User:Tyler Kelso/sandbox

= Article Evaluation Project = The article I chose for my article evaluation is: Digital image processing. I chose this article because of my likely future in the field of technology and my curiosity of the process, history and future of Digital image processing.

Lead

 * The lead for this article is incredibly clear in its intentions and the information it will be representing.
 * Lead includes the concepts that will be reviewed in the article, but not in as much detail.
 * All information addressed in the lead is represented in the article.
 * The lead is very concise in representing the information it presents.

Content

 * All content shown in the article is relevant information regarding Digital imaging processing
 * The content this article makes reference to is mostly current articles as late as 2019, but does hold information from the 1990's due to its history section.
 * All content in the article is content that belongs within this article/is appropriate to the article.
 * The article does not deal with one of Wikipedia's equity gaps.

Tone and Balance

 * The article remains 100% neutral and only discusses facts relevant to digital imaging processing.
 * The article does not make claims that appear biased towards and position
 * There are no over-represented or underrepresented viewpoints within the article
 * The article does not attempt to persuade the reader in any one position

Sources and References

 * All of the information comes from reliable sources.
 * The sources used for this article reflect the available literature on the topic.
 * The sources are all as current as the late 2010's (except for the sources relating to the history section, where the information is accurate about the time they represent).
 * The sources are written by a plethora of different authors
 * the links used in the article as reference work

Organization

 * The article is setup neatly and makes it very easy to understand information.
 * The article does not have any grammatical errors that went noticed.
 * The article is broken down into the various components that have gone into Digital imaging processing

Images and Media

 * The article uses images to help readers better understand the different transformations available for Digital image processing
 * The images are very clearly captioned so the reader can determine their place and use in the article
 * The images adhere to Wikipedia's copyright regulations
 * The images are laid out in a table and are made very easy to understand

Checking the talk page

 * There is the topic of merging the article with the article Digital imaging.
 * This article is a part of the WikiProject: WikiProject Technology which is related to the WikiProject Human–Computer Interaction

Overall Evaluation

 * Overall I believe the article is a very strong article that represents information very well.
 * The article makes great use at using its references and related articles within itself.
 * The article could be improved by including information on the current day use of Digital image processing or linking to articles regarding that matter.
 * This article is incredibly well developed and has flawlessly executed the basic fundamental rules in creating a well developed article.

Article Evaluations
Lead Evaluation - The lead to the article is very powerful and clears all points it attempts to make very concisely.

Content Evaluation - The content in the article all remains relevant to the concepts supposed to be represented within the article with recent and relevant information.

Tone and Balance Evaluation - The articles tone remains completely unbiased as the article strictly covers facts about digital imaging processing and does not have any over-represented or underrepresented viewpoints or attempt to persuade the reader towards any one position.

Sources and References - The sources referenced by this article all come from reliable sources and from a plethora of different authors with working links.

Organization - The article has great organization and makes it very clear to find the information you are looking for as it breaks down all of the various aspects very nicely.

Images and Media - The article utilizes its use of images to make the concepts it is attempting to visually show the reader very easy to read.

Checking the talk page - The talk page is rather vacant but supplies insight into future moves for the article and its relating articles/Wikiprojects.

Overall evaluation - Overall the article is incredibly strong and holds very few flaws in comparison to its great deal of areas of perfection.

= Chosen Article = The article I chose for my project is the same one I used for my article evaluation, the article Digital image processing.

Article
Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems. The generation and development of digital image processing are mainly affected by three factors: first, the development of computers; second, the development of mathematics (especially the creation and improvement of discrete mathematics theory); third, the demand for a wide range of applications in environment, agriculture, military, industry and medical science has increased.

Contents

 * 1History
 * 1.1Image sensors
 * 1.2Image compression
 * 1.3Digital signal processor (DSP)
 * 1.4Medical imaging
 * 2Tasks
 * 3Digital image transformations
 * 3.1Filtering
 * 3.2Image padding in Fourier domain filtering
 * 3.3Filtering code examples
 * 3.4Affine transformations
 * 4Applications
 * 4.1Digital camera images
 * 4.2Film
 * 5Fatigue detection and monitoring technologies
 * 6See also
 * 7References
 * 8Further reading
 * 9External links

History
Further information: Digital imaging § History

Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s, at Bell Laboratories, the Jet Propulsion Laboratory, Massachusetts Institute of Technology, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement. The purpose of early image processing was to improve the quality of the image. It was aimed for human beings to improve the visual effect of people. In image processing, the input is a low-quality image, and the output is an image with improved quality. Common image processing include image enhancement, restoration, encoding, and compression. The first successful application was the American Jet Propulsion Laboratory (JPL). They used image processing techniques such as geometric correction, gradation transformation, noise removal, etc. on the thousands of lunar photos sent back by the Space Detector Ranger 7 in 1964, taking into account the position of the sun and the environment of the moon. The impact of the successful mapping of the moon's surface map by the computer has been a huge success. Later, more complex image processing was performed on the nearly 100,000 photos sent back by the spacecraft, so that the topographic map, color map and panoramic mosaic of the moon were obtained, which achieved extraordinary results and laid a solid foundation for human landing on the moon.

