Abstract: This paper is based on the image processing, image as we know is the two dimensional signal and there are various other parameters related to the image processing. Image processing is a very important tool in the modern times because it is very essential to process the image to get the clear cut picture of what actually it depicts. Processing involves various process to be done by the multiplication, addition and various other arithmetic operations. The paper explains various operations that are involved during the processing of the signals. After explaining the operations involved in the image processing various applications have been described in the end. There are wide range of applications of the image processing, in the medical technology e.g. in the MRI scanning, image processing is an essential tool which gives us the idea that what actually is inside the body and gives the complete details of the organ. In the same way image processing is used in other areas.
An image may be defined as a two dimensional function, f(x,y) where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x,y) is called the intensity or gray level of the image at that point. When x,y, and the intensity values of f are all infinite, discrete quantities, we call the image a digital image. The field of digital image processing refers to the processing digital images by means of a digital computer. Digital image is composed of finite number of elements each of which has a particular location and value. These elements are called picture elements, image elements, pels, and pixels. Pixel is the term used most widely to denote the elements of a digital image. Vision is the moost advanced of our senses so it is not surprising that images play the single most important role in human perception. However unlike humans who are limited to the visual band of the electromagnetic spectrum image machines cover almost the entire electromagnetic spectrum ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultrasound, electron microscopy, and computer generated images. Thus digital image processing encompasses a wide and varied field of applications. Image processing is sometimes defined as the area in which both the input as well as the output is an image. But this definition sometimes dos not completely explain the phenomenon of the digital signal processing for e.g. under this definition even the trivial taskof computing the average intensity of an image( which yields a single number) would not be considered an image processing operation. On the other hand these are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. The area itself is a branch of artificial intelligence whose objective is to elumate human intelligence the field of artificial intelligence is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis also called image understanding is in between image processing and computer vision.
Operations in image processing:
ngeometry, anaffine transformationoraffine mapor anaffinity(from the Latin,affinis, "connected with") between twovector spaces(strictly speaking, twoaffine spaces) consists of alinear transformationfollowed by atranslation:
In the finite-dimensional case each affine transformation is given by a matrix A and a vectorb, satisfying certain properties described below.Geometrically, an affine transformation inEuclidean spaceis one that preservesThecollinearityrelation between points; i.e., three points which lie on a line continue to be collinear after the transformationRatios of distances along a line; i.e., for distinct collinear pointsp1,p2,p3, the ratio|p2-p1| / |p3-p2|is preservedIn general, an affine transformation is composed of linear transformations (rotation,scalingorshear) and a translation (or "shift"). Several linear transformations can be combined into a single one, so that the general formula given above is still applicable.
An affine transformation isinvertibleif and only ifAis invertible. In the matrix representation, the inverse is:
The invertible affine transformations form theaffine group, which has thegeneral linear groupof degreenas subgroup and is itself a subgroup of the general linear group of degreen+ 1.
