Search Results for "parallel image segmentation"
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15 projects for "parallel image segmentation" with 2 filters applied:
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MatImage
Image Processing library for Matlab
matImage is an open-source MATLAB library for image processing and analysis. It provides a variety of tools for image enhancement, segmentation, and feature extraction. It’s especially useful for users working on biomedical images or those needing detailed image analysis in MATLAB. -
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ComfyUI Essentials
Essential nodes that are weirdly missing from ComfyUI core
ComfyUI_essentials is a ComfyUI custom node collection that adds practical nodes the author considers missing from the ComfyUI core. The project focuses on useful workflow building blocks rather than generic duplicates, with nodes for image handling, mask processing, sampling, segmentation, conditioning, text, and miscellaneous operations. Its image tools include functions for batching, cropping, flipping, resizing, compositing, background removal, color matching, LUT application, sharpening, tiling, and latent previewing. Its mask tools include blur, smoothing, fixing, flipping, color-based masks, segmentation masks, bounding boxes, transition masks, and batch utilities. ... -
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PRDownloader
A file downloader library for Android with pause and resume support
...Supports proper request canceling. Many requests can be made in parallel. All types of customization are possible. -
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GenAI Processors
GenAI Processors is a lightweight Python library
GenAI Processors is a lightweight Python library for building modular, asynchronous, and composable AI pipelines around Gemini. Its central abstraction is the Processor, a unit of work that consumes an asynchronous stream of parts (text, images, audio, JSON) and produces another stream, making it natural to chain operations and keep everything streaming end-to-end. Processors can be composed sequentially (to build multi-step flows) or in parallel (to fan-out work and merge results), which... -
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Pixelization
Stable-diffusion-webui-pixelization
This is a specialized extension for the popular Stable Diffusion Web UI (AUTOMATIC1111) that focuses on converting or “pixelizing” images into a pixel-art aesthetic. It's designed as a plugin you install into the Web UI so that in the “Extras” or “Pixelization” tab you can drag in an input image and produce a stylized, block-based version with control over cell size, color depth, and segmentation. The extension uses pre-trained models and optionally can co-operate with the Web UI’s other features (image-to-image, prompt-based generation) so you can combine pixelization with generative workflows. For digital art, game assets, or retro aesthetic workflows, this offers a fast path from photo or high-res asset to stylized tiles or sprites. ... -
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Unet
Source code for unet-pytorch, which can train its own model
...Its README notes that U-Net is better suited to datasets with fewer features and shallow visual structures, such as medical image segmentation, rather than complex VOC-style scenes. It is useful for developers and students who want a clear U-Net implementation for segmentation experiments, custom masks, and biomedical-style image analysis. -
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jQuery & Zepto Lazy
A lightweight, fast, feature-rich, powerful and highly configurable
Lazy is a fast, feature-rich, and lightweight delayed content-loading plugin for jQuery and Zepto. It's designed to speed up page loading times and decrease traffic to your users by only loading the content in view. You can use Lazy in all vertical and horizontal scroll ways. It supports images in img / tags and backgrounds, supplied with CSS like background-image, by default. On those elements, Lazy can set a default image or a placeholder while loading and supports retina displays as well.... -
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SwiftOCR
Fast and simple OCR library written in Swift
SwiftOCR is a fast and simple OCR library written in Swift. It uses a neural network for image recognition. As of now, SwiftOCR is optimized for recognizing short, one-line long alphanumeric codes (e.g. DI4C9CM). We currently support iOS and OS X. If you want to recognize normal text like a poem or a news article, go with Tesseract, but if you want to recognize short, alphanumeric codes (e.g. gift cards), I would advise you to choose SwiftOCR because that's where it exceeds. Tesseract is... -
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ResNeXt
Implementation of a classification framework
ResNeXt is a deep neural network architecture for image classification built on the idea of aggregated residual transformations. Instead of simply increasing depth or width, ResNeXt introduces a new dimension called cardinality, which refers to the number of parallel transformation paths (i.e. the number of “branches”) that are aggregated together. Each branch is a small transformation (e.g. bottleneck block) and their outputs are summed—this enables richer representation without excessive parameter blowup. ... -
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FolioReaderKit
A Swift ePub reader and parser framework for iOS
...In the identity, the inspector set StoryboardFolioReaderContrainer as a class. Media Overlays (Sync text rendering with audio playback). TTS - Text to Speech Support, parse epub cover image, RTL Support. Vertical or/and Horizontal scrolling, share Custom Image Quotes NEW, supports multiple instances at same time, like parallel reading. -
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Tiramisu
Polyhedral compiler for expressing fast and portable data algorithms
Tiramisu is a compiler for expressing fast and portable data parallel computations. It provides a simple C++ API for expressing algorithms (Tiramisu expressions) and how these algorithms should be optimized by the compiler. Tiramisu can be used in areas such as linear and tensor algebra, deep learning, image processing, stencil computations and machine learning. The Tiramisu compiler is based on the polyhedral model thus it can express a large set of loop optimizations and data layout transformations. ... -
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PyCNN
Image Processing with Cellular Neural Networks in Python
Image Processing with Cellular Neural Networks in Python. Cellular Neural Networks (CNN) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. -
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CRFasRNN
Semantic image segmentation method described in the ICCV 2015 paper
CRF-RNN is a deep neural architecture that integrates fully connected Conditional Random Fields (CRFs) with Convolutional Neural Networks (CNNs) by reformulating mean-field CRF inference as a Recurrent Neural Network. This fusion enables end-to-end training via backpropagation for semantic image segmentation tasks, eliminating the need for separate, offline post-processing steps. Our work allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of objects. Currently we have trained this model to recognize 20 classes. This software allows you to test our algorithm on your own images – have a try and see if you can fool it, if you get some good examples you can send them to us. ... -
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Theba is a plugin-based image analysis framework for segmentation of and measurements on 3D and 2D images. Theba has a nice GUI that allows inspection and manipulation of the image and a wide range of plugins including segmentation.
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