The FPGA implementation of a one-bit-per-pixel image registration algorithm

Nguyen, An Hung; Pickering, Mark; Lambert, Andrew

doi:10.1007/s11554-014-0420-3

Cited by 15 publications

(8 citation statements)

References 24 publications

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“…Our proposed binary approach (we call one-bit KWA) was compared with its original version, the kernel-warping algorithm (KWA), the multi-pass I2A algorithm (MP-I2A) and the Lucas-Kanade algorithm (LK) [3] processing fullresolution images, and the ALC algorithm processing binary images (one-bit ALC) [4]. The maximum number of iterations applied to the LK, the MP-I2A, the one-bit KWA and the original KWA algorithms was 25, while this number for the ALC algorithm was 40.…”

Section: Simulational Resultsmentioning

confidence: 99%

The FPGA implementation of an image registration algorithm using binary images

Nguyen

Pickering

Lambert

2014

2014 International Conference on ReConFigurable Computing and FPGAs (ReConFig14)

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The FPGA implementation of image registration algorithms is a challenging problem due to the limited resources of the hardware and the requirement for real-time processing speeds. Image registration approaches using low bit-resolution images are more feasible for implementation on FPGAs than those using full resolution images because of the significant reduction in hardware resources required. The real-time processing requirement can also be satisfied with the use of simple logic operations such as AND, XOR and NOT instead of more complex computations such as additions and multiplications. This paper presents the implementation of an image registration algorithm on two FPGAs from the SPARTAN-3E family for the case of translational motion.

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Section: Simulational Resultsmentioning

confidence: 99%

The FPGA implementation of an image registration algorithm using binary images

Nguyen

Pickering

Lambert

2014

2014 International Conference on ReConFigurable Computing and FPGAs (ReConFig14)

Self Cite

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“…• Finally, Nguyen et al [37] address the need for small light-weight vehicles, such as unmanned ground or air vehicles, to sense their own motion for use in autonomous navigation algorithms. As the processing is ideally performed on-board these vehicles, there are severe restrictions on the processing environment available to perform the optical flow calculations.…”

Section: In the Context Of Real-time Tracking And Navigation Systemsmentioning

confidence: 99%

Real-time motion estimation for image and video processing applications

Botella

Garcı́a

2014

J Real-Time Image Proc

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This contribution focuses on different topics that are covered by the special issue titled ''Real-Time Motion Estimation for image and video processing applications'' and which incorporate GPUS, FPGAs, VLSI systems, DSPs, and Multicores, among other platforms. The guest editors have solicited original contributions, which address a wide range of theoretical and practical issues related to high-performance motion estimation image processing including, but not limited to: real-time matching motion estimation systems, real-time energy-based motion estimation systems, gradient-based motion estimation systems, optical flow estimation systems, color motion estimation systems, multi-scale motion estimation systems, optical flow and motion estimation systems, analysis or comparison of specialized architectures for motion estimation systems and realworld applications.

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“…Therefore, to get an estimate of the total time required to form a quantum plenoptic image, the data reading and transmission times must be added to the acquisition time of the employed sensor. This problem is addressed by an interdisciplinary approach, involving the development of ultrafast single-photon sensor systems, based on SPAD arrays [17][18][19][20][21][22], the optimization of circuit electronics to collect and manage the high number of frames (e.g., by GPU) [23,24], the development of dedicated algorithms (compressive sensing, machine learning, quantum tomography) to achieve the desired SNR with a minimal number of acquisitions [25][26][27][28]. Finally, the performances of QPI will be further enhanced by a novel approach to imaging based on quantum Fisher information [29,30].…”

Section: Introductionmentioning

confidence: 99%

Towards Quantum 3D Imaging Devices

et al. 2021

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We review the advancement of the research toward the design and implementation of quantum plenoptic cameras, radically novel 3D imaging devices that exploit both momentum–position entanglement and photon–number correlations to provide the typical refocusing and ultra-fast, scanning-free, 3D imaging capability of plenoptic devices, along with dramatically enhanced performances, unattainable in standard plenoptic cameras: diffraction-limited resolution, large depth of focus, and ultra-low noise. To further increase the volumetric resolution beyond the Rayleigh diffraction limit, and achieve the quantum limit, we are also developing dedicated protocols based on quantum Fisher information. However, for the quantum advantages of the proposed devices to be effective and appealing to end-users, two main challenges need to be tackled. First, due to the large number of frames required for correlation measurements to provide an acceptable signal-to-noise ratio, quantum plenoptic imaging (QPI) would require, if implemented with commercially available high-resolution cameras, acquisition times ranging from tens of seconds to a few minutes. Second, the elaboration of this large amount of data, in order to retrieve 3D images or refocusing 2D images, requires high-performance and time-consuming computation. To address these challenges, we are developing high-resolution single-photon avalanche photodiode (SPAD) arrays and high-performance low-level programming of ultra-fast electronics, combined with compressive sensing and quantum tomography algorithms, with the aim to reduce both the acquisition and the elaboration time by two orders of magnitude. Routes toward exploitation of the QPI devices will also be discussed.

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The FPGA implementation of a one-bit-per-pixel image registration algorithm

Cited by 15 publications

References 24 publications

The FPGA implementation of an image registration algorithm using binary images

The FPGA implementation of an image registration algorithm using binary images

Real-time motion estimation for image and video processing applications

Towards Quantum 3D Imaging Devices

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