Thesimilarity transformationsform the subgroup whereAis a scalar times anorthogonal matrix. If and only if the determinant ofAis 1 or -1 then the transformation preserves area; these also form a subgroup. Combining both conditions we have theisometries, the subgroup of both whereAis an orthogonal matrix. Each of these groups has a subgroup of transformations which preserveorientation: those where the determinant ofAis positive. In the last case this is in 3D the group ofrigid bodymotions (proper rotationsand pure translations). For any matrixAthe following propositions are equivalent: A-Iis invertible Adoesnothave aneigenvalueequal to 1 for allbthe transformation has exactly onefixed point there is abfor which the transformation has exactly one fixed point affine transformations with matrixAcan be written as a linear transformation with some point as origin If there is a fixed point, we can take that as the origin, and the affine transformation reduces to a linear transformation. This may make it easier to classify and understand the transformation. For example, describing a transformation as a rotation by a certain angle with respect to a certain axis is easier to get an idea of the overall behavior of the transformation than describing it as a combination of a translation and a rotation. However, this depends on application and context. Describing such a transformation for anobjecttends to make more sense in terms of rotation about an axis through the center of that object, combined with a translation, rather than by just a rotation with respect to some distant point. As an example: “move 200 m north and rotate 90 anti-clockwise“, rather than the equivalent ”with respect to the point 141 m to the northwest, rotate 90 anti-clockwise”. Affine transformations in 2D without fixed point (so whereAhas eigenvalue 1) are: pure translations scalingin a given direction, with respect to a line in another direction (not necessarily perpendicular), combined with translation that is not purely in the direction of scaling; thescale factoris the other eigenvalue; taking “scaling” in a generalized sense it includes the cases that the scale factor is zero (projection) and negative; the latter includesreflection, and combined with translation it includesglide reflection. shearcombined with translation that is not purely in the direction of the shear (there is no other eigenvalue than 1; it has algebraicmultiplicity2, but geometric multiplicity 1)
color correctionby usingcolor gels, or filters, is a process used instage lighting,photography,television,cinematographyand other disciplines, the intention of which is to alter the overall color of the light; typically the light color is measured on a scale known ascolor temperature, as well as along agreenmagentaaxis orthogonal to the color temperature axis.
Without color correction gels, a scene may have a mix of various colors. Applying color correction gels in front of light sources can alter the color of the various light sources to match. Mixed lighting can produce an undesirable aesthetic when displayed on a television or in a theatre.
Conversely, gels may also be used to make a sceneappearmore natural by simulating the mix of color temperatures that occur naturally. This application is useful especially where motivated lighting(lending the impression that it isdiegetic) is the goal. Color gels may also be used to tint lights for artistic effect.
Digital compositingis the process of digitally assembling multiple images to make a final image, typically for print,motion picturesor screen display. It is the evolution into the digital realm of optical filmcompositing.
The basic operation used is known as ‘alpha blending', where an opacity value, ‘a' is used to control the proportions of two inputpixelvalues that end up a single output pixel. Consider three pixels;<>a foreground pixel, f
a background pixel, b
a composited pixel, c
and a, the opacity value of the foreground pixel. (a=1 for opaque foreground, a=0 for a completely transparent foreground). A monochrome raster image where the pixel values are to be interpreted as alpha values is known as amatte. Then, considering all three colour channels, and assuming that the colour channels are expressed in a ?=1 colour space (that is to say, the measured values are proportional to light intensity), we have:
cr= a fr+ (1 - a) br
cg= a fg+ (1 - a) bg
cb= a fb+ (1 - a) bb
Note that if the operations are performed in a colour space where ? is not equal to 1 then the operation will lead to non-linear effects which can potentially be seen asaliasingartifacts (or ‘jaggies') along sharp edges in the matte. More generally, nonlinear compositing can have effects such as “halos” around composited objects, because the influence of the alpha channel is non-linear. It is possible for a compositing artist to compensate for the effects of compositing in non-linear space. Performing alpha blending is an expensive operation if performed on an entire image or 3D scene. If this operation has to be done in real time video games there is an easy trick to boost performance.
cout= a fin+ (1 - a) bin
cout= a fin+ bin- a bin
cout= bin+ a (fin- bin)
By simply rewriting the mathematical expression one can save 50% of the multiplications required.
incomputer vision, sets ofdataacquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems.Image registrationis the process of transforming the different sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from different measurements.
Medical imageregistration (for data of the same patient taken at different points in time) often additionally involveselastic(also known asnonrigid) registration to cope with deformation of the subject (due to breathing, anatomical changes, and so forth). Nonrigid registration of medical images can also be used to register a patient's data to an anatomical atlas, such as theTalairachatlas for neuroimaging.