The cost of processing was fairly high, however, with the computing equipment of that era. That changed in the 1970s, when digital image processing proliferated as cheaper computers and dedicated hardware became available. This led to images being processed in real-time, for some dedicated problems such as television standards conversion. As general-purpose computers became faster, they started to take over the role of dedicated hardware for all but the most specialized and computer-intensive operations. With the fast computers and signal processors available in the 2000s, digital image processing has become the most common form of image processing, and is generally used because it is not only the most versatile method, but also the cheapest.

Image sensors
Main article: Image sensor

The basis for modern image sensors is metal-oxide-semiconductor (MOS) technology, which originates from the invention of the MOSFET (MOS field-effect transistor) by Mohamed M. Atalla and Dawon Kahng at Bell Labs in 1959. This led to the development of digital semiconductor image sensors, including the charge-coupled device (CCD) and later the CMOS sensor.

The charge-coupled device was invented by Willard S. Boyle and George E. Smith at Bell Labs in 1969. While researching MOS technology, they realized that an electric charge was the analogy of the magnetic bubble and that it could be stored on a tiny MOS capacitor. As it was fairly straightforward to fabricate a series of MOS capacitors in a row, they connected a suitable voltage to them so that the charge could be stepped along from one to the next. The CCD is a semiconductor circuit that was later used in the first digital video cameras for television broadcasting.

The NMOS active-pixel sensor (APS) was invented by Olympus in Japan during the mid-1980s. This was enabled by advances in MOS semiconductor device fabrication, with MOSFET scaling reaching smaller micron and then sub-micron levels. The NMOS APS was fabricated by Tsutomu Nakamura's team at Olympus in 1985. The CMOS active-pixel sensor (CMOS sensor) was later developed by Eric Fossum's team at the NASA Jet Propulsion Laboratory in 1993. By 2007, sales of CMOS sensors had surpassed CCD.

Image compression
Main article: Image compression

An important development in digital image compression technology was the discrete cosine transform (DCT), a lossy compression technique first proposed by Nasir Ahmed in 1972. DCT compression became the basis for JPEG, which was introduced by the Joint Photographic Experts Group in 1992. JPEG compresses images down to much smaller file sizes, and has become the most widely used image file format on the Internet. Its highly efficient DCT compression algorithm was largely responsible for the wide proliferation of digital images and digital photos, with several billion JPEG images produced every day as of 2015.

Digital signal processor (DSP)
Main article: Digital signal processor

Electronic signal processing was revolutionized by the wide adoption of MOS technology in the 1970s. MOS integrated circuit technology was the basis for the first single-chip microprocessors and microcontrollers in the early 1970s, and then the first single-chip digital signal processor (DSP) chips in the late 1970s. DSP chips have since been widely used in digital image processing.

The discrete cosine transform (DCT) image compression algorithm has been widely implemented in DSP chips, with many companies developing DSP chips based on DCT technology. DCTs are widely used for encoding, decoding, video coding, audio coding, multiplexing, control signals, signaling, analog-to-digital conversion, formatting luminance and color differences, and color formats such as YUV444 and YUV411. DCTs are also used for encoding operations such as motion estimation, motion compensation, inter-frame prediction, quantization, perceptual weighting, entropy encoding, variable encoding, and motion vectors, and decoding operations such as the inverse operation between different color formats (YIQ, YUV and RGB) for display purposes. DCTs are also commonly used for high-definition television (HDTV) encoder/decoder

Medical imaging
Further information: Medical imaging

In 1972, the engineer from British company EMI Housfield invented the X-ray computed tomography device for head diagnosis, which is what is usually called CT (computer tomography). The CT nucleus method is based on the projection of the human head section and is processed by computer to reconstruct the cross-sectional image, which is called image reconstruction. In 1975, EMI successfully developed a CT device for the whole body, which obtained a clear tomographic image of various parts of the human body. In 1979, this diagnostic technique won the Nobel Prize. Digital image processing technology for medical applications was inducted into the Space Foundation Space Technology Hall of Fame in.

