Robust Bioinspired Architecture for Optical-Flow Computation

Botella, Guillermo; García, Antonio; Rodríguez-Álvarez, Manuel; Ros, Eduardo; Meyer-Baese, Uwe; Molina, M.C.

doi:10.1109/tvlsi.2009.2013957

Cited by 77 publications

(49 citation statements)

References 29 publications

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“…Stage II performs the spatial differentiation building a pyramidal structure of each temporal derivative. Figure 9 represents what the authors (Botella et al, 2010) call as "Convolutive Unit Cell" which implements the separable convolution organized in rows and columns. Each part of this cell will be replicated sufficiently to perform a pyramidal structure.…”

Section: Multichannel Gradient Modelmentioning

confidence: 99%

“…After that, Tomasi has implemented in 2010 and 2011 a fully real-time multimodal system mixing motion estimation and binocular disparity (Tomassi et al, 2010(Tomassi et al, , 2011) combining low-level and mid-level vision primitives. • Botella et al implemented a robust gradient based optical flow real-time system and its extension to mid-level vision combining orthogonal variant moments (Botella et al, 2009(Botella et al, , 2010. Also the block matching acceleration motion estimation has been implemented in real-time by Gonzalez and Botella (González et al, 2011).…”

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confidence: 99%

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Real-Time Motion Processing Estimation Methods in Embedded Systems

Botella¹,

González²

2012

Real-Time Systems, Architecture, Scheduling, and Application

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Section: Multichannel Gradient Modelmentioning

confidence: 99%

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confidence: 99%

Real-Time Motion Processing Estimation Methods in Embedded Systems

Botella¹,

González²

2012

Real-Time Systems, Architecture, Scheduling, and Application

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“…In recent decades, the field programmable gate array (FPGA) has been widely used in the image processing (such as imaging compression [7,8], filtering [9][10][11], edge detection [12,13], real-time processing of video images [14,15], and motion estimation [16][17][18]) to make real-time processing come true. González et al [16,17] optimized matching-based motion estimation algorithms using an Altera custom instruction-based paradigm and a combination of synchronous dynamic random access memory (SDRAM) and on-chip memory in Nios II processors, and presented a low-cost system.…”

Section: Introductionmentioning

confidence: 99%

On-Board Ortho-Rectification for Images Based on an FPGA

et al. 2017

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Abstract:The traditional ortho-rectification technique for remotely sensed (RS) images, which is performed on the basis of a ground image processing platform, has been unable to meet timeliness or near timeliness requirements. To solve this problem, this paper presents research on an ortho-rectification technique based on a field programmable gate array (FPGA) platform that can be implemented on board spacecraft for (near) real-time processing. The proposed FPGA-based ortho-rectification method contains three modules, i.e., a memory module, a coordinate transformation module (including the transformation from geodetic coordinates to photo coordinates, and the transformation from photo coordinates to scanning coordinates), and an interpolation module. Two datasets, aerial images located in central Denver, Colorado, USA, and an aerial image from the example dataset of ERDAS IMAGINE 9.2, are used to validate the processing speed and accuracy. Compared to traditional ortho-rectification technology, the throughput from the proposed FPGA-based platform and the personal computer (PC)-based platform are 11,182.3 kilopixels per second and 2582.9 kilopixels per second, respectively. This means that the proposed FPGA-based platform is 4.3 times faster than the PC-based platform for processing the same RS images. In addition, the root-mean-square errors of the planimetric coordinates ϕ X and ϕ Y and the distance ϕ S are 1.09 m, 1.61 m, and 1.93 m, respectively, which can meet the requirements of correction accuracy in practice.

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“…The final aim of optical flow estimation is to compute an approximation to the motion field from timevarying image intensity. Several different real-time approaches to motion estimation have been proposed [1][2][3][4][5][6][7][8][9], and these could preliminarily be classified as belonging to matching domain approximations [10], energy models [11] and gradient models [12,13]. Despite the number of different models and algorithms [14], none of them covers all the problems associated with real-world processing, such as noise, illumination changes, second order motion, occlusions, etc.…”

Section: Introductionmentioning

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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Robust Bioinspired Architecture for Optical-Flow Computation

Cited by 77 publications

References 29 publications

Real-Time Motion Processing Estimation Methods in Embedded Systems

Real-Time Motion Processing Estimation Methods in Embedded Systems

On-Board Ortho-Rectification for Images Based on an FPGA

Real-time motion estimation for image and video processing applications

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