Intensity-based vs feature-based:
Image registration or image alignment algorithms can be classified into intensity-based and feature-base One of the images is referred to as thereferenceorsourceand the second image is referred to as thetargetorsensed.Image registration involves spatially transforming the target image to align with the reference image.Intensity-based methods compare intensity patterns in images via correlation metrics, while feature-based methods find correspondence between image features such as points, lines, and contours. Intensity-based methods register entire images or subimages. If subimages are registered, centers of corresponding sub images are treated as corresponding feature points. Feature-based method established correspondence between a number of points in images. Knowing the correspondence between a number of points in images, a transformation is then determined to map the target image to the reference images, thereby establishing point-by-point correspondence between the reference and target images
Image registration algorithms can also be classified according to the transformation models they use to relate the target image space to the reference image space. The first broad category of transformation models includeslinear transformations, which include translation, rotation, scaling, and other affine transforms.Linear transformationsare global in nature, thus, they cannot model local geometric differences between images.
The second category of transformations allow ‘elastic' or ‘nonrigid' transformations. These transformations are capable of locally warping the target image to align with the reference image. Nonrigid transformations include radial basis functions (thin-plate or surface splines, multiquadrics, and compactly-supported transformations), physical continuum models (viscous fluids), and large deformation models (diff eomorphisms).
Spatial vs. frequency domain methods
Spatial methods operate in the image domain, matching intensity patterns or features in images. Some of the feature matching algorithms are outgrowths of traditional techniques for performing manual image registration, in which an operator chooses correspondingcontrol points(CPs) in images. When the number of control points exceeds the minimum required to define the appropriate transformation model, iterative algorithms likeRANSACcan be used to robustly estimate the parameters of a particular transformation type (e.g. affine) for registration of the images.
Frequency-domain methods find the transformation parameters for registration of the images while working in the transform domain. Such methods work for simple transformations, such as translation, rotation, and scaling. Applying thePhase correlationmethod to a pair of images produces a third image which contains a single peak. The location of this peak corresponds to the relative translation between the images. Unlike many spatial-domain algorithms, the phase correlation method is resilient to noise, occlusions, and other defects typical of medical or satellite images. Additionally, the phase correlation uses thefast Fourier transformto compute the cross-correlation between the two images, generally resulting in large performance gains. The method can be extended to determine rotation and scaling differences between two images by first converting the images tolog-polarcoordinates. Due to properties of theFourier transform, the rotation and scaling parameters can be determined in a manner invariant to translation.
Single- vs. multi-modality methods
Another classification can be made between single-modality and multi-modality methods. Single-modality methods tend to register images in the same modality acquired by the same scanner/sensor type, while multi-modality registration methods tended to register images acquired by different scanner/sensor types.
Multi-modality registration methods are often used inmedical imagingas images of a subject are frequently obtained from different scanners. Examples include registration of brainCT/MRI images or whole bodyPET/CTimages for tumor localization, registration of contrast-enhancedCTimages against non-contrast-enhancedCTimages for segmentation of specific parts of the anatomy, and registration ofultrasoundandCTimages forprostatelocalization inradiotherapy.
Automatic vs. interactive methods
Registration methods may be classified based on the level of automation they provide.Manual, interactive, semi-automatic, and automatic methodshave been developed. Manual methods provide tools to align the images manually. Interactive methods reduce user bias by performing certain key operations automatically while still relying on the user to guide the registration. Semi-automatic methods perform more of the registration steps automatically but depend on the user to verify the correctness of a registration. Automatic methods do not allow any user interaction and perform all registration steps automatically.
Image differencingis animage processingtechnique used to determine changes between images. The difference between two images is calculated by finding the difference between each pixel in each image, and generating an image based on the result. For this technique to work, the two images must first be aligned so that corresponding points coincide, and their photometric values must be made compatible, either by careful calibration, or by post-processing. The complexity of the pre-processing needed before differencing varies with the type of image.
Image differencing techniques are commonly used inastronomyto locate objects that fluctuate in brightness or move against the star field.
TheHutchinson metriccan be used to “measureof the discrepancy between twoimagesfor use infractalimage processing”.
