GPU implementation of cross-correlation for image generation in real time

Kapinchev, Konstantin; Bradu, Adrian; Barnes, Frederick R.M.; Podoleanu, Adrian Gh.

doi:10.1109/icspcs.2015.7391783

Cited by 19 publications

(5 citation statements)

References 5 publications

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“…Template matching [19] is a high-level machine vision method that distinguishes the parts on an image that match a prede ned layout. Cross-correlation represents, by de nition, one of such methods as a measure of similarity between two different signals (images in this case), by sliding the rst shorter template signal over the tested signal [20], [21]. The resulting coe cients will de ne a measure of the probability of the template signal to be found on a speci c position on the second signal.…”

Section: Block 1: Ratio Values and Input Video Drift Correctionmentioning

confidence: 99%

SIGAA – SIGnaling Automated Analysis: a new tool for Ca2+ signaling quantification using ratiometric Ca2+ dyes

Lopes

Gonçalves-Ribeiro

Sebastião

et al. 2023

Preprint

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Astrocytes are non-neural cells, restricted to the brain and spinal cord, whose functions and morphology depend on their location. Astrocyte-astrocyte and astrocyte-neuron interactions occur through cytoplasmic Ca2+ levels changes, that are assessed to determine cell function and response (i.e. drug testing). Evaluation of changes in intracellular Ca2+ levels is mostly centered on fluorescence imaging approaches, performed through video recording of cells incubated with Ca2+-sensitive dyes. By observing ion concentration shifts over time in a delimited region of interest (ROI) comprising a single cell, it is possible to attain conclusions on cell responses to specific stimuli. Our work describes a tool named SIGAA – SIGnaling Automated Analysis, for astrocyte ROI-based fluorescent imaging, which is tailored for two wavelengths dyes by using two inputs of Ca2+ signaling recorded frames/videos and outputs a set of features relevant to the experiment’s conclusions and cell characterization. SIGAA performs automatic drift correction for the two recorded videos with a template matching algorithm, followed by astrocyte identification (ROI) using morphological reconstruction techniques. SIGAA then extracts intracellular Ca2+ evolution functions for all identified ROIs, detects function transients, and estimates a set of features for each signal, which are very similar to the ones obtained by the traditional methods and software used so far. SIGAA is a new fully automated tool, which can speed up hour-long studies and analysis to a few minutes, showing reliable results as the validity tests indicate.

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Section: Block 1: Ratio Values and Input Video Drift Correctionmentioning

confidence: 99%

SIGAA – SIGnaling Automated Analysis: a new tool for Ca2+ signaling quantification using ratiometric Ca2+ dyes

Lopes

Gonçalves-Ribeiro

Sebastião

et al. 2023

Preprint

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“…The correlation filters approach is a powerful tool in digital signal processing [ 8 , 9 ]. This algorithm class utilizes the properties of Fourier transform of turning convolution in the spatial domain into function multiplication in the Fourier domain [ 10 – 13 ].…”

Section: Related Workmentioning

confidence: 99%

Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information

Shimizu

2020

Computational Intelligence and Neuroscience

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Object tracking is an important procedure in the computer vision field as it estimates the position, size, and state of an object along the video’s timeline. Although many algorithms were proposed with high accuracy, object tracking in diverse contexts is still a challenging problem. The paper presents some methods to track the movement of two types of objects: arbitrary objects and humans. Both problems estimate the state density function of an object using particle filters. For the videos of a static or relatively static camera, we adjusted the state transition model by integrating the movement direction of the object. Also, we propose that partitioning the object needs tracking. To track the human, we partitioned the human into N parts and, then, tracked each part. During tracking, if a part deviated from the object, it was corrected by centering rotation, and the part was, then, combined with other parts.

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“…Super-computing may now be possible with a desktop computer equipped with a high-end GPU [24]. Numerous solutions have been proposed recently for improving the speed of algorithms like cross-correlation with GPU supercomputing [25].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Information Trace in Local Field Potentials by a Computational Method of Two-Dimensional Time-Shifting Synchronization Likelihood Based on Graphic Processing Unit Acceleration

Zhao

Wan

2017

Neurosci. Bull.

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The local field potential (LFP) is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood (SL) is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit (GPU)-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes, delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals (like EEG and fMRI) using similar recording techniques.

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GPU implementation of cross-correlation for image generation in real time

Cited by 19 publications

References 5 publications

SIGAA – SIGnaling Automated Analysis: a new tool for Ca2+ signaling quantification using ratiometric Ca2+ dyes

SIGAA – SIGnaling Automated Analysis: a new tool for Ca2+ signaling quantification using ratiometric Ca2+ dyes

Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information

Mapping the Information Trace in Local Field Potentials by a Computational Method of Two-Dimensional Time-Shifting Synchronization Likelihood Based on Graphic Processing Unit Acceleration

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