• Fitting

    Fitting

    Invite you to consult the lecture content "Fitting" below. Contents of lectures introduce to you the content ''Problem with vertical least squares, total least squares, least squares as likelihood maximization, least squares for general curves". Hopefully document content to meet the needs of learning, work effectively.

     34 p vlute 16/11/2015 17 0

  • Face detection

    Face detection

    To help you specialized culture and art have added references in the process of learning and study. Invite you to consult the lecture content "Face detection". Each of your content and references for additional lectures will serve the needs of learning and research.

     39 p vlute 16/11/2015 13 0

  • Comp 776: Computer vision

    Comp 776: Computer vision

    What kind of information can we extract from an image, why study computer vision, why is computer vision difficult,... To help you answer the questions above, you are invited to refer to the content of the curriculum ''Comp 776: Computer vision". Hope this is useful references for you.

     61 p vlute 16/11/2015 11 0

  • Today: Cameras

    Today: Cameras

    The pinhole projection model, cameras with lenses, digital cameras,... is the main content of the lecture "Today: Cameras". Invite you to consult the detailed content lectures to capture details.

     71 p vlute 16/11/2015 15 0

  • Capturing light

    Capturing light

    Radiometry, solid angle, radiometry of thin lenses, from light rays to pixel values, the interaction of light and surfaces,... is the main content of the lecture "Capturing light". Invite you to consult the detailed content lectures to capture details.

     31 p vlute 16/11/2015 10 0

  • Feature extraction: Corners and blobs

    Feature extraction: Corners and blobs

    Characteristics of good features, applications, finding corners, corner detection basic idea, corner detection mathematics,... As the main contents of the lectures "Feature extraction: Corners and blobs". Invite you to consult for additional documents for the academic needs and research.

     65 p vlute 16/11/2015 12 0

  • Linear filtering

    Linear filtering

    Given a camera and a still scene, how can you reduce noise, what about near the edge, why is separability useful in practice,... To help you understand more about this issue, invite you to consult the lecture content "Linear filtering". Hope this is useful references for you.

     42 p vlute 16/11/2015 10 0

  • Color

    Color

    Color is a psychological property of our visual experiences when we look at objects and lights, not a physical property of those objects or lights, color is the result of interaction between physical light in the environment and our visual system. To help you understand more about this issue, invite you to consult the lecture content "Color". Hope this is useful references for you.

     57 p vlute 16/11/2015 5 0

  • Edge detection

    Edge detection

    Origin of edges, characterizing edges, image gradient, finite difference filters, effects of noise,... As the main contents of the lecture "Edge detection". Each of your content and references for additional lectures will serve the needs of learning and research.

     30 p vlute 16/11/2015 22 0

  • Image alignment

    Image alignment

    Invite you to consult the lecture content "Image alignment" below. Contents of lectures introduce to you the content: A look into the past, bing streetside images, image alignment, alignment as fitting, fitting an affine transformation. Hopefully document content to meet the needs of learning, work effectively.

     61 p vlute 16/11/2015 5 0

  • Fitting: The hough transform

    Fitting: The hough transform

    Voting schemes, hough transform, parameter space representation, algorithm outline, basic illustration,... As the main contents of the lecture "Fitting: The hough transform". Each of your content and references for additional lectures will serve the needs of learning and research.

     46 p vlute 16/11/2015 13 0

  • Discriminative and generative methods for bags of features

    Discriminative and generative methods for bags of features

    Invite you to consult the lecture content "Discriminative and generative methods for bags of features" below. Contents of lectures introduce to you the content: Image classification, discriminative methods, nearest neighbor classifier, classification, support vector machines. Hopefully document content to meet the needs of learning, work effectively.

     22 p vlute 16/11/2015 6 0

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