Hybrid FPGA-CPU pupil tracker

Kowalski, Bartlomiej; Huang, Xiaojing; Steven, Samuel; Dubra, Alfredo

doi:10.1364/boe.433766

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…In order to adapt to the changes in the imaging position, the cross-correlation methods need to manually change the reference. In addition to postprocessing methods, some imaging systems [23][24][25][26][27][28][29] are capable of compensating for movement during the imaging process, but special hardware equipment with corresponding optics needs to be integrated into the imaging system.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Image Stabilization for Adaptive Optics Ophthalmoscopy

Liu

et al. 2022

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An adaptive optics scanning laser ophthalmoscope (AOSLO) has the characteristics of a high resolution and a small field of view (FOV), which are greatly affected by eye motion. Continual eye motion will cause distortions both within the frame (intra-frame) and between frames (inter-frame). Overcoming eye motion and achieving image stabilization is the first step and is of great importance in image analysis. Although cross-correlation-based methods enable image registration to be achieved, the manual identification and distinguishing of images with saccades is required; manual registration has a high accuracy, but it is time-consuming and complicated. Some imaging systems are able to compensate for eye motion during the imaging process, but special hardware devices need to be integrated into the system. In this paper, we proposed a deep-learning-based algorithm for automatic image stabilization. The algorithm used the VGG-16 network to extract convolution features and a correlation filter to detect the position of reference in the next frame, and finally, it compensated for displacement to achieve registration. According to the results, the mean difference in the vertical and horizontal displacement between the algorithm and manual registration was 0.07 pixels and 0.16 pixels, respectively, with a 95% confidence interval of (−3.26 px, 3.40 px) and (−4.99 px, 5.30 px). The Pearson correlation coefficients for the vertical and horizontal displacements between these two methods were 0.99 and 0.99, respectively. Compared with cross-correlation-based methods, the algorithm had a higher accuracy, automatically removed images with blinks, and corrected images with saccades. Compared with manual registration, the algorithm enabled manual registration accuracy to be achieved without manual intervention.

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Section: Introductionmentioning

confidence: 99%

Deep-Learning Image Stabilization for Adaptive Optics Ophthalmoscopy

Liu

et al. 2022

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Asymmetric Activation of Retinal ON and OFF Pathways by AOSLO Raster-Scanned Visual Stimuli

Patterson,

Cai,

Yang

et al. 2024

Preprint

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Adaptive optics scanning light ophthalmoscopy (AOSLO) enables high-resolution retinal imaging, eye tracking, and stimulus delivery in the living eye. AOSLO-mediated visual stimuli are created by temporally modulating the excitation light as it scans across the retina. As a result, each location within the field of view receives a brief flash of light during each scanner cycle (every 33-40 ms). Here we usedin vivocalcium imaging with AOSLO to investigate the impact of this intermittent stimulation on the retinal ON and OFF pathways. Raster-scanned backgrounds exaggerated existing ON-OFF pathway asymmetries leading to high baseline activity in ON cells and increased response rectification in OFF cells.

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Embedded CPU-GPU pupil tracking

Kowalski,

Huang,

Dubra

2024

Biomed. Opt. Express

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We explore camera-based pupil tracking using high-level programming in computing platforms with end-user discrete and integrated central processing units (CPUs) and graphics processing units (GPUs), seeking low calculation latencies previously achieved with specialized hardware and programming (Kowalski et al., [Biomed. Opt. Express 12, 6496 (2021)10.1364/BOE.433766]. Various desktop and embedded computers were tested, some with two operating systems, using the traditional sequential pupil tracking paradigm, in which the processing of the camera image only starts after it is fully downloaded to the computer. The pupil tracking was demonstrated using two Scheimpflug optical setups, telecentric in both image and object spaces, with different optical magnifications and nominal diffraction-limited performance over an ∼18 mm full field of view illuminated with 940 nm light. Eye images from subjects with different iris and skin pigmentation captured at this wavelength suggest that the proposed pupil tracking does not suffer from ethnic bias. The optical axis of the setups is tilted at 45° to facilitate integration with other instruments without the need for beam splitting. Tracking with ∼0.9-4.4 µm precision and safe light levels was demonstrated using two complementary metal-oxide-semiconductor cameras with global shutter, operating at 438 and 1,045 fps with an ∼500 × 420 pixel region of interest (ROI), and at 633 and 1,897 fps with ∼315 × 280 pixel ROI. For these image sizes, the desktop computers achieved calculation times as low as 0.5 ms, while low-cost embedded computers delivered calculation times in the 0.8-1.3 ms range.

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Hybrid FPGA-CPU pupil tracker

Cited by 4 publications

References 36 publications

Deep-Learning Image Stabilization for Adaptive Optics Ophthalmoscopy

Deep-Learning Image Stabilization for Adaptive Optics Ophthalmoscopy

Asymmetric Activation of Retinal ON and OFF Pathways by AOSLO Raster-Scanned Visual Stimuli

Embedded CPU-GPU pupil tracking

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