Tasks
Digital image processing allows the use of much more complex algorithms, and hence, can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analogue means.

In particular, digital image processing is a concrete application of, and a practical technology based on:


 * Classification
 * Feature extraction
 * Multi-scale signal analysis
 * Pattern recognition
 * Projection

Some techniques which are used in digital image processing include:


 * Anisotropic diffusion
 * Hidden Markov models
 * Image editing
 * Image restoration
 * Independent component analysis
 * Linear filtering
 * Neural networks
 * Partial differential equations
 * Pixelation
 * Point feature matching
 * Principal components analysis
 * Self-organizing maps
 * Wavelets

Filtering
Digital filters are used to blur and sharpen digital images. Filtering can be performed by:


 * convolution with specifically designed kernels (filter array) in the spatial domain
 * masking specific frequency regions in the frequency (Fourier) domain

The following examples show both methods:

Image padding in Fourier domain filtering
Images are typically padded before being transformed to the Fourier space, the highpass filtered images below illustrate the consequences of different padding techniques: Notice that the highpass filter shows extra edges when zero padded compared to the repeated edge padding.

Filtering code examples
MATLAB example for spatial domain highpass filtering. img=checkerboard(20);                          % generate checkerboard % ************************** SPATIAL DOMAIN  *************************** klaplace=[0 -1 0; -1 5 -1; 0 -1 0];             % Laplacian filter kernel X=conv2(img,klaplace);                         % convolve test img with % 3x3 Laplacian kernel figure imshow(X,[])                                   % show Laplacian filtered title('Laplacian Edge Detection')

Affine transformations
Affine transformations enable basic image transformations including scale, rotate, translate, mirror and shear as is shown in the following examples: To apply the affine matrix to an image, the image is converted to matrix in which each entry corresponds to the pixel intensity at that location. Then each pixel's location can be represented as a vector indicating the coordinates of that pixel in the image, [x, y], where x and y are the row and column of a pixel in the image matrix. This allows the coordinate to be multiplied by an affine-transformation matrix, which gives the position that the pixel value will be copied to in the output image.

However, to allow transformations that require translation transformations, 3 dimensional homogeneous coordinates are needed. The third dimension is usually set to a non-zero constant, usually 1, so that the new coordinate is [x, y, 1]. This allows the coordinate vector to be multiplied by a 3 by 3 matrix, enabling translation shifts. So the third dimension, which is the constant 1, allows translation.

Because matrix multiplication is associative, multiple affine transformations can be combined into a single affine transformation by multiplying the matrix of each individual transformation in the order that the transformations are done. This results in a single matrix that, when applied to a point vector, gives the same result as all the individual transformations performed on the vector [x, y, 1] in sequence. Thus a sequence of affine transformation matrices can be reduced to a single affine transformation matrix.

For example, 2 dimensional coordinates only allow rotation about the origin (0, 0). But 3 dimensional homogeneous coordinates can be used to first translate any point to (0, 0), then perform the rotation, and lastly translate the origin (0, 0) back to the original point (the opposite of the first translation). These 3 affine transformations can be combined into a single matrix, thus allowing rotation around any point in the image.

Applications
Further information: Digital imaging and Applications of computer vision

Digital camera images
Digital cameras generally include specialized digital image processing hardware – either dedicated chips or added circuitry on other chips – to convert the raw data from their image sensor into a color-corrected image in a standard image file format.

Film
Westworld (1973) was the first feature film to use the digital image processing to pixellate photography to simulate an android's point of view.

Fatigue detection and monitoring technologies
There were significant advancements in fatigue monitoring technology the past decade. These innovative technology solutions are now commercially available and offer real safety benefits to drivers, operators and other shift workers across all industries.[citation needed]

Software developers, engineers and scientists develop fatigue detection software using various physiological cues to determine the state of fatigue or drowsiness. The measurement of brain activity (electroencephalogram) is widely accepted as the standard in fatigue monitoring. Other technology used to determine fatigue related impairment include behavioural symptom measurements such as; eye behaviour, gaze direction, micro-corrections in steering and throttle use as well as heart rate variability.[citation needed]

See also[edit]

 * Digital imaging
 * Computer graphics
 * Computer vision
 * CVIPtools
 * Digitizing
 * Free boundary condition
 * GPGPU
 * Homomorphic filtering
 * Image analysis
 * IEEE Intelligent Transportation Systems Society
 * Multidimensional systems
 * Remote sensing software
 * Standard test image
 * Superresolution
 * Total variation denoising
 * Machine Vision
 * Bounded variation
 * Radiomics