enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. U-Net - Wikipedia

    en.wikipedia.org/wiki/U-Net

    U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [ 2 ] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation .

  3. Connected-component labeling - Wikipedia

    en.wikipedia.org/wiki/Connected-component_labeling

    Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic.

  4. Insight Segmentation and Registration Toolkit - Wikipedia

    en.wikipedia.org/wiki/Insight_Segmentation_and...

    ITK stands for The Insight Segmentation and Registration Toolkit. The toolkit provides leading-edge segmentation and registration algorithms in two, three, and more dimensions. ITK uses the CMake build environment to manage the configuration process. The software is implemented in C++ and it is wrapped for Python. An offshoot of the ITK project ...

  5. Image segmentation - Wikipedia

    en.wikipedia.org/wiki/Image_segmentation

    In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...

  6. Graph cuts in computer vision - Wikipedia

    en.wikipedia.org/wiki/Graph_cuts_in_computer_vision

    As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision [1]), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.

  7. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    The model was trained with back-propagation. The training algorithm was further improved in 1991 [55] to improve its generalization ability. The model architecture was modified by removing the last fully connected layer and applied for medical image segmentation (1991) [51] and automatic detection of breast cancer in mammograms (1994). [52]

  8. Point Cloud Library - Wikipedia

    en.wikipedia.org/wiki/Point_Cloud_Library

    The Point Cloud Library (PCL) is an open-source library of algorithms for point cloud processing tasks and 3D geometry processing, such as occur in three-dimensional computer vision. The library contains algorithms for filtering, feature estimation, surface reconstruction, 3D registration, [5] model fitting, object recognition, and segmentation ...

  9. Random walker algorithm - Wikipedia

    en.wikipedia.org/wiki/Random_walker_algorithm

    The random walker algorithm is an algorithm for image segmentation. In the first description of the algorithm, [1] a user interactively labels a small number of pixels with known labels (called seeds), e.g., "object" and "background". The unlabeled pixels are each imagined to release a random walker, and the probability is computed that each ...