Edge co-occurrences can account for rapid categorization of natural versus animal images
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Updated
Jan 9, 2020 - Jupyter Notebook
Edge co-occurrences can account for rapid categorization of natural versus animal images
Material and ms for the paper caracterizing sparseness of coefficients in natural images
Explore deep learning-powered image classification with PyTorch. Achieved 98% accuracy on Natural Images and 95% on Birds Species using AlexNet and EfficientNet-B1. Dive into the code and results!
A framework to build a sparse representation of edges in images.
In this repository, we deal with the task of calculating the principal components of natural images and video frame compression bitrate analysis.
Playing around with generative models trained on natural images, along with (eventually) some visualization tools.
Website for "Model-based stimulus synthesis of natural-like random textures for the study of motion perception"
Simple and extensible GAN image-to-image translation framework. Supports natural and medical images.
Source code for the paper "An Adaptive Affinity Graph with Subspace Pursuit for Natural Image Segmentation".
A list of papers and datasets about natural/color image segmentation (processing)
Source code for the paper "Affinity Fusion Graph-based Framework for Natural Image Segmentation".
python package to access the data of the NSD (natural scenes dataset) fMRI project
Explored CNNs with TensorFlow to create models for cropped single-digit and original multi-digit images from SVHN dataset.
image registration related books, papers, videos, and toolboxes
A curated list of resources dedicated to scene text localization and recognition
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