features-extraction

Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders.

EntroPy: complexity of time-series in Python (DEPRECATED)

Kaldi-compatible feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd - Provide C++ & Python API

Deep learning methods for feature selection in gene expression autism data.

Repo of paper title 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'

An in-depth tutorial on the theory of panorama stitching

Problem Statement: Given the tweets from customers about various tech firms who manufacture and sell mobiles, computers, laptops, etc, the task is to identify if the tweets have a negative sentiment towards such companies or products.

Cloud-Assisted Multi-View Video Summarization using CNN and Bi-Directional LSTM

Learnable STRF, from Riad et al. 2021 JASA

Awesome papers on Feature Extraction (Dimensionality Reduction)

Codes for the paper "A Robust Of-line Writer Identification Method" in ACTA AUTOMATICA SINICA, 2020

FeatTS is a Semi-Supervised Clustering method that leverages features extracted from the raw time series to create clusters that reflect the original time series.

The project addresses automatic detection of microaneurysms (MA) which are first detectable changes in Diabetic Retinopathy (DR). Green channel, being the most contrasted channel, of the color fundus images are considered. The algorithm includes pre-processing, MA candidates detection, features extraction, classification and comparison with ground truth to evaluate the performance of classifier model.

Spatio-temporal features extraction that measure the stabilty. The proposed method is based on a compression algorithm named Run Length Encoding. The workflow of the method is presented bellow.

LibSA4Py: Light-weight static analysis for extracting type hints and features

Using Disparity Map and Optical Flow info to extract features from the detected objects from maskRCNN.

The objective of this work is to detect the cell phone and/or camera used by a person in restricted areas. The paper is based on intensive image processing techniques, such as, features extraction and image classification. The dataset of images is generated with cell phone camera including positive (with cell phone) and negative (without cell phone) images. We then extract relevant features by using classical features extraction techniques including Histogram of Oriented Gradients (HOG) and Speeded up Robust Features (SURF).The extracted features are then, passed to classifier for detection. We employ Support Vector Machine (SVM), Nearest Neighbor (K-NN) and Decision tree classifier which are already trained on our dataset of training images of persons using mobile or otherwise. Finally, the detection performance in terms of error rate is compared for various combinations of feature extraction and classification techniques. Our results show that SURF with SVM classifier gives the best accuracy.

[IN PROGRESS] Multimodal feature extraction modules for ease of doing research and reproducibility.

automatic pipeline based on MRI segmentation and features extraction

Conversion of depth data to derived images that allow classical feature detection. My master thesis.

Thesis: "Autocalibration of monocular cameras for autonomous driving scenarios", carried out in collaboration with Luxoft. This repository contains all the related code and the two implemented solution pipelines for Structure from Motion (SfM) implementation.

Little addon for Feature Detection and Image Tracking using OPENCV 3.4 and openFrameworks

Classifying a person's actions (opening right hand, opening left hand, opening both hands, closing both hands) based on real motor imagery electroencephalogram (EEG) data on an STM32 microcontroller and the Gapuino board using Python and C++, achieved classification accuracy of 40%

Time series features creation

This repository contains Python scripts for extracting linguistic features from Filipino texts.

Generalized Time Series Decomposition (GTSD) is an experimental method of generating and using large recurrence plots to self identify and segment away features of an unknown time series.

Using transfer learning to classify tools for a ColRobot application