 for hyperspectral-image compression. However, the computational cost of determining the data-dependent PCA transform is high due to its traditional eigendecomposition implementation which requires calculation of a covariance matrix across the data. Several strategies for reducing the computation burden of PCA are explored, including both spatial and spectral subsampling in the covariance calculation as well as an iterative algorithm that circumvents determination of the covariance matrix entirely. Experimental results investigate the impacts of such low-complexity PCA on JPEG2000 compression of hyperspectral images, focusing on rate-distortion performance as well as data-analysis performance at an anomaly-detection task.

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Development of a fine-grained parallel Karhunen–Loève transform

Cited by 8 publications

References 22 publications

An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis

An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis

Adaptive hyperspectral image compression using the KLT and integer KLT algorithms

Low-Complexity Principal Component Analysis for Hyperspectral Image Compression

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