Search Results for "windows command code"
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Showing 48 open source projects for "windows command code"
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Image Fusion
Deep Learning-based Image Fusion: A Survey
This repository is a survey / code collection centered on deep learning–based image fusion (e.g. fusing infrared + visible light images, multi-modal fusion) methods. It catalogs many fusion algorithms (e.g. DenseFuse, FusionGAN, NestFuse, etc.), links to code implementations, and describes evaluation metrics. The repository includes a “General Evaluation Metric” subfolder containing objective fusion metrics. It is not a single monolithic tool, but rather a curated reference and aggregation... -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads:... -
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Exposure Correction
Learning multi-scale deep model correcting over- and under- exposed
Exposure_Correction is a research project that provides the implementation for the paper Learning Multi-Scale Photo Exposure Correction (CVPR 2021). The repository focuses on correcting poorly exposed photographs, handling both underexposure and overexposure using a deep learning approach. The method employs a multi-scale framework that learns to enhance images by adjusting exposure levels across different spatial resolutions. This allows the model to preserve fine details while correcting... -
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mTRF-Toolbox
A MATLAB package for modelling multivariate stimulus-response data
mTRF-Toolbox is a MATLAB package for modelling multivariate stimulus-response data, suitable for neurophysiological data such as MEG, EEG, sEEG, ECoG and EMG. It can be used to model the functional relationship between neuronal populations and dynamic sensory inputs such as natural scenes and sounds, or build neural decoders for reconstructing stimulus features and developing real-time applications such as brain-computer interfaces (BCIs). Toolbox Paper: ... -
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Source code from the Research Institute for Signals, Systems and Computational Intelligence http://fich.unl.edu.ar/sinc
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MTCNN Face Detection Alignment
Joint Face Detection and Alignment
MTCNN_face_detection_alignment is an implementation of the “Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks” algorithm. The algorithm uses a cascade of three convolutional networks (P-Net, R-Net, O-Net) to jointly detect faces (bounding boxes) and align facial landmarks in a coarse-to-fine manner, leveraging multi-task learning. Non-maximum suppression and bounding box regression at each stage. The repository includes Caffe / MATLAB code, support scripts,... -
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Spheroid_segmentation
Deep learning networks for spheroid segmentation
To accelerate the analysis of tumors' spheroids, different deep learning networks were trained to automatize the segmentation process. The code provides the trained networks based on Vgg16, Vgg19, ResNet18, and ResNet50 ready to be used for segmentation purposes. It also provides Matlab functions ready to be used to train new networks, segment new images, and measure the quality of the training using different quantitative parameters. -
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DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN
This repository implements DnCNN (“Deep CNN Denoiser”) from the paper “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising”. DnCNN is a feedforward convolutional neural network that learns to predict the residual noise (i.e. noise map) from a noisy input image, which is then subtracted to yield a clean image. This formulation allows efficient denoising, supports blind Gaussian noise (i.e. unknown noise levels), and can be extended to related tasks like image... -
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CAM
Class Activation Mapping
This repository implements Class Activation Mapping (CAM), a technique to expose the implicit attention of convolutional neural networks by generating heatmaps that highlight the most discriminative image regions influencing a network’s class prediction. The method involves modifying a CNN model slightly (e.g., using global average pooling before the final layer) to produce a weighted combination of feature maps as the class activation map. Integration with existing CNNs (with light... -
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This Matlab code is used for demonstration of the effect of CDF-TS as a preprocessing method to transform data. Written by Ye Zhu, Deakin University, April 2021, version 1.0. This software is under GNU General Public License version 3.0 (GPLv3) This code is a demo of method described by the following publication: Zhu, Y., Ting, K.M., Carman, M. and Angelova, M., 2021, April. CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern...
