OpenCV with Python

Gollapudi, Sunila

doi:10.1007/978-1-4842-4261-2_2

Cited by 35 publications

(19 citation statements)

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“…To quantify the enrichment of endocytic sites on nanoridges with high curvature, we developed automated image processing tools. Using the bright field image of a cell on a substrate, we employed a line detection algorithm to create a mask that discriminates between on-and-off ridge sections of the cell 23 . We next employed a Differences-of-Gaussians (DoG) algorithm to detect and localize fluorescent puncta 24 .…”

Section: Ormocomp Nanoridges Induce Membrane Curvature and Recruit The Endocytic Machinerymentioning

confidence: 99%

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Induced Nanoscale Membrane Curvature Bypasses Clathrin’s Essential Endocytic Function

Drubin

2021

Preprint

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During clathrin-mediated endocytosis (CME), flat plasma membrane is rapidly remodeled to produce a nanometer-scale spherical vesicle. The mechanisms underlying this shape change are not known, but it has been hypothesized that the clathrin coat stabilizes membrane curvature. Here, we used nanopatterning to produce glass-like substrates with U-shaped features mimicking membrane shapes induced by fibrillar materials such as collagen. These substrates bend the ventral plasma membranes of cells grown on them into shapes characteristic of the energetically-unfavorable U-shaped intermediate stage of CME. This induced plasma membrane curvature recruits the endocytic machinery, enhances endocytic site maturation, and partially bypasses clathrin's role in CME, supporting the conclusion that clathrin's essential endocytic function is to stabilize membrane curvature.

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Section: Ormocomp Nanoridges Induce Membrane Curvature and Recruit The Endocytic Machinerymentioning

confidence: 99%

“…Puncta of endocytic proteins were detected using Trackmate 6.0.3 in Differences-of-Gaussians mode 24 . Nanoridges were detected using custom Python scripts built on the Hough transform in Python package cv2 23 . TIRF videos were analyzed using the MATLAB cmeAnalysis package 26 .…”

Section: Computational Analysismentioning

confidence: 99%

Induced Nanoscale Membrane Curvature Bypasses Clathrin’s Essential Endocytic Function

Drubin

2021

Preprint

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“…For implementing the proposed experiment, Python programming language is used here on the google colaboratory pro version environment with 25GB GPU, and the name of the GPU is Tesla P100 [39]. As a python package, cv2 [40], numpy, pickle, tensorflow, keras, matplotlib used here where learning rates is 0.001, and optimizer is adam. The number of epochs and batch size are almost the same for all the experiments.…”

Section: Environmental Setup and Evaluation Metricsmentioning

confidence: 99%

Rotation, Translation and Scale Invariant Sign Word Recognition Using Deep Learning

Miah¹,

Shin²,

Hasan³

et al. 2023

Computer Systems Science and Engineering

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Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task. One of the main functions of sign language is to communicate with each other through hand gestures. Recognition of hand gestures has become an important challenge for the recognition of sign language. There are many existing models that can produce a good accuracy, but if the model test with rotated or translated images, they may face some difficulties to make good performance accuracy. To resolve these challenges of hand gesture recognition, we proposed a Rotation, Translation and Scale-invariant sign word recognition system using a convolutional neural network (CNN). We have followed three steps in our work: rotated, translated and scaled (RTS) version dataset generation, gesture segmentation, and sign word classification. Firstly, we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation, Translation and Scale of the original images to create the RTS version dataset. Then we have applied the gesture segmentation technique. The segmentation consists of three levels, i) Otsu Thresholding with YCbCr, ii) Morphological analysis: dilation through opening morphology and iii) Watershed algorithm. Finally, our designed CNN model has been trained to classify the hand gesture as well as the sign word. Our model has been evaluated using the twenty sign word dataset, five sign word dataset and the RTS version of these datasets. We achieved 99.30% accuracy from the twenty sign word dataset evaluation, 99.10% accuracy from the RTS version of the twenty sign word evolution, 100% accuracy from the five sign word dataset evaluation, and 98.00% accuracy from the RTS version five sign word dataset evolution. Furthermore, the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition.

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“…S1). The merging procedure was achieved using Python3 OpenCV 99 . During the appearance of the stimulus each .…”

Section: Experimental Design and Visual Stimulationmentioning

confidence: 99%

“…S1). The merging procedure was achieved using Python3 OpenCV 99 . During the appearance of the stimulus each frame was filtered using an automatic Canny edge detection algorithm (https://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-cannyedge-detection-with-python-and-opencv/), then the filtered cartoon was blended with the radial checkerboard.…”

Section: Experimental Design and Visual Stimulationmentioning

confidence: 99%

The amplitude of fNIRS hemodynamic response in the visual cortex unmasks autistic traits in typically developing children

Mazziotti

Scaffei

Conti

et al. 2021

Preprint

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Autistic traits represent a continuum dimension across the population, with autism spectrum disorder (ASD) being the extreme end of the distribution. Accumulating evidence shows that neuroanatomical and neurofunctional profiles described in relatives of ASD individuals reflect an intermediate neurobiological pattern between the clinical population and healthy controls. This suggests that quantitative measures detecting autistic traits in the general population represent potential candidates for the development of biomarkers identifying early pathophysiological processes associated with ASD. Functional near-infrared spectroscopy (fNIRS) has been extensively employed to investigate neural development and function. In contrast, the potential of fNIRS to define reliable biomarkers of brain activity has been barely explored. Features of non-invasiveness, portability, ease of administration and low-operating costs make fNIRS a suitable instrument to assess brain function for differential diagnosis, follow-up, analysis of treatment outcomes and personalized medicine in several neurological conditions. Here, we introduce a novel standardized procedure with high entertaining value to measure hemodynamic responses (HDR) in the occipital cortex of adult subjects and children. We found that the variability of evoked HDR correlates with the autistic traits of children, assessed by the Autism-Spectrum Quotient. Interestingly, HDR amplitude was especially linked to social and communication features, representing the core symptoms of ASD. These findings establish a quick and easy strategy for measuring visually-evoked cortical activity with fNIRS that optimize the compliance of young subjects, setting the background for testing the diagnostic value of fNIRS visual measurements in the ASD clinical population.

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OpenCV with Python

Cited by 35 publications

References 0 publications

Induced Nanoscale Membrane Curvature Bypasses Clathrin’s Essential Endocytic Function

Induced Nanoscale Membrane Curvature Bypasses Clathrin’s Essential Endocytic Function

Rotation, Translation and Scale Invariant Sign Word Recognition Using Deep Learning

The amplitude of fNIRS hemodynamic response in the visual cortex unmasks autistic traits in typically developing children

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