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A STUDTHE IMPORTANCE OF IMAGE PROCESSING AND ITS APLLICATIONS

Profile image of Dr. Basavaprasad BDr. Basavaprasad B

Abstract

A systematic study on importance of image processing and its applications to the field of computer vision is carried out in this paper. An image is defined as an array, or a matrix, of square pixels (elements of picture) arranged in rows and columns. Image processing is a procedure of converting an image into digital form and carry out some operation on it, in order to get an improved image and take out several helpful information from it. Mathematically image processing is defined as the processing of a two dimensional picture by a computer i.e., an image is defined as a function of two real variables, like t(x, y) with an amplitude such as brightness of an image at the coordinate point (a, b). The outcome of image processing can be an image or a set of features or characteristics related to the image. Most image processing methods treats an image as a two-dimensional signal and implementing standard signal-processing techniques to it. The goal of this operation can be divided into 3 categories. Firstly image processing in which input is an image and output is also an image; secondly image analysis in which input is an image and output are the dimensions or measurements. Finally image understanding in which input is an image and output is the standard description of an image. Some of the important applications of image processing in the field of science and technology include computer vision, remote sensing, feature extraction, face detection, forecasting, optical character recognition, finger-print detection, optical sorting, argument reality, microscope imaging, lane departure caution system, Non-photorealistic representation, medical image processing, and morphological imaging. Key Words: Image, Digital Image, Compression, Enhancement, OCR, ATR.

