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  • 1
    UoMASM

    UoMASM

    A C++ Active Shape Model Library from the University of Manchester

    A library and associated tools for building and searching with Active Shape Models (ASM). It builds on the VXL computer vision libraries. Includes a GUI tool to search new images with the ASM. Depends on: VXL, Qt (Version 4), UoMqVXL
    Downloads: 0 This Week
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  • 2
    Hiera

    Hiera

    A fast, powerful, and simple hierarchical vision transformer

    Hiera is a hierarchical vision transformer designed to be fast, simple, and strong across image and video recognition tasks. The core idea is to use straightforward hierarchical attention with a minimal set of architectural “bells and whistles,” achieving competitive or superior accuracy while being markedly faster at inference and often faster to train. The repository provides installation options (from source or Torch Hub), a model zoo with pre-trained checkpoints, and code for evaluation...
    Downloads: 0 This Week
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  • 3
    BADGr

    BADGr

    Toolbox for Box Approximation, Decomposition, and Grasping

    BADGr, the BoxGrasping toolbox, is a package for Box Approximation, Decomposition, and Grasping. The toolbox was developed in the Computer Vision & Active Perception Lab, at the Royal Institute of Technology, as a participant of the EU research project PACO-PLUS, and published at the project's end in Summer 2010. BADGr provides modules to approximate the shape of a point cloud (possibly from sensor data) by box primitives. These box primitives then serve as a base for the generation of box-based pre-grasp hypotheses for robot grippers.
    Downloads: 0 This Week
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  • 4

    Time Adaptive Self-Organizing Map

    An Artificial Neural Network for Clustering, Classification, etc

    This project tries to include Time Adaptive Self-Organizing Map (TASOM) implementations for solving Computational Intelligence problems such as Pattern Recognition, Computer Vision, Clustering, Active Contour Modeling, and the like. The TASOM has been originally introduced for adaptive and changing environments. Several versions of TASOM networks have been introduced. Some of them are capable of changing the number of neurons based on the problems at hand. Moreover, a binary tree version of the TASOM has been introduced for faster performance.
    Downloads: 0 This Week
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