Image Processing and its Applications
Image Processing
Image processing is nothing but a method to do some operations on an image, to get an enhanced image, or to get some useful information from it. It is a type of signal processing in which input is an image and output may be an image or characteristics/features associated with that image. Image processing is a rapidly growing technology. It is a core research area in the engineering and computer science discipline.
Steps in Image Processing
- Reading the image via image processing tools.
- Analyzing and applying changes in the image.
- Output is a result of which altered image or report that is based on image analysis.
Tools for Image Processing
OpenCV
Most well-known library, multi-platform, and simple to utilize. It covers all the fundamental strategies and algorithms to play out a few image and video processing tasks, functions admirably with C++ and Python.
Tensorflow
This is the most well-known machine learning and deep learning library today. Its prominence quickly increased and outperformed existing libraries because of the simplicity of the API. TensorFlow is a free open-source library for data streams and differential programming. It is a symbolic math library that is additionally utilized for machine learning applications, for example, neural networks.
TensorFlow 2.0 encourages the execution of pre-prepared models that are tuned for picture and speech recognition, object detection, recommendations, reinforced learning, and so forth. Such reference models permit you to utilize unique best practices and fill in as beginning stages for building up your own elite solutions.
Matlab
Matlab is an extraordinary tool for making image processing applications and is generally utilized in research as it permits quick prototyping. Another fascinating perspective is that Matlab code is very concise when compared with C++, making it simpler to peruse and troubleshoot. It handles errors before execution by proposing a few different ways to make the code faster.
Keras
Keras is a deep learning Python library that combines the elements of different libraries, for example, Tensorflow, Theano, and CNTK. Keras has a favorable position over contenders, for example, Scikit-learn and PyTorch, as it runs on top of Tensorflow.
Keras can run on TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Intended for quick experimentation with deep neural networks, it centers around convenience, measured quality, and extensibility. Keras follows best practices for decreasing cognitive load: it offers steady and basic APIs and limits the number of user actions required for regular use cases.
Image Processing Applications
- Agriculture
One can use Image Processing techniques towards developing applications for sorting the fruits, determining the fruit's quality, disease identification in crops, weed recognition, species identification, etc.
2. Automobiles
In the automobile sector, image processing has many applications and particularly used in safety-critical applications. Lane detection, number plate detection, toll collection systems, accent impact assessment, driver assistant systems, rear and front cameras for parking applications, and many more. Many researchers are working actively towards building safer and smarter image processing-based automobile applications.
3. Industry
In the Industry, image processing has a million applications. It starts from quality control applications, inspection applications, fault detection, robotic guidance and control, defective parts identification, color identification, etc. Image processing has a key role in Industry 4.0.
4. Medicine
Image processing has found some fantastic applications in the medical field. Image processing can be used for diagnostic purposes. The x-ray or scan images can be read and understood to see the variations or symptoms for many diseases. Recent developments have ensured that the robots can perform surgery which has image processing as a very important component. Cancer cell detection, tumor detection, stent placement, etc are many such applications.
5. Defense
Image processing has vast spread applications in the defense industry. It has been used in defense and security-related applications. Few applications are target detection and target tracking, missile guidance, navigation, etc.