Segmentation ( image processing ):
incomputer vision,segmentationrefers to the process of partitioning adigital imageinto multiplesegments(setsofpixels) (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 visual characteristics.
The result of image segmentation is a set of segments that collectively cover the entire image, or a set ofcontoursextracted from the image (seeedge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such ascolor,intensity, ortexture.Adjacentregions are significantly different with respect to the same characteristic(s).
High dynamic range imaging:
Inimage processing,computer graphics, andphotography,high dynamic range imaging(HDRIor justHDR) is a set of techniques that allow a greaterdynamic rangeofluminances between the lightest and darkest areas of an image than standard digital imaging techniques or photographic methods. This wider dynamic range allows HDR images to more accurately represent the wide range of intensity levels found in real scenes, ranging from direct sunlight to faint starlight.
The two main sources of HDR imagery arecomputer renderingsand merging of multiple photographs, which in turn are known as low dynamic range (LDR)or standard dynamic range (SDR)images.Tone mappingtechniques, which reduce overall contrast to facilitate display of HDR images on devices with lower dynamic range, can be applied to produce images with preserved or exaggerated local contrast for artistic effect.
Applications of image processing:
Today there is almost no technical area that is not impacted in some way by digital image processing. The areas of application of digital image processing are so varied that some form of organization is desirable in attempting to capture the breath of this field. One of the simplest ways is to develop a basic understanding of the extent of image processing applications to categories images according to their source. This principal energy source for images in use today is the electromagnetic spectrum.
Some of the areas where image processing is mostly used is:
- Computer vision
- Augmented Reality
- Face detection
- Feature detection
- Lane departure warning system
- Non-photorealistic rendering
- Medical image processing
- Microscope image processing
- Morphological image processing
- Remote sensing
Computer visionis the science and technology of machines that see. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models to the construction of computer vision systems. Examples of applications of computer vision include systems for: Controlling processes (e.g., anindustrial robotor anautonomous vehicle). Detecting events (e.g., for visual surveillance orpeople counting). Organizing information (e.g., for indexing databases of images and image sequences). Modeling objects or environments (e.g. industrial inspection, medical image analysis or topographical modeling). Interaction (e.g., as the input to a device forcomputer-human interaction).
Computer vision is closely related to the study ofbiological vision. The field of biological vision studies and models the physiological processes behind visual perception in humans and other animals. Computer vision, on the other hand, studies and describes the processes implemented in software and hardware behind artificial vision systems. Interdisciplinary exchange between biological and computer vision has proven fruitful for both fields. Computer vision is, in some ways, the inverse ofcomputer graphics. While computer graphics produces image data from 3D models, computer vision often produces 3D models from image data. There is also a trend towards a combination of the two disciplines, e.g., as explored inaugmented reality. Sub-domains of computer vision include scene reconstruction, event detection,video tracking,object recognition, learning, indexing,motion estimation, andimage restoration.
Augmented reality(AR) is a term for a live direct or indirect view of a physical real-world environment whose elements are merged with (oraugmented by)virtualcomputer-generated imagery- creating amixed reality. The augmentation is conventionally inreal-timeand in semantic context with environmental elements, such as sports scores on TV during a match. With the help of advanced AR technology (e.g. addingcomputer visionandobject recognition) the information about the surrounding real world of the user becomesinteractiveand digitally usable. Artificial information about the environment and the objects in it can be stored and retrieved as an information layer on top of the real world view. The term augmented reality is believed to have been coined in 1990 by Thomas Caudell, an employee of Boeing at the time. 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 ofhead-mounted displaysandvirtual retinal displaysfor visualization purposes, and construction of controlled environments containing any number of sensors andactuators.
Face detection can be regarded as a specific case ofobject-class detection; In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars. Face detection can be regarded as a more general case offace localization; In face localization, the task is to find the locations and sizes of aknownnumber of faces (usually one). In face detection, one does not have this additional information. 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. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation),or both.
