This write up is a simple explanation on Images and processing of the images for all who wish to start with.
Let us start with steps involved in Imaging.
• Understand the objects
• Capture Images (from Real World Objects)
• Digital Conversion (Create Computer readable images)
o Image – Pixel Representation in a matrix
• Image Pre Processing
• Image Analysis
Why?
• To Overcome Limitations of Human Eye
• To Analyze real world objects and changes continuously
• For Automation
• For Archival
• To do Accurate Measurements using computational powers of personal computers
What is an Image?
In simple, image is a representation of the external form of a person or thing in art.
Talking about processing always take us in to digital domain where computers play a major role in all complex processing of images. Hence an image when taken in digital form becomes a digital image.
Digital images are made of picture elements called pixels.Typically, pixels are organized in an ordered rectangular array. The size of an image is determined by the dimensions of this pixel array. The image width is the number of columns, and the image height is the number of rows in the array. Thus the pixel array is a matrix of M columns x N rows. To refer to a specific pixel within the image matrix, we define its coordinate at x and y. The coordinate system of image matrices defines x as increasing from left to right and y as increasing from top to bottom.Compared to normal mathematic convention, the origin is in the top left corner and the y coordinate is flipped. Why is the coordinate system flipped vertically? Originally, digital images were defined in terms of the electron beam scanning pattern of televisions. The beam scanned from left to right and top to bottom. Other than this historical reason, there is no purpose served by this inversion of the y coordinate.

Characteristics:
Image size is not to be confused with the size of the real world representation of an image. Image size specifically describes the number of pixels within a digital image. The real world representation of a digital image requires one additional factor called resolution. Resolution is the spatial scale of the image pixels.
Intensity, defines an image. Each pixel has its own intensity value. Monochrome images have intensity from the darkest gray (black) to lightest gray (white). Color images, on the other hand, have intensity from the darkest and lightest of three different colors, Red, Green, and Blue. The various mixtures of these color intensities produces a color image. Thus the two most basic types of digital images, Mono and Color, are known as grayscale and RGB images
Intensity values in digital images are defined by bits. A bit is binary and only has two possible values, 0 or 1. An 8-bit intensity range has 256 possible values, 0 to 255. This can be seen mathematically by 2(# of bits). For a 1-bit, or binary, image, 21 = 2 possible values and for an 8-bit image, 28 = 256 possible values.
RGB images use 8-bit intensity ranges for each color and B&W images have a single 8-bit intensity range. Since RGB images contain 3 x 8-bit intensities they are also referred to as 24-bit color images. There are also 10 to 16 bit images possible.
Image Processing:
Digital image processing is the use of computer algorithms to perform image processing on digital images.
• Preparing the images for further analysis
• Enhance
• Filter
• Region of Interest Selection
• Objects of Interest Selection
General Image processing techniques used are:
• Image Enhancement: methods are applied to image data without any prior info or knowledge of the image contents, in general. Enhancement is often required to overcome the effects of degradation on Images due to several factors. (Brightness/contrast are typical examples).
• Edge Detection: n an image, an edgeis a curve that follows a path of rapid change in image intensity. Edges are often associated with the boundaries of objects in a scene. Edgedetection is used to identify the edges in an image. To see a profile of an object edge detection would be of use. Canny, Roberts, Sobel are known edge detection methods.

Morphology: Morphologyis a broad set of image processing operations that process images based on shapes. Morphological operations apply a structuring element to an input image, creating an output image of the same size. In a morphological operation, the value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbours. By choosing the size and shape of the neighbourhood, you can construct a morphological operation that is sensitive to specific shapes in the input image. Dilation, Erosion etc., are major operators. Any analysis on identifying the objects with shape or not falling in to definite shape (blobs) morphological operators are used.

Segmentation: is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super-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 analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.


• Convolution, Filtering, and Transforms: Filtering is a neighbourhood operation,in which the value of any given pixel in the output image is determined by applying some algorithm to the values of the pixels in the neighbourhood of the corresponding input pixel. Linear filtering of an image is accomplished through an operation called convolution.
Convolution is a neighbourhood operation in which each output pixel is the weighted sum of neighbouring input pixels.
There are methods available for Spatial domain and frequency domain to do different filtering operations. Transforms like Fourier, hough etc., would be under frequency domain to do filtering process.

Following the Image processing, an image is analysed in Image analysis steps to get the needed output from the image like measurements, feature extraction, comparison and lead to different applications in Computer Vision and Machine Vision segments.
Image processing in modern era is used in all cameras in mobile phones to provide a better picture of our interest to many complex problems in the field of vision, remote sensing and many more.
This blog write up is to give a beginning to all who are interested to know Image processing and get in to this wonderful area of working. Application areas are many including medical, industrial automation, materials science, cosmetics, space, defence etc.
Online Solutions (Imaging) Pvt. Ltd., Chennai India represents many companies in these kinds of Image processing software in India from around the globe. ADCIS is one important company from France that the company deals with releases such software products. Online Solutions India with ADCIS France also conducts courses on “Advanced Imaging” in collaboration with Institutions.
In India, if it is all about Image processing then Online Solutions could render their services from teaching, setting up labs and also helping projects.
Note: The above article is a collection from various web sites, manufacturers and distributors / integrators of image processing. If any company/individual finds any ownership of contents – they can let us know at [email protected] to either change or remove the contents. Article is provided only for the information purposes and facts can be cross verified by the readers.
Source : https://www.onlsol.com/blog/a-simple-abc-s-of-image-processing/