Nothing Special   »   [go: up one dir, main page]
Scribd Logo
Upload

Welcome to Scribd!

  • 17 views

    BANDOQUILLO-LINEARALGEBRA

    Uploaded by

    Michelle Bandoquillo
    1. A vector space model represents sets of items or objects based on their features and is used in computer vision, image processing, and other areas of mathematics and computer science. 2. Some real-life applications of vector space models include surface reconstruction from images, image processing, robotics and navigation, graph matching, and pattern recognition and classification. 3. Surface reconstruction from images uses vector space models to develop algorithms that reconstruct 3D models of surfaces from 2D images by translating pixel colors into mathematical points in a plane.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd

    BANDOQUILLO-LINEARALGEBRA

    Uploaded by

    Michelle Bandoquillo
    17 views2 pages
    1. A vector space model represents sets of items or objects based on their features and is used in computer vision, image processing, and other areas of mathematics and computer science. 2. Some real-life applications of vector space models include surface reconstruction from images, image processing, robotics and navigation, graph matching, and pattern recognition and classification. 3. Surface reconstruction from images uses vector space models to develop algorithms that reconstruct 3D models of surfaces from 2D images by translating pixel colors into mathematical points in a plane.

    Original Description:

    Original Title

    BANDOQUILLO-LINEARALGEBRA. (1) (1) (1)

    Copyright

    © © All Rights Reserved

    Available Formats

    PDF, TXT or read online from Scribd

    Did you find this document useful?

    Is this content inappropriate?

    1. A vector space model represents sets of items or objects based on their features and is used in computer vision, image processing, and other areas of mathematics and computer science. 2. Some real-life applications of vector space models include surface reconstruction from images, image processing, robotics and navigation, graph matching, and pattern recognition and classification. 3. Surface reconstruction from images uses vector space models to develop algorithms that reconstruct 3D models of surfaces from 2D images by translating pixel colors into mathematical points in a plane.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    17 views2 pages

    BANDOQUILLO-LINEARALGEBRA

    Uploaded by

    Michelle Bandoquillo
    1. A vector space model represents sets of items or objects based on their features and is used in computer vision, image processing, and other areas of mathematics and computer science. 2. Some real-life applications of vector space models include surface reconstruction from images, image processing, robotics and navigation, graph matching, and pattern recognition and classification. 3. Surface reconstruction from images uses vector space models to develop algorithms that reconstruct 3D models of surfaces from 2D images by translating pixel colors into mathematical points in a plane.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    of 2

    BANDOQUILLO MICHELLE P.

    BSEM 2-1

    ACTIVITY NO.5

    BSEM 33
    LINEAR ALGEBRA

    A vector space model is a representation of a set of items or objects based on their


    features. It is used in computer vision and image processing to transform images into feature
    vectors that can be compared and matched with each other.

    This vector space has also been used in many areas of mathematics and computer science.
    Many real-life applications of vector space include the following:

    1.Surface reconstruction from images

    2.Image processing

    3.Robotics and navigation

    4.Graph matching

    5.Pattern recognition and classification

    The first one is the surface reconstruction from images. The goal of this example of
    vector space is to develop an algorithm for reconstructing reflection surfaces from images.
    The idea is to translate the image into a mathematical language that can be understood by
    computer programs. For example, I will find the closest point on a plane that has the same
    color as each pixel in an image. The next step will be to find all the points in the plane that
    are connected to each other through lines and do some more calculations. The result will be
    a 3D model of the object's surface.

    The second one is image processing. In converting this to mathematical language, the
    reflectance of an object is the fraction of incident light that is reflected by the object. It is
    therefore a dimensionless quantity. The reflectance is related to the reflectivity, which is a
    dimensional quantity and depends on what material the surface of the object consists of.
    Reflectivity can be measured for electromagnetic waves in general using a spectrometer and
    for visible light using a colorimeter. In radiometry, reflectance refers to how much
    electromagnetic power is reflected by a surface into a given solid angle per unit projected
    area (i.e., the ratio of reflected power density to incident power density).

    The third is robotics and navigation. The ability to navigate space is one of the most
    important skills a robot can learn. Without it, they would be unable to move around their
    environment and
    accomplish any tasks given to them. Navigating space is a complex problem that has been
    studied for decades.

    One of the most common ways to solve this problem is by using a technique called the A*.
    algorithm. This algorithm uses a mathematical representation of space that allows you to
    calculate how far your robot can travel in a given direction before hitting an obstacle.

    The A* algorithm does not assume anything about your environment except that it can be
    represented as a grid with obstacles on it. You can use any type of grid as long as you have
    some way of representing it mathematically — whether it's an array or list or something else
    entirely, like graph data structures.

    Fourth is graph matching. Many real-world graphs are labeled by other nodes in the graph.
    For example, in a social network, each node is labeled by an individual's name. In an
    infrastructure network, each node might be labeled with a component's name. In a city map,
    each node could be a person's location, and each edge could be the distance between two
    people.

    These labels can be used to improve the quality of graph matching by automatically aligning
    the labels with similar ones across different graphs. We call this approach label alignment
    graph matching (LAGM). LAGM can be used as a preprocessing step for many graph
    matching algorithms; it improves the quality of matches on unlabeled graphs and reduces the
    search space for traditional forms of graph matching like vertex or edge matching.

    The last one is Pattern Recognition and Classification. If expressed in mathematical


    language, it is the process of assigning labels to data points. The purpose of this exercise is
    to identify a pattern, or set of patterns, in the data and then label each data point with one or
    more labels that can be used for further analysis. The goal of this project is to provide a tool
    for pattern recognition and classification. This program will accept an image as input and
    output a list of possible identities for each pixel in the input image.

    You might also like