Incomputer visionandimage processingthe concept offeature detectionrefers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is animage featureof a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions.
There is no universal or exact definition of what constitutes a feature, and the exact definition often depends on the problem or the type of application. Given that, a feature is defined as an "interesting" part of animage, and features are used as a starting point for many computer vision algorithms. Since features are used as the starting point and main primitives for subsequent algorithms, the overall algorithm will often only be as good as its feature detector. Consequently, the desirable property for a feature detector isrepeatability: whether or not the same feature will be detected in two or more different images of the same scene. Feature detection is a low-levelimage processingoperation. That is, it is usually performed as the first operation on an image, and examines every pixelto see if there is a feature present at that pixel. If this is part of a larger algorithm, then the algorithm will typically only examine the image in the region of the features. As a built-in pre-requisite to feature detection, the input image is usually smoothed by aGaussiankernel in ascale-spacerepresentation and one or several feature images are computed, often expressed in terms of localderivativeoperations. Occasionally, when feature detection iscomputationally expensiveand there are time constraints, a higher level algorithm may be used to guide the feature detection stage, so that only certain parts of the image are searched for features. Where many computer vision algorithms use feature detection as the initial step, so as a result, a very large number of feature detectors have been developed. These vary widely in the kinds of feature detected, the computational complexity and the repeatability. At an overview level, these feature detectors can (with some overlap) be divided into the following groups.
Lane departure warning system:
In road-transport terminology, alane departure warning systemis a mechanism designed to warn a driver when the vehicle begins to move out of its lane(unless aturn signalis on in that direction) onfreewaysandarterial roads. These systems are designed to minimize accidents by addressing the main causes of collisions: driving error, distraction and drowsiness. In 2009 theNHTSAbegan studying whether to mandate lane departure warning systems andfrontal collision warning systemson automobiles.
There are two main types of systems:
Systems which warn the driver if the vehicle is leaving its lane. ( visual, audible, and/or vibration warnings)systems which warn the driver and if no action is taken automatically take steps to ensure the vehicle stays in its lane. The first production lane departure warning system in Europe was developed by theUnited States Iteriscompany forMercedes Actroscommercial trucks. The system debuted in 2000 and is now available on most trucks sold in Europe. In 2002, the Iteris system became available onFreightliner Trucks ' trucks in North America. In all of these systems, the driver is warned of unintentional lane departures by an audiblerumble stripsound generated on the side of the vehicle drifting out of the lane. If a turn signal is used, no warnings are generated.
medical imagingis the technique and process used to createimagesof the human body (or parts and function thereof) for clinical purposes (medical proceduresseeking to reveal, diagnoseor examinedisease) or medical science (including the study of normalanatomyandphysiology). As a discipline and in its widest sense, it is part ofbiological imagingand incorporatesradiology(in the wider sense),nuclear medicine, investigativeradiological sciences,endoscopy, (medical)thermography, medical photography andmicroscopy(e.g. for human pathological investigations). Measurement and recording techniques which are not primarily designed to produceimages, such aselectroencephalography(EEG),magnetoencephalography(MEG),Electrocardiography(EKG) and others, but which produce data susceptible to be represented asmaps(i.e. containing positional information), can be seen as forms of medical imaging.
