Future Scope of Image Processing

Keywords: image processing future, image processing improvement

Imaging can be defined as the representation of an objects external form. That definition no longer holds true. More information within an image can be considered. Fluorescent tags, mechanical-biological parameters, internal structures are some of the recent additions. Fabrication while imaging and the characterization of materials as yet undefined can also be part of imaging. The extremely small images can be measured in nanometers also. Future imaging systems are expected to be less expensive. They will have to be easier to use. There are various types of imaging systems such as those used for chemical, optical, thermal, medical and molecular imaging. The use of scanning techniques and statistical analyses for image analysis are needed to extract valid image values. The satellite applications programs of the future will be based on extensive research in the area of imaging. A number of different sensors will be used in the satellites orbiting the earth. Scientifically useful information will be extracted from these systems. New techniques will be needed to organize and classify the different sets of data obtainable from the orbiting satellites. The future trend in remote sensing will be based on sensors that can record the same scene in many different ways. Graphics data will be important in image processing app1ications. Satellite based imaging for planetary exploration as well as military applications will be the future trend. Biomedical applications, astronomy, and scene analysis for the robotic vehicles are also pertinent areas of future applications of imaging4. Adaptive search of large image data bases will become the norms, since video and graphics data will be available from a variety of sensors developed for remote sensing applications of satellite systems. The design and coordination of microscopy imaging techniques for research in molecular biology is gaining importance.

KEY WORDS: future paradigm for imaging techniques, cellular neural network for imaging techniques, Advances in image processing and artificial intelligence, improved sensors for satellite imaging, ultrasound imaging, digital image processing, document and medical imaging, remote sensing.

INTRODUCTION

The advances taking place in broadband wireless devices and in mobile technology used for hand-held devices have several applications in the field of image processing. Internet enables acquisition of instant information. Most of this information is designed for visual consumption in the form of text, graphics, and pictures, or integrated multimedia presentations. Image processing essentially means algorithmic enhancement, manipulation, or analysis (also understanding or recognition) of the digital image data. Image processing can be thought of a form of signal processing for which the input is an image, such as photographs or frames of video. The output of image processing can be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. The acquisition of images is referred to as imaging. Image Processing deals with images which are two-dimensional entities (such as scanned office documents, x-ray films, satellite pictures, etc) captured electronically. The technique of video image processing used to solve problems associated with the real-time road traffic control systems is gaining importance. This has a direct emphasis on the future improvements planned for digital video camera techniques. The nuances of Image Processing and the range of applications in which the technology will be deployed in the future will be of value for planning in this vital area. Image Processing is considered to be one of the most rapidly evolving areas of information technology today, with growing applications in all areas of business. This technology holds the possibility of developing the ultimate machine in the future that would be able to perform the visual functions of human beings. The basis for all kinds of future visual automation is relevant to image processing field. Sophisticated optical sorting systems use image processing to discriminate the colors of an object, thereby visually sorting a product though the use of light sensors. Augmented reality5,7 is a term used for a live direct or indirect view of a physical real-world environment whose elements are merged with (or augmented by) virtual computer-generated imagery, thus creating a mixed reality. The augmentation is conventionally in real-time, such as sports scores on TV during a match. Augmented reality research explores the application of computer-generated imagery in live video streams as a way to expand the real-world. Advanced research includes use of head-mounted displays and virtual retinal displays for visualization purposes, and construction of controlled environments containing any number of sensors and actuators

Traffic data collection under mixed traffic conditions is one of the major problems faced by researchers as well as traffic regulatory authorities. There is a growing demand for road traffic data of all kinds. Increasing congestion problems and problems associated with existing detectors created an interest in such new vehicle detection technologies1. But the systems have difficulties with congestion, shadows and lighting transitions. Problems related to image processing application to road traffic are due to the fact that real world images are to be processed in real time.

Every image processing technique or algorithm takes an input, an image or a sequence of images and produces an output, which may be a modified image and/or a description of the input image contents. Image Processing extracts information from images and integrates it for several applications. There are several fields in which image processing applications are relevant. Medical imaging, industrial applications, remote sensing, space applications, and military applications are a few examples.

