An Implementation of Combined Local-Global Optical Flow

Jara‐Wilde, Jorge; Cerda, Mauricio; Delpiano, José; Härtel, Steffen

doi:10.5201/ipol.2015.44

Cited by 9 publications

(9 citation statements)

References 9 publications

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“…The CLG method (30,31) combines the HS method and a second method called Lucas-Kanade (LK). LK method assumes the constancy of motion in the neighborhood of a pixel i.e.…”

Section: Combined Local-global (Clg) Methodsmentioning

confidence: 99%

“…Takagaki et al 2011 (29) developed and reported a new method (Temporospatial -TS) in which temporal correlation of a given pixel with neighboring pixels was used to estimate local motion in voltage sensitive dye (VSD) imaging data. Recently, Mohajerani et al 2013 (16) used the combined local-global (CLG) algorithm (30,31) to determine velocity vector fields of flow in wide-field VSD imaging data. They also manually determined the location of sources and sinks in space and time.…”

Section: Introductionmentioning

confidence: 99%

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Optical-flow analysis toolbox for characterization of spatiotemporal dynamics in mesoscale optical imaging of brain activity

et al. 2017

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Wide-field optical imaging techniques constitute powerful tools to investigate mesoscale neuronal activity. The sampled data constitutes a sequence of image frames in which one can investigate the flow of brain activity starting and terminating at source and sink locations respectively. Approaches to the analyses of information flow include qualitative assessment to identify sources and sinks of activity as well as their trajectories, and quantitative measurements based on computing the temporal variation of the intensity of pixels. Furthermore, in a few studies estimates of wave motion have been reported using optical-flow techniques from computer vision. However, a comprehensive toolbox for the quantitative analyses of mesoscale brain activity data is still lacking. We present a graphical-user-interface toolbox based in Matlab® for investigating the spatiotemporal dynamics of mesoscale brain activity using optical-flow analyses. The toolbox includes the implementation of three optical-flow methods namely Horn-Schunck, Combined Local-Global, and Temporospatial algorithms for estimating velocity vector fields of flow of mesoscale brain activity. From the velocity vector fields we determined the locations of sources and sinks as well as the trajectories and temporal velocities of flow of activity. Using simulated data as well as experimentally derived sensory-evoked voltage and calcium imaging data from mice, we compared the efficacy of the three optical-flow methods for determining spatiotemporal dynamics. Our results indicate that the combined local-global method we employed, yields the best results for estimating wave motion. The automated approach permits rapid and effective quantification of mesoscale brain dynamics and may facilitate the study of brain function in response to new experiences or pathology.

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“…The CLG method (30,31) combines the HS method and a second method called Lucas-Kanade (LK). LK method assumes the constancy of motion in the neighborhood of a pixel i.e.…”

Section: Combined Local-global (Clg) Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optical-flow analysis toolbox for characterization of spatiotemporal dynamics in mesoscale optical imaging of brain activity

et al. 2017

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“…This toolbox allows us to estimate the spatiotemporal dynamics, such as trajectory and speed, of pixels in our VSD recordings, using a variety of optical flow analysis methods. We used the Horn-Schuck (HS) method 27,61 for our optical flow analysis, and repeated select analyses with the Combined Local-Global (CLG) method, which combines the HS method with the Lucas-Kanade (LK) method 27,62,63 . The main difference between these methods is that the LK method assumes a pixel's motion is constant relative to neighboring pixels, whereas the HS method does not make this assumption 27 .…”

Section: Optical Flow Analysismentioning

confidence: 99%

Altered cortical processing of sensory input in Huntington disease mouse models

Sepers

Mackay

Koch

et al. 2021

Preprint

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Huntington disease (HD), a hereditary neurodegenerative disorder, manifests as progressively impaired movement and cognition. Although early abnormalities of neuronal activity in striatum are well established in HD models, there are fewer in vivo studies of the cortex. Here, we record local field potentials (LFPs) in YAC128 HD model mice versus wild-type mice. In multiple cortical areas, limb sensory stimulation evokes a greater change in LFP power in YAC128 mice. Mesoscopic imaging using voltage-sensitive dyes reveal more extensive spread of evoked sensory signals across the cortical surface in YAC128 mice. YAC128 layer 2/3 sensory cortical neurons ex vivo show increased excitatory events, which could contribute to enhanced sensory responses in vivo. Cortical LFP responses to limb stimulation, visual and auditory input are also significantly increased in zQ175 HD mice. Results presented here extend knowledge of HD beyond ex vivo studies of individual neurons to the intact cortical network.

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“…In previous works, matrix splitting methods have often been used. Horn & Schunck rely on a Jacobi-like iteration, while Jara-Wilde et al use a socalled Pointwise-Coupled Gauss-Seidel in [8]. Krylov methods such as Conjugate Gradient (CG) can be found in [4].…”

Section: Solvers Reviewmentioning

confidence: 99%

An Efficiency-Driven Approach For Real-Time Optical Flow Processing On Parallel Hardware

Seznec

Gac

Orieux

et al. 2020

2020 IEEE International Conference on Image Processing (ICIP)

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This article tackles the entire lifecycle of an algorithm: from its design to its implementation. It exhibits a method for making efficient choices at algorithm design time knowing the characteristics of the underlying hardware target. As of today, computing the optical flow of a stream of images is still a demanding task. In the meantime, the use of Graphics Processing Units (GPU) has become mainstream and allows substantial gains in processing frame rate. In this paper, we focus on a specific variational method (CLG [1]) where linear systems have to be solved. They depend on two parameters α and ρ. To efficiently solve the problem, we look at convergence speed with respect to the model's parameters. We benchmark usual linear solvers with preconditioners to identify the fastest in terms of convergence per iteration. We then show that once implemented on GPUs, the most efficient solver changes depending on the model parameters. For 640 × 480 images, with the right choice of solver and parameters, our implementation can solve the system with relative 10e −7 accuracy in 0.25 ms on a Titan V GPU. All the results are aggregated on a 30-image set to increase confidence in their extendability.

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An Implementation of Combined Local-Global Optical Flow

Cited by 9 publications

References 9 publications

Optical-flow analysis toolbox for characterization of spatiotemporal dynamics in mesoscale optical imaging of brain activity

Optical-flow analysis toolbox for characterization of spatiotemporal dynamics in mesoscale optical imaging of brain activity

Altered cortical processing of sensory input in Huntington disease mouse models

An Efficiency-Driven Approach For Real-Time Optical Flow Processing On Parallel Hardware

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