166 Open source projects that are alternatives of or similar to SIFT-BoF

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

🎒 Bag of Visual words (BoW) approach for object classification and detection in images together with SIFT feature extractor and SVM classifier.

ezSIFT: An easy-to-use standalone SIFT library written in C/C++

💎 "Marker-less Augmented Reality" with OpenCV and OpenGL.

The hottest High Dynamic Range (HDR) Library

Artificial intelligence/machine learning course at UCF in Spring 2020 (Fall 2019 and Spring 2019)

No description or website provided.

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

Radiomics (here mainly means hand-crafted based radiomics) contains data acquire, ROI segmentation, feature extraction, feature selection, machine learning modeling, and stastical analysis.

Fast and simple music and audio analysis using RNN in Python 🕵️‍♀️ 🥁

A collection of non-residential buildings for performance analysis and algorithm benchmarking

An intuitive library to extract features from time series

MODS (Matching On Demand with view Synthesis) is algorithm for wide-baseline matching.

Android app analysis and feature extraction library

Extract video features from raw videos using multiple GPUs. We support RAFT and PWC flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, ResNet features.

Direct Graphical Models (DGM) C++ library, a cross-platform Conditional Random Fields library, which is optimized for parallel computing and includes modules for feature extraction, classification and visualization.

Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland. - Mirrored from https://gitlab.idiap.ch/bob/bob

🎨 Color recognition & classification & detection on webcam stream / on video / on single image using K-Nearest Neighbors (KNN) is trained with color histogram features by OpenCV.

Golang Utilities for Data Analysis

Fingerprint recognition engine for Java that takes a pair of human fingerprint images and returns their similarity score. Supports efficient 1:N search.

Efficient similarity search and clustering for Ruby

Python implementation of "Elliptic Fourier Features of a Closed Contour"

Code examples for training AlexNet using Keras and Theano

Stock for Deep Learning and Machine Learning

Data transformations for the ML era

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

⭐ A Python package for optimal 1D k-means clustering.

Time series features

Animated Visualizations of Popular Machine Learning Algorithms

Implementation of VLAE

machine learning algorithms in Swift

Convenient wrapper for TensorFlow feature extraction from pre-trained models using tf.contrib.slim

feature detector and descriptor for spherical panorama

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

文本聚类（Kmeans、DBSCAN、LDA、Single-pass）

Automated feature extraction in Python

A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018).

B-SHOT : A Binary Feature Descriptor for Fast and Efficient Keypoint Matching on 3D Point Clouds

R/shiny interface for interactive visualization of data in SummarizedExperiment objects

Image cluster 图像聚类

.NET library for 1D signal processing focused specifically on audio processing

Extract deep learning features from images using simple python interface

👷‍♂️ A simple package for extracting useful features from character objects 👷‍♀️

Flexible time series feature extraction & processing

🏗 Statistical models for biomolecular dynamics 🏗

Diabetic Retinopathy is a very common eye disease in people having diabetes. This disease can lead to blindness if not taken care of in early stages, This project is a part of the whole process of identifying Diabetic Retinopathy in its early stages. In this project, we'll extract basic features which can help us in identifying Diabetic Retinopa…

A C++ Qt GUI desktop program to calculate Harris, FAST, SIFT and SURF image features with OpenCV

Cukatify is a music social media project

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

This repository explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply…

特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现

📖 This guide is to help you understand the basics of the computerized image and develop computer vision projects with OpenCV. Includes Python, Java, JavaScript, C# and C++ examples.

Deep Learning: Image classification, feature visualization and transfer learning with Keras

Different clustering approaches applied on different problemsets

📢 Ready to learn or review your knowledge!

USING BERT FOR Attribute Extraction in KnowledgeGraph. fine-tuning and feature extraction. 使用基于bert的微调和特征提取方法来进行知识图谱百度百科人物词条属性抽取。

Super fast simple k-means implementation for unidimiensional and multidimensional data.

Graph-based framework for text classification

Audio feature extraction and classification