Image processing is the application of Machine Learning comprising a set of computational techniques for analyzing, enhancing, compressing, and reconstructing images. Its main components are importing (i.e., image capturing through digital photography); analysis and manipulation of the image, which is accomplished using various specialized software applications; and output (e.g., to a printer or monitor).
The applications of image analytics are endless. Organizations are starting to realize all of the possible ways to extract value from unstructured data, such as images or video footage, to create new and enticing customer experiences within retail, entertainment, transportation, airport security, insurance claims, and more.
What is Image Processing?
Image processing is a method of performing operations on an image to convert it into digital form. It can also enhance the image or extract useful information from it. It is a form of signal processing where the input is an image and the output may be an image or characteristics/features associated with that image. There are many applications that can benefit from image recognition, image captioning, and scene description. For instance, law enforcement can use the technology to identify suspects from pictures and it can be a good utility for self-driving cars by providing them with a 360 degree camera.
Image processing basically includes the following three steps:
There are two types of methods used for image processing:
Analogue image processing can be used for hard copies like prints and photographs. Image analysts use various fundamentals of interpretation while using these visual techniques.
Digital image processing techniques help in manipulating images by using computers. The three general phases of data during use of digital techniques are pre-processing, enhancement and display, and information extraction.
Images are the best source of perceptual information for human beings. Vision allows humans to receive and understand the world around us.
Image understanding, image analysis, and computer vision aim to mimic? the effects of human vision by digitally perceiving and understanding images.
Image processing methods have started to become increasingly sophisticated and the effort is to develop as much automation as possible. Image processing techniques can be applied to neuroimaging, for example, with the goal to improve detection of abnormal brain tissue, including abnormalities that may not be readily recognizable by visual analysis alone.
Medical Image Processing is the technique of creating visual representations of a body’s interior for medical analysis, intervention, and examination of the function of organs or tissue. Medical imaging attempts to reveal internal structures underneath the skin and bones, as well as to diagnose and treat disease. Medical imaging also sets a database of normal anatomy and physiology to help identify abnormalities.
OCR (optical character recognition) is the technique of converting a scanned document into fully editable and searchable virtual files, transforming paper documents into formats such as Microsoft Word, Excel spreadsheets, CSV files, and searchable PDF documents. Highly useful documents that are safeguarded from physical damage are created as a result of scanning digital files with OCR technology. It even allows businesses to have easier and quicker access to streams of data in a digital format.
Visual information is the most important type of information perceived, processed, and interpreted by the human brain. 33.33% of the human brain’s cortical area processes visual information. Digital image processing carries out automatic processing, manipulation, and interpretation of visual information, and it plays a significant role in many aspects of our daily life, with applications such as in television, photography, robotics, remote sensing, medical diagnosis, and industrial inspection.
Another use of OCR is in banking, where it is utilized to process checks without human intervention. A check can be inserted into a machine where the framework filters the sum to be issued and the right measure of cash is exchanged. This innovation has been idealized for printed checks, and is genuinely precise for handwritten checks diminishing the hold-up time in banks.
Automatic number plate recognition is a mass observation method that uses optical character recognition on pictures to recognize vehicle registration plates. ANPR also stores the pictures caught by the cameras, including the numbers caught from license plates. ANPR innovation own to plate variety from place to place as it is an area particular innovation. ANPR is used by various police forces and as a technique for electronic toll accumulation on pay-per-use streets.
Handwriting recognition is the capacity of a PC to get and translate intelligible handwritten data from sources such as paper records, photos, touch-screens, and different gadgets. The picture of the written content might be detected "off line" from a bit of paper by optical scanning (optical character recognition) or clever word recognition. On the other hand, the developments of the pen tip may be detected "on line," for instance by a pen-based PC screen surface.
To process printed material, Medical services have also seen an expansion in the use of OCR innovation to process printed material. Medical service experts continuously need to manage extensive volumes of documents for each patient, including protection frames and general health forms. To stay aware of all of this data, it is valuable to input relevant information into an electronic database. With OCR processing tools, we can extract data from structures and put it into databases, so that each patient's information is quickly recorded and retrieved when needed in the future.
Receipt imaging is broadly utilized as a part of numerous organizations’ applications to monitor financial records and keep accumulation of payments from heaping up. In government offices and autonomous organizations, OCR simplifies information gathering and analysis, among other procedures.
Legal Industry: The legal industry uses the OCR innovation to digitize documents and enter them into databases. Legal experts can find and search through documents stored in enormous databases by basically typing in a few keywords.
A CAPTCHA is a system that can create and grade tests that humans can pass but current software technology can't. Malicious programmers can make software to misuse personal information on websites. For instance, dictionary attack is an assault against secret word-confirmed frameworks in which a programmer composes a system that runs through a series of common passwords. In CAPTCHA, a picture comprising an arrangement of letters and numbers with a variety of sizes and textual styles, distracting backgrounds, arbitrary portions, highlights, and noise is produced, so that text cannot be read via OCR. Current OCR frameworks can be utilized to evacuate the noise and portion the picture to make the picture tractable by such malicious users.