IMAGING IN INDUSTRY

The applications in industry include fingerprint or retina recognition, processing records of security or traffic cameras. The applications in medicine include ultrasound imaging, magnetic resonance. Stereography is the art of using two almost identical photographs to create a three-dimensional (3D) image. The viewer requires special glasses or a stereoscope to see the 3D image. With modern technology, it has applications in motion picture and television industry. Stereography is a complicated process. Modern stereography uses specialized computer software and camera hardware. Volumetric displays do not require special goggles. The three-dimensional graphics created by this type of display can be viewed from any angle. Each viewer can observe the picture from a different perspective. To create volumetric graphics, a technique called as swept surface volumetric display, which is based on persistence of vision is adopted. Here use of fast-moving lit surfaces creates the illusion of a solid shape. To display volumetric 3D images there is another option which is called as static volume. No moving parts are used in the visible area of the display. However mirrors and lenses are used to direct a beam of laser light. Very fast pulses of laser light are directed at different points in the air. Persistence of vision gives the illusion of a single solid object. This method is useful for medical diagnosis. A 3D display can show a realistic image of a heart. Architects and builders can visualize a construction project in three dimensions. Future applications include methods of interacting with volumetric displays. Sensors can be used by users to manipulate and adjust the graphics. A camera connected to a display can track an athlete’s motions and rotate the images as needed. These types of volumetric interactions can aid in literally reaching out and touching the three dimensional images of kith and kin separated geographically.

Bio-medical and other applications2 are possible, wherein model building and rendering can convert 2D image to a 3D image by using the mesh skeleton of a component or an organ. Use of 3D image processing to build realistic models for movies and buildings will also become a reality. 3D image processing requires a mesh object. An image processing program helps in creating lines to build up the mesh skeleton. 3D scanner can also be used to capture the information. The mesh skeleton contains volume and depth information so that a 3D model can be developed. Rendering is used to include colors and textures over the 3D model to make it look realistic.

The computer can make use of different 2D screenshots to capture every angle of the model. The user can move the model and it will appear as a 3D image. 3D imaging is a process to render a three-dimensional image on a two-dimensional surface by creating the optical illusion of depth. 3D imaging makes use of two still or motion camera lenses a slight distance apart to photograph a three-dimensional object. The process effectively duplicates the stereoscopic vision of human eyes. The image is reproduced as two flat images that are seen separately, creating a visual illusion of depth. The spot where the left and right images overlap is the point of convergence. As objects in 3D imaging move further from the point of convergence, they appear either closer or further away from the viewer, creating the illusion of depth.

Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies. Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. It is also used in video surveillance. Some recent digital cameras use face detection for autofocus.

The concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. Features are used as a starting point for many computer vision algorithms. The desirable property for a feature detector is repeatability. Whether or not the same feature will be detected in two or more different images of the same scene is going to be important.

Morphological image processing consists of a set of operators that transform images according to certain characterizations. Mathematical morphology is the field of acquisition and processing of image information starting with simple image modifications using point transforms or linear filters and ending up with sophisticated tools and techniques for the analysis and processing of geometrical structures, based on set theory, lattice theory, topology, and random functions. This area also covers the use of digital image processing techniques to process, analyze and present images obtained from a microscope. Such processing is now commonplace in a number of diverse fields such as medicine, biological research, cancer research, drug testing, metallurgy, etc. A number of manufacturers of microscopes now specifically design the features that allow the microscopes to interface to an image processing system.

CONCLUSION

A major challenge for automatic image analysis is that the sheer complexity of the visual task which has been mostly ignored by the current approaches. New technological breakthrough in the areas of digital computation and telecommunication has relevance for future applications of image processing1. The satellite imaging and remote sensing applications programs of the future will feature a variety of sensors orbiting the earth. This technology is required for military and other types of surveillance, statistical data collection in the fields of forestry, agriculture, disaster prediction, weather prediction. In order to extract scientifically useful information, it will be necessary to develop techniques to register real-time data recorded by a variety of sensors for various applications3.

FUTURE SCOPE

The future of image processing will involve scanning the heavens for other intelligent life out in space. Also new intelligent, digital species created entirely by research scientists in various nations of the world will include advances in image processing applications. Due to advances in image processing and related technologies there will be millions and millions of robots in the world in a few decades time, transforming the way the world is managed. Advances in image processing and artificial intelligence6 will involve spoken commands, anticipating the information requirements of governments, translating languages, recognizing and tracking people and things, diagnosing medical conditions, performing surgery, reprogramming defects in human DNA, and automatic driving all forms of transport. With increasing power and sophistication of modern computing, the concept of computation can go beyond the present limits and in future, image processing technology will advance and the visual system of man can be replicated. The future trend in remote sensing will be towards improved sensors that record the same scene in many spectral channels. Graphics data is becoming increasingly important in image processing app1ications. The future image processing applications of satellite based imaging ranges from planetary exploration to surveillance applications.

Using large scale homogeneous cellular arrays of simple circuits to perform image processing tasks and to demonstrate pattern-forming phenomena is an emerging topic. The cellular neural network is an implementable alternative to fully connected neural networks and has evolved into a paradigm for future imaging techniques. The usefulness of this technique has applications in the areas of silicon retina, pattern formation, etc.