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ANDTool
Analysis Nuclei DAB (AND) Tool
Analysis Nuclei DAB (AND) Tool is a Graphical User Interface (GUI) to analyse microscopy images representing cells with nuclei stained using DAB dyes. The tool requires as input the original RGB images, and the FastRed, FastBlue, DAB channel, easily obtained using the Fiji function: "ImageJ" -> "Image" -> "Colour Deconvolution" -> "FastRed FastBlue DAB" Then, the tool first segment the nuclei using the FastBlue channel and the DAB channel, and then computes statistics by subdividing... -
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Isolation‐based anomaly detection
Isolation‐based anomaly detection using nearest‐neighbor ensembles
This site provides the source code of Isolation‐based anomaly detection (iNNE). https://onlinelibrary.wiley.com/doi/abs/10.1111/coin.12156 Bandaragoda, T.R., Ting, K.M., Albrecht, D., Liu, F.T., Zhu, Y. and Wells, J.R., 2018. Isolation‐based anomaly detection using nearest‐neighbor ensembles. Computational Intelligence, 34(4), pp.968-998. -
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Isolation Similarity
aNNE similarity based on Isolation Kernel
Demo of using aNNE similarity for DBSCAN. Written by Xiaoyu Qin, Monash University, March 2019, version 1.0 This software is under GNU General Public License version 3.0 (GPLv3) This code is a demo of method described by the following publication: Qin, X., Ting, K.M., Zhu, Y. and Lee, V.C., 2019, July. Nearest-neighbour-induced isolation similarity and its impact on density-based clustering. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp.... -
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Machine Learning Homework
Matlab Coding homework for Machine Learning
The Machine-Learning-homework repository by user “Ayatans” is a collection of MATLAB code intended to solve or illustrate assignments in machine learning courses. It includes implementations of standard machine learning algorithms (such as regression, classification, etc.), scripts for data loading and preprocessing, and evaluation routines (e.g. accuracy, error metrics). Because it is structured as homework or practice material, the code is likely intended more for didactic use than for... -
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Tiny
Tiny Face Detector, CVPR 2017
This repository implements the Tiny Face Detector (from Hu & Ramanan, CVPR 2017) in MATLAB (using MatConvNet). The method is designed to detect tiny faces (i.e. very small-scale faces) by combining multi-scale context modeling, foveal descriptors, and scale enumeration strategies. It provides training/testing scripts, a demo (tiny_face_detector.m), model loading, evaluation on WIDER FACE, and supporting utilities (e.g. cnn_widerface_eval.m). The code depends on MatConvNet, which must be... -
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Coursera Machine Learning
Coursera Machine Learning By Prof. Andrew Ng
CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes... -
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Activity Recognition
Resources about activity recognition
This repository is a curated collection of resources, papers, code, and summaries relating to human activity recognition/behavior recognition. It is not a single integrated software package but rather a knowledge base organizing feature extraction methods, deep learning approaches, transfer learning strategies, datasets, and representative research in behavior recognition. The repository includes links to code in MATLAB, Python, summaries of algorithms, datasets, and relevant research... -
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OpenFace
A state-of-the-art facial behavior analysis toolkit
OpenFace is an advanced facial behavior analysis toolkit intended for computer vision and machine learning researchers, those in the affective computing community, and those who are simply interested in creating interactive applications based on facial behavior analysis. The OpenFace toolkit is capable of performing several complex facial analysis tasks, including facial landmark detection, eye-gaze estimation, head pose estimation and facial action unit recognition. OpenFace is able to... -
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ConvNet Burden
Memory consumption and FLOP count estimates for convnets
convnet-burden is a MATLAB toolbox / script collection estimating computational cost (FLOPs) and memory consumption of various convolutional neural network architectures. It lets users compute approximate burdens (in FLOPs, memory) for standard image classification CNN models (e.g. ResNet, VGG) based on network definitions. The tool helps researchers compare the computational efficiency of architectures or quantify resource needs. Estimation of memory consumption (e.g. feature map sizes,... -
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OpenCE
Contrast Enhancement Techniques for low-light images
OpenCE is an open source implementation of the paper Cascaded Pyramid Network for Multi-Person Pose Estimation (CVPR 2018) by Yilun Chen, Zhicheng Wang, Yuxiang Peng, Zhiqiang Zhang, Gang Yu, and Jian Sun. The framework provides a complete training and evaluation pipeline for human pose estimation using a cascaded pyramid network (CPN). OpenCE leverages a feature pyramid structure combined with a refinement stage to improve keypoint detection accuracy across multiple scales, particularly for... -
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CFNet
Training a Correlation Filter end-to-end allows lightweight networks
CFNet is the official implementation of End-to-end representation learning for Correlation Filter based tracking (CVPR 2017) by Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, and Philip H. S. Torr. The framework combines correlation filters with deep convolutional neural networks to create an efficient and accurate visual object tracker. Unlike traditional correlation filter trackers that rely on hand-crafted features, CFNet learns feature representations directly from... -
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MatlabFunc
Matlab codes for feature learning
MatlabFunc is a collection of MATLAB functions developed by the ZJULearning group to support various tasks in computer vision, machine learning, and numerical computation. The repository brings together a wide range of utility scripts, algorithms, and implementations that serve as building blocks for research and development. These functions cover areas such as matrix operations, optimization, data processing, and visualization, making them broadly applicable across different research... -
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Detect and Track
Code release for "Detect to Track and Track to Detect", ICCV 2017
Detect-Track is the official implementation of the ICCV 2017 paper Detect to Track and Track to Detect by Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman. The framework unifies object detection and tracking into a single pipeline, allowing detection to support tracking and tracking to enhance detection performance. Built upon a modified version of R-FCN, the code provides implementations using backbone networks such as ResNet-50, ResNet-101, ResNeXt-101, and Inception-v4, with... -
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ECO
Matlab implementation of the ECO tracker
ECO (Efficient Convolution Operators for Tracking) is a high-performance object tracking algorithm developed by Martin Danelljan and collaborators. It is based on discriminative correlation filters and designed to handle appearance changes, occlusions, and scale variations in visual object tracking tasks. The code provides a MATLAB implementation of the ECO and ECO-HC (high-speed) variants and was one of the top performers on multiple visual tracking benchmarks. -
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HashingBaselineForImageRetrieval
Various hashing methods for image retrieval and serves as the baseline
This repository provides baseline implementations of deep supervised hashing methods for image retrieval tasks using PyTorch. It includes clean, minimal code for several hashing algorithms designed to map images into compact binary codes while preserving similarity in feature space, enabling fast and scalable retrieval from large image datasets.
