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Jul 6, 2022 · Architectural photography : composition, capture, and digital image processing. by. Schulz, Adrian, 1980-. Publication date. 2009. Topics. Architectural photography, Photography -- Digital techniques. Publisher. Santa Barbara, CA : Rocky Nook ; Sebastopol, CA : Distributed by O'Reilly Media.
Image Processing techniques using OpenCV and Python. - BhanuPrakashNani/Image_Processing
- 1.2 Discrete-Time Systems
- 1.3 Linear Time-Invariant Systems
- 3.3 Discrete to Continuous
- 4.4 Weighted
- h = M 1H
- 4.3 Least-Squares
- 5.1 Pinhole Camera
- 5.2 Lenses
- 6.1 Lookup Table
- 7.2 Derivative Filters
- Dff(x)g = Dff[x] h (x)g (7.7)
- h 0(x) h (y) (7.19)
- h 0[x] h [y] (7.20)
In its most general form, a discrete-time system is a transformation that maps a discrete-time signal, f[x], onto a unique g[x], and is denoted as: g[x] =
Of particular interest to us will be a class of discrete-time systems that are both linear and time-invariant. A system is said to be linear if it obeys the rules of superposition, namely:
If the Nyquist rate is met, then a discrete-time signal fully charac-terizes the continuous-time signal from which it was sampled. On the other hand, if the Nyquist rate is not met, then the sampling leads to aliasing, and the discrete-time signal does not accurately represent its continuous-time counterpart. In the former case, it is possible to r...
the form: Least-Squares G[ ] = F [ ]H[ ] (4.2) In other words, a filter modifies the frequencies of the input signal. It is often the case that such filters pass certain frequencies and attenuate others (e.g., a lowpass, bandpass, or highpass filters). The design of such filters consists of four basic steps: choose the desired frequency response ch...
(4.6) Since the matrix M is square, this design is equivalent to solv-ing for n unknowns (the filter taps) from n linearly independent equations. This fact illustrates the shortcomings of this approach, namely, that this method produces a filter with a frequency re-sponse that exactly matches the sampled response, but places no constraints on the r...
Our goal is to design a filter h that “best” approximates a specified frequency response. As before this constraint can be expressed as:
The history of the pinhole camera (or camera obscura) dates back as early as the fifth century B.C., and continues to be popular to-day among students, artists, and scientists. The Chinese philoso-pher Mo Ti is believed to be the first to notice that objects reflect light in all directions and that the light rays that pass through small hole produc...
It is important to remember that both the perspective and or-thographic projection equations are only approximations of more complex imaging systems. Commercial cameras are constructed with a variety of lenses that collect and focus light onto the im-age plane. That is, light emanates from a point in the world in all directions and, whereas a pinho...
The internal representation of a digital image is simply a matrix of numbers representing grayscale or color values. But when an image is displayed on a computer monitor we typically do not see a direct mapping of the image. An image is first passed through a lookup table (LUT) that maps the image intensity values to bright-ness values, Figure 6.1....
Discrete differentiation forms the foundation for many applications in image processing and computer vision. We are all familiar with the definition of the derivative of a continuous signal f(x): Dff(x)g = f(x + ) f(x) lim ✪ ε 0 (7.5) This definition requires that the signal f(x) be well defined for all x 2 R. So, does it make sense to differentia...
and expressing the right-hand side in terms of the convolution sum:
And finally, sampling both sides gives an expression for the partial derivative of the discretely sampled two-dimensional signal:
Notice that calculating the partial derivative requires a pair of one-dimensional convolutions: a derivative filter, h0[x], in the di-mension of differentiation, and an interpolation filter, h[y], in the other dimension (for multi-dimensional signals, all remain-ing dimensions would be convolved with the interpolation filter). Since two-dimensional...
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Why do we process images? Acquire an image. Correct aperture and color balance. Reconstruct image from projections. Prepare for display or printing. Adjust image size. Color mapping, gamma-correction, halftoning. Facilitate picture storage and transmission. Efficiently store an image in a digital camera. Send an image from space.
Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation, and processing of image data for tasks such as storage, transmission, and extraction of pictorial information.
Image Processing that is predominantly aimed at undergraduate study and teaching: Vol.1: Fundamental Techniques, Vol.2: Core Algorithms, Vol.3: Advanced Methods (this volume). While it builds on the previous two volumes and relies on the their proven format, it contains all new material published by the authors for the first time.
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Digital Image Processing, 4th edition Gonzalez and Woods Pearson/Prentice Hall © 2018 Table of Contents Chapter 1 Introduction 1 1.1 What is Digital Image Processing? 2 1.2 The Origins of Digital Image Processing 3 1.3 Examples of Fields that Use Digital Image Processing 7 Gamma-Ray Imaging 8 X-Ray Imaging 8 Imaging in the Ultraviolet Band 11
