Image Eraser allows users to perform image segmentation inside browser using a vector editor (FabricJS) and JS implementations of superpixel algorithms.
http://www.eraseimage.com/Tags | image-segmentation superpixel-algorithms computer-vision |
Implementation | Javascript |
License | MIT |
Platform | OS-Independent |
OpenCV (Open Source Computer Vision) is a library of programming functions for real time computer vision. The library has more than 500 optimized algorithms. It is used to interactive art, to mine inspection, stitching maps on the web on through advanced robotics.
image-processing visualization library 3d-image-processing 3d image-analysisA python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.
artificial-intelligence machine-learning prediction image-prediction python3 offline-capable imageai artificial-neural-networks algorithm image-recognition object-detection squeezenet densenet video inceptionv3 detection gpu ai-practice-recommendationsCVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Code released under the MIT License.
video-annotation computer-vision computer-vision-annotation deep-learning image-annotation annotation-tool annotation labeling labeling-tool image-labeling image-labelling-tool bounding-boxes boundingbox image-classification annotations imagenet detection recognition tensorflowWe present network definition and weights for our second place solution in CVPR 2018 DeepGlobe Building Extraction Challenge. Automatic building detection in urban areas is an important task that creates new opportunities for large scale urban planning and population monitoring. In a CVPR 2018 Deepglobe Building Extraction Challenge participants were asked to create algorithms that would be able to perform binary instance segmentation of the building footprints from satellite imagery. Our team finished second and in this work we share the description of our approach, network weights and code that is sufficient for inference.
satellite-imagery computer-vision image-segmentation deep-learning pytorchHere we present our wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge. In this work, we describe our winning solution for MICCAI 2017 Endoscopic Vision Sub-Challenge: Robotic Instrument Segmentation and demonstrate further improvement over that result. Our approach is originally based on U-Net network architecture that we improved using state-of-the-art semantic segmentation neural networks known as LinkNet and TernausNet. Our results shows superior performance for a binary as well as for multi-class robotic instrument segmentation. We believe that our methods can lay a good foundation for the tracking and pose estimation in the vicinity of surgical scenes.
medical-imaging robot-assisted-surgery computer-vision image-segmentation deep-learning pytorchSOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.
computer-vision library deep-learning image-processing object-detection cpu real-time convolutional-neural-networks recurrent-neural-networks face-detection facial-landmarks machine-learning-algorithms image-recognition image-analysis vision-framework embedded detection iot-device iotThis repository contains a Torch implementation for both the DeepMask and SharpMask object proposal algorithms. DeepMask is trained with two objectives: given an image patch, one branch of the model outputs a class-agnostic segmentation mask, while the other branch outputs how likely the patch is to contain an object. At test time, DeepMask is applied densely to an image and generates a set of object masks, each with a corresponding objectness score. These masks densely cover the objects in an image and can be used as a first step for object detection and other tasks in computer vision.
Labelme is a graphical image annotation tool inspired by http://labelme.csail.mit.edu. It is written in Python and uses Qt for its graphical interface. Fig 2. VOC dataset example of instance segmentation.
image-annotation computer-vision annotations deep-learning semantic-segmentation instance-segmentation video-annotation classificationThe Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.
machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpegLabelbox is a data labeling tool that's purpose built for machine learning applications. Start labeling data in minutes using pre-made labeling interfaces, or create your own pluggable interface to suit the needs of your data labeling task. Labelbox is lightweight for single users or small teams and scales up to support large teams and massive data sets. Simple image labeling: Labelbox makes it quick and easy to do basic image classification or segmentation tasks. To get started, simply upload your data or a CSV file containing URLs pointing to your data hosted on a server, select a labeling interface, (optional) invite collaborators and start labeling.
image-classification image-segmentation computer-vision tensorflow labeling annotations deep-learning recognition tools image-annotationPytorch implementation of our method for adapting semantic segmentation from the synthetic dataset (source domain) to the real dataset (target domain). Based on this implementation, our result is ranked 3rd in the VisDA Challenge. Learning to Adapt Structured Output Space for Semantic Segmentation Yi-Hsuan Tsai*, Wei-Chih Hung*, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang and Manmohan Chandraker IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (spotlight) (* indicates equal contribution).
deep-learning computer-vision domain-adaptation semantic-segmentation generative-adversarial-network adversarial-learning pytorchThe VLFeat open source library implements popular computer vision algorithms specialising in image understanding and local featurexs extraction and matching. Algorithms incldue Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixes, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. VLFeat is distributed under the BSD license (see the COPYING file).
AliceVision is a Photogrammetric Computer Vision Framework which provides a 3D Reconstruction and Camera Tracking algorithms. AliceVision aims to provide strong software basis with state-of-the-art computer vision algorithms that can be tested, analyzed and reused. The project is a result of collaboration between academia and industry to provide cutting-edge algorithms with the robustness and the quality required for production usage. Learn more details about the pipeline and tools based on it on AliceVision website.
computer-vision 3d-reconstruction photogrammetry structure-from-motion camera-trackingGluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.
deep-learning computer-vision neural-network gluon mxnet machine-learning image-classification object-detection semantic-segmentationWelcome to our training guide for inference and deep vision runtime library for NVIDIA DIGITS and Jetson Xavier/TX1/TX2. This repo uses NVIDIA TensorRT for efficiently deploying neural networks onto the embedded platform, improving performance and power efficiency using graph optimizations, kernel fusion, and half-precision FP16 on the Jetson.
deep-learning inference computer-vision embedded image-recognition object-detection segmentation jetson jetson-tx1 jetson-tx2Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos. The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for motion segmentation in videos, but it can be also used (or adapted) for other computer vision problems (for more information, please see this page). Currently the LRSLibrary offers more than 100 algorithms based on matrix and tensor methods. The LRSLibrary was tested successfully in several MATLAB versions (e.g. R2014, R2015, R2016, R2017, on both x86 and x64 versions). It requires minimum R2014b.
rpca matrix-factorization matrix-completion tensor-decomposition tensor matlab matrix subspace-tracking subspace-learningLightNet provides a simple and efficient Python interface to DarkNet, a neural network library written by Joseph Redmon that's well known for its state-of-the-art object detection models, YOLO and YOLOv2. LightNet's main purpose for now is to power Prodigy's upcoming object detection and image segmentation features. However, it may be useful to anyone interested in the DarkNet library. Once you've downloaded LightNet, you can install a model using the lightnet download command. This will save the models in the lightnet/data directory. If you've installed LightNet system-wide, make sure to run the command as administrator.
machine-learning computer-vision neural-network neural-networks object-detection darknet-image-classification image-classification ai artificial-intelligence cython cython-wrapper yoloLTI-Lib is an object oriented computer vision library written in C++ for Windows/MS-VC++ and Linux/gcc. It provides lots of functionality to solve mathematical problems, many image processing algorithms, some classification tools and much more...
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
pytorch data-augmentation kaggle-competition kaggle deep-learning computer-vision keras neural-networks neural-network-example transfer-learning
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