The Colour Contrast Analyser (CCA) helps you determine the legibility of text and the contrast of visual elements, such as graphical controls and visual indicators.

An implementation of Deep Canonical Correlation Analysis (DCCA or Deep CCA) with pytorch.

An implementation of Deep Canonical Correlation Analysis (DCCA or Deep CCA) with Keras.

Context Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark

The Colour Contrast Analyser (CCA) helps you determine the legibility of text and the contrast of visual elements, such as graphical controls and visual indicators. The macOS version was developed by Cédric Trévisan based on the Windows version developed by Jun in collaboration with Steve Faulkner.

The main objective of this project is to identify overspeed vehicles, using Deep Learning and Machine Learning Algorithms. After acquisition of series of images from the video, trucks are detected using Haar Cascade Classifier. The model for the classifier is trained using lots of positive and negative images to make an XML file. This is followed by tracking down the vehicles and estimating their speeds with the help of their respective locations, ppm (pixels per meter) and fps (frames per second). Now, the cropped images of the identified trucks are sent for License Plate detection. The CCA (Connected Component Analysis) assists in Number Plate detection and Characters Segmentation. The SVC model is trained using characters images (20X20) and to increase the accuracy, 4 cross fold validation (Machine Learning) is also done. This model aids in recognizing the segmented characters. After recognition, the calculated speed of the trucks is fed into an excel sheet along with their license plate numbers. These trucks are also assigned some IDs to generate a systematized database.

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

The Colour Contrast Analyser (CCA) helps you determine the legibility of text and the contrast of visual elements, such as graphical controls and visual indicators.

A clone of Castro's playback controls.

Reference implementation of Arm-CCA RMM specification

CCAs for looking into DNNs

Feature fusion using Canonical Correlation Analysis (CCA)

💰 Accept cards & crypto. (Abandoned)

Command line interface to convert strings into any case.

Cloudera_Material: Study Material to help people preparing for Cloudera CCA Spark and Hadoop Developer Exam (CCA175). Feel free to collaborate.

Latest promos, discounts or coupons for Linux Foundation & CNCF certifications (CKA, CKAD, CKS, LFCS, CNPE, KCNA, KCSA, PCA, ICA, CCA, CAPA, CGOA, CBA, OTCA, KCA, CNPA) training & certification bundles

A Julia package for advanced Matrix Diagonalization algorithms (PCA, Whitening, MCA, gMCA, CCA, gCCA, CSP, CSTP, AJD, mAJD)

CCA Digital Archives Processing Manual

Local integration for Tigo CCA that provides real-time and historical solar panel data directly via your local network, without using the cloud.

Deep Multiset Canonical Correlation Analysis - An extension of CCA to multiple datasets

Home Assistant custom component for local access to Tigo CCA

an external ledger for in-game economies, running on a webserver with a RESTful API, exceptionally fast and lightweight written in C++

swebench repro script for running confucius-code-agent (CCA)

Canonical Correlation Analysis Zoo: CCA, GCCA, MCCA, DCCA, DGCCA, DVCCA, DCCAE, KCCA and regularised variants

Code for the paper "End-to-end training of deep probabilistic CCA on paired biomedical observations".

Canonical Correlation Analysis, Variational CCA

Deep & Sparse CCA for Neuroimaging in Tensorflow

Implementation of Fast ml-CCA from the ICCV-2015 work "Multi-Label Cross-Modal Retrieval"

Matlab code for our CCA 2016 paper "Fast Algorithms for UAV Tasking and Routing"

CCA, DCCA, DCCAE, ConvCCA