Microscope image processing:
Until the early 1990s, most image acquisition in video microscopy applications was typically done with an analog video camera, often simply closed circuit TV cameras. While this required the use of aframe grabbertodigitizethe images, video cameras provided images at full video frame rate (25-30 frames per second) allowing live video recording and processing. While the advent of solid state detectors yielded several advantages, the real-time video camera was actually superior in many respects. Today, acquisition is usually done using aCCDcameramounted in the optical path of the microscope. The camera may be full colour or monochrome. Very often, very high resolution cameras are employed to gain as much direct information as possible.Cryogeniccooling is also common, to minimise noise. Often digital cameras used for this application providepixel intensity data to a resolution of 12-16 bits, much higher than is used in consumer imaging products. Ironically, in recent years, much effort has been put into acquiring data atvideo rates, or higher (25-30 frames per second or higher). What was once easy with off-the-shelf video cameras now requires special, high speed electronics to handle the vast digital data bandwidth. Higher speed acquisition allows dynamic processes to be observed in real time, or stored for later playback and analysis. Combined with the high image resolution, this approach can generate vast quantities of raw data, which can be a challenge to deal with, even with a moderncomputersystem. It should be observed that while current CCD detectors allow very highimage resolution, often this involves a trade-off because, for a given chip size, as the pixel count increases, the pixel size decreases. As the pixels get smaller, their well depth decreases, reducing the number of electrons that can be stored. In turn, this results in a poorersignal to noise ratio. For best results, one must select an appropriate sensor for a given application. Because microscope images have an intrinsic limiting resolution, it often makes little sense to use a noisy, high resolution detector for image acquisition. A more modest detector, with larger pixels, can often produce much higher quality images because of reduced noise. This is especially important in low-light applications such asfluorescence microscopy. Moreover, one must also consider the temporal resolution requirements of the application. A lower resolution detector will often have a significantly higher acquisition rate, permitting the observation of faster events. Conversely, if the observed object is motionless, one may wish to acquire images at the highest possible spatial resolution without regard to the time required to acquire a single image.
Remote sensingis the small or large-scale acquisition of information of an object or phenomenon, by the use of either recording or real-time sensing device(s) that arewireless, or not in physical or intimate contact with the object (such as by way ofaircraft,spacecraft,satellite,buoy, orship). In practice, remote sensing is the stand-off collection through the use of a variety of devices for gathering information on a given object or area. Thus,Earth observationorweather satellitecollection platforms, ocean and atmospheric observingweather buoyplatforms, the monitoring of a parolee via anultrasoundidentification system, Magnetic Resonance Imaging(MRI),Positron Emission Tomography(PET),X-radiation(X-RAY) andspace probesare all examples of remote sensing. In modern usage, the term generally refers to the use of imaging sensor technologies including: instruments found in aircraft and spacecraft as well as those used inelectrophysiology, and is distinct from other imaging-related fields such asmedical imaging. There are two kinds of remote sensing. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding area being observed. Reflected sunlight is the most common source of radiation measured by passive sensors. Examples of passive remote sensors include filmphotography ,Infrared,charge-coupled devices, andradiometers. Active collection, on the other hand, emits energy in order to scan objects and areas whereupon a sensor then detects and measures the radiation that is reflected or backscattered from the target.RADARis an example of active remote sensing where the time delay between emission and return is measured, establishing the location, height, speed and direction of an object. Remote sensing makes it possible to collect data on dangerous or inaccessible areas. Remote sensing applications include monitoringdeforestationin areas such as theAmazon Basin, the effects ofclimate changeonglaciersand Arctic and Antarctic regions, anddepth soundingof coastal and ocean depths. Military collection during thecold warmade use of stand-off collection of data about dangerous border areas. Remote sensing also replaces costly and slow data collection on the ground, ensuring in the process that areas or objects are not disturbed. Orbital platforms collect and transmit data from different parts of theelectromagnetic spectrum, which in conjunction with larger scale aerial or ground-based sensing and analysis, provides researchers with enough information to monitor trends such asEl Nioand other natural long and short term phenomena. Other uses include different areas of theearth sciencessuch asnatural resource management, agricultural fields such as land usage and conservation, and national security and overhead, ground-based and stand-off collection on border areas.
Image processing is the most important application in the modern electronic world, there are lot reasons behind the processing the image signals. There are lot of errors in the image when It is taken by the equipment e.g. signal distractions and there are lot of noise signals amplified along with the two dimensional signals which need to be attenuated before being given to the output. Signal processing is also needed in remote sensing because the signals which are received by the satellites are just an idea about what could be at the remote end so this signals needs to be processd.
- prokies tata mgraw hill publication
- sanjay sharma