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6 रिपॉजिटरी

Awesome GitHub RepositoriesConnected Component Identification

Algorithms for identifying and counting contiguous regions of identical values in a grid.

Distinct from Grid Connectivity Solving: Focuses on simple connectivity and component counting using DFS, whereas Grid Connectivity Solving focuses on plug-based DP for tiling.

Explore 6 awesome GitHub repositories matching software engineering & architecture · Connected Component Identification. Refine with filters or upvote what's useful.

Awesome Connected Component Identification GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • azl397985856/leetcodeazl397985856 का अवतार

    azl397985856/leetcode

    55,758GitHub पर देखें↗

    This project is a curated educational resource and solution repository for algorithmic challenges, specifically focused on LeetCode problems. It serves as a technical reference for common data structures and algorithmic patterns, providing verified code implementations across multiple programming languages alongside detailed logic and complexity analysis. The repository functions as a comprehensive study guide for competitive programming and technical interview preparation. It includes specialized learning tools such as an Anki flashcard dataset for spaced repetition and a browser extension t

    Implements depth-first search to identify and count connected components within 2D grids.

    JavaScriptalgoalgorithmalgorithms
    GitHub पर देखें↗55,758
  • hybridgroup/gocvhybridgroup का अवतार

    hybridgroup/gocv

    7,463GitHub पर देखें↗

    GoCV is a computer vision library and Go language binding for OpenCV. It serves as an image processing toolkit and deep learning inference engine, providing programmatic access to a wide range of algorithms for image manipulation, object detection, and video analysis. The project differentiates itself through high-performance native bindings and hardware acceleration. It utilizes a foreign function interface to map Go calls to C++ functions and includes a hardware-agnostic backend dispatch to route neural network tasks to computation engines such as CUDA and OpenVINO. The library covers a br

    Labels and counts connected regions of pixels to separate distinct objects in binary images.

    Go
    GitHub पर देखें↗7,463
  • shimat/opencvsharpshimat का अवतार

    shimat/opencvsharp

    6,011GitHub पर देखें↗

    OpenCVSharp is a .NET library that wraps native OpenCV functions, providing C# developers with access to OpenCV's computer vision capabilities through an API that mirrors the native C/C++ style. It serves as a managed wrapper for image processing, feature detection, object detection, and image manipulation tasks, while also handling automatic disposal of unmanaged OpenCV resources like Mat objects to prevent memory leaks in .NET applications. The library enables keypoint detection and descriptor extraction using algorithms such as AKAZE, BRISK, or FAST, with brute-force or FLANN-based matchin

    Labels connected regions of foreground pixels in binary images and returns blob properties.

    C#computer-visiondotnetdotnetstandard
    GitHub पर देखें↗6,011
  • garnele007/swiftocrgarnele007 का अवतार

    garnele007/SwiftOCR

    4,632GitHub पर देखें↗

    SwiftOCR एक नेटिव Swift लाइब्रेरी है जिसे छवियों से टेक्स्ट और अल्फ़ान्यूमेरिक अक्षरों को निकालने के लिए डिज़ाइन किया गया है। यह एक न्यूरल नेटवर्क टेक्स्ट रिकॉग्नाइज़र के रूप में कार्य करता है जो विज़ुअल डेटा से अक्षरों और स्ट्रिंग्स की पहचान करता है। इस लाइब्रेरी में एक कस्टम OCR मॉडल ट्रेनर और कस्टम फॉन्ट रिकग्निशन के लिए टूल शामिल हैं। ये क्षमताएं मान्यता सटीकता में सुधार करने के लिए विशिष्ट फॉन्ट और कैरेक्टर सेट के अनुरूप विशेष न्यूरल नेटवर्क के निर्माण की अनुमति देती हैं। यह सिस्टम व्यक्तिगत अक्षर क्षेत्रों की पहचान करने के लिए कनेक्टेड-कंपोनेंट लेबलिंग का उपयोग करता है और छोटे अल्फ़ान्यूमेरिक कोड को डिजिटल टेक्स्ट में बदलने के लिए इमेज प्रोसेसिंग का उपयोग करता है।

    Employs connected-component identification to locate individual character blobs in images.

    Swift
    GitHub पर देखें↗4,632
  • zxing-js/libraryzxing-js का अवतार

    zxing-js/library

    2,917GitHub पर देखें↗

    This library is a multi-format barcode processing utility designed for web browsers and server-side environments. It provides a comprehensive suite of tools for both decoding existing barcode symbols and generating new ones from input data. The project distinguishes itself by enabling continuous, real-time barcode scanning directly from live video streams. By utilizing browser camera interfaces and automated frame processing, it allows applications to capture and interpret one-dimensional and two-dimensional barcode data without requiring manual image uploads or user intervention. Beyond rea

    Analyzes pixel luminance to isolate high-contrast barcode patterns from background noise in raw image data.

    TypeScriptbarcodedecoding-imagesencoding-library
    GitHub पर देखें↗2,917
  • marcosfede/algorithmsmarcosfede का अवतार

    marcosfede/algorithms

    1,132GitHub पर देखें↗

    This project is a comprehensive repository of fundamental computer science algorithms and data structures designed as a reference for academic study, technical interview preparation, and competitive programming. It provides standardized implementations of core computational strategies, serving as an educational resource for developers to master software engineering fundamentals and algorithmic problem-solving. The collection distinguishes itself through a multi-language approach, offering cross-language solutions for complex tasks ranging from graph traversal and dynamic programming to bitwis

    Implements algorithms for identifying and counting contiguous regions of identical values in a grid.

    Pythonalgorithmbfscompetitive-programming
    GitHub पर देखें↗1,132
  1. Home
  2. Software Engineering & Architecture
  3. Algorithmic Problem Solving
  4. Grid Connectivity Solving
  5. Connected Component Identification

सब-टैग एक्सप्लोर करें

  • Pixel Connectivity AnalysisIdentifying and labeling contiguous regions of pixels in binary images. **Distinct from Connected Component Identification:** Applies connected component analysis to image pixels rather than abstract data grids.