Figures (12)
Figure- 2: Flowchart of Image Processing Progression [6]. 2.1 Image Enhancement: Image enhancementencompasses the processes of changing images, whether they are traditional photochemical photographs, digital photographs or illustrations. Conventional analog image enhancing is known as photo retouching, using tools such as an airbrush to change photographs, or editing design with any medium of Traditional art. Graphic software programs, which can be broadly grouped into raster graphics editors, and Three Dimensional modelers and vector graphics editors are the primary tools with which a user may influence, enhance, and transform images. Several image editing programs are also used to render or create computer art from scratch [1].
Figure- 2: Flowchart of Image Processing Progression [6]. 2.1 Image Enhancement: Image enhancementencompasses the processes of changing images, whether they are traditional photochemical photographs, digital photographs or illustrations. Conventional analog image enhancing is known as photo retouching, using tools such as an airbrush to change photographs, or editing design with any medium of Traditional art. Graphic software programs, which can be broadly grouped into raster graphics editors, and Three Dimensional modelers and vector graphics editors are the primary tools with which a user may influence, enhance, and transform images. Several image editing programs are also used to render or create computer art from scratch [1].
2.3 Image C ompression: Image restoration is the operation of taking a noisy/ corrupted image and estimates the clean creative image. Altered highlig method form may come in many forms such blur, noise, and camera miss-focus. Image res different from image enhancement. The latter is designed to ht characteristics of the image which make the image more agreeable to the viewer, but not essentially to construct practical data from a scientific sense. Image en s like stretching contrast, de-blurring by neighbor process supplied by imaging packages priori model of the method that created the image as motion toration is hancement a nearest do not use 16].
2.3 Image C ompression: Image restoration is the operation of taking a noisy/ corrupted image and estimates the clean creative image. Altered highlig method form may come in many forms such blur, noise, and camera miss-focus. Image res different from image enhancement. The latter is designed to ht characteristics of the image which make the image more agreeable to the viewer, but not essentially to construct practical data from a scientific sense. Image en s like stretching contrast, de-blurring by neighbor process supplied by imaging packages priori model of the method that created the image as motion toration is hancement a nearest do not use 16].
2.2 Image Restoration:
2.2 Image Restoration:
The objective ofimage compression is to decrease insignificance and idleness of the image data in order to be able to store or transmit data in incompetent form. Image compression may be lossy. Lossless density is favored for archival reasons and frequently for medical image processing, technical drawings, clipart or comics. Lossy compression techniques, particularly when used at short-bit rates, introduce compression artifacts. Lossy methods are especially fitting for normal images such as snaps in appliances where slight sometimes unnoticeable loss of loyalty is suitable to attain a extensive decline in bit rate. The lossy compression which constructs unnoticeable differences may be called visually lossless [2].
The objective ofimage compression is to decrease insignificance and idleness of the image data in order to be able to store or transmit data in incompetent form. Image compression may be lossy. Lossless density is favored for archival reasons and frequently for medical image processing, technical drawings, clipart or comics. Lossy compression techniques, particularly when used at short-bit rates, introduce compression artifacts. Lossy methods are especially fitting for normal images such as snaps in appliances where slight sometimes unnoticeable loss of loyalty is suitable to attain a extensive decline in bit rate. The lossy compression which constructs unnoticeable differences may be called visually lossless [2].
Biometrics (orbiometric verification) refers to the automatic identification of humans by their behaviors or characteristics. Biometrics is recycled in computer science as a type of identification and access control. It is also used to recognize individuals in groups’ that are under surveillance. Biometric identifiers are the exceptional, assessable characteristics used to label and describe individuals. Biometric identifiers are habitually classified as physiological © versus __ behavioral characteristics. Physiological uniqueness is related to the nature of the body. Few examples includes fingerprint, face recognition, Palm print, DNA,hand — geometry, iris recognition, retina and odor/smell. Behavioral characteristics are related to the pattern of performance of a person, including but not limited to typing rhythm, voice and gait. Some researchers have coined the term behavior metrics to describe the latter class of biometrics. The Fingerprint Verification antagonism(FVC) is an intercontinental competition focused on fingerprint verification software assessment. A subgroup of impressions of fingerprint obtained with different sensors was offered to registered members are allow to adjust the parameters of respective algorithms. Members were appealed to offer enroll and match executable records of their algorithms; the evaluation was carry out at the organizers’ amenities using the submitted effective files on a confiscated database obtained with the identical sensors as the training set.
Biometrics (orbiometric verification) refers to the automatic identification of humans by their behaviors or characteristics. Biometrics is recycled in computer science as a type of identification and access control. It is also used to recognize individuals in groups’ that are under surveillance. Biometric identifiers are the exceptional, assessable characteristics used to label and describe individuals. Biometric identifiers are habitually classified as physiological © versus __ behavioral characteristics. Physiological uniqueness is related to the nature of the body. Few examples includes fingerprint, face recognition, Palm print, DNA,hand — geometry, iris recognition, retina and odor/smell. Behavioral characteristics are related to the pattern of performance of a person, including but not limited to typing rhythm, voice and gait. Some researchers have coined the term behavior metrics to describe the latter class of biometrics. The Fingerprint Verification antagonism(FVC) is an intercontinental competition focused on fingerprint verification software assessment. A subgroup of impressions of fingerprint obtained with different sensors was offered to registered members are allow to adjust the parameters of respective algorithms. Members were appealed to offer enroll and match executable records of their algorithms; the evaluation was carry out at the organizers’ amenities using the submitted effective files on a confiscated database obtained with the identical sensors as the training set.
Automatic target recognition (ATR) is the skil for an algorithm or device to distinguish objects or targets stand on data gained from sensors. The function of regular target recognition technology is a serious element o f robotic warfare. ATR machines are used in unmanned aerial vehic (Auto Missi launc Attac missi the IFF trans at Urbana-Champaign wi shown that it is possib radar multistate, p e of Standoff, whic k Missile- Expand e. The fundamental es and cruise missiles. Electric affords an matic Target Recognition Unit) to the Land Attack h processes post-launch and pre- ponder. Researchers at the University of th the support of DARPA have h aiming data, allows high quickness in comparison, and permits the SLAM-ER i.e., Standof: ed Response, "Fire-and- version of an ATR sys e to build asynthetic a ATRU video f Land forget" tem is llinois perture image(RAD) of an aircraft target using passive erhaps detailed enough to enable invo untary ATR (Automatic Target Recognition. Other applications of include a proposed security system that uses active UWB radar signals to recognize objects or humans that have dropped onto channel tracks of rail. It is also possible to detect the damaged infrastructures caused by the earthquakes using satellite [10]. ATR to computer vision andimage processing that deals with 2.8 Object Recognition:
Automatic target recognition (ATR) is the skil for an algorithm or device to distinguish objects or targets stand on data gained from sensors. The function of regular target recognition technology is a serious element o f robotic warfare. ATR machines are used in unmanned aerial vehic (Auto Missi launc Attac missi the IFF trans at Urbana-Champaign wi shown that it is possib radar multistate, p e of Standoff, whic k Missile- Expand e. The fundamental es and cruise missiles. Electric affords an matic Target Recognition Unit) to the Land Attack h processes post-launch and pre- ponder. Researchers at the University of th the support of DARPA have h aiming data, allows high quickness in comparison, and permits the SLAM-ER i.e., Standof: ed Response, "Fire-and- version of an ATR sys e to build asynthetic a ATRU video f Land forget" tem is llinois perture image(RAD) of an aircraft target using passive erhaps detailed enough to enable invo untary ATR (Automatic Target Recognition. Other applications of include a proposed security system that uses active UWB radar signals to recognize objects or humans that have dropped onto channel tracks of rail. It is also possible to detect the damaged infrastructures caused by the earthquakes using satellite [10]. ATR to computer vision andimage processing that deals with 2.8 Object Recognition:
2.10 Traffic M onitoring:
2.10 Traffic M onitoring:
The current disclosure relates to a number of invention heading for, normally to the application of image processing tec hniques to traffic data acquisition usi ng images/videos. The inventions exist in a system of traffic monitoring, the fundamental job of which is for acquisition of traffic data and detection of incident. Further application of image processing methods for the vehicle detection, from the series of video images, as well as the acq an tec uisition of traffic data and detection of individual facet, the present develo distinctively, the traffic incident. In pment provides a hnique of processing images recognized from a system of traffic monitoring which is video based. In one more feature, the current development is headed to a Region of Interest i.e, ROI for judgment of a vehicle which is moving and a added feature is directed night position in monitoring a traffic application of a variety of algorithms to a traffic monitoring system based on video is also measured imaginative. Other creative characteristic of the present mo system is sketched in the asserts. to a technique of detecting day or system. It is the nitoring of traffic 2.11 Face Detection:
The current disclosure relates to a number of invention heading for, normally to the application of image processing tec hniques to traffic data acquisition usi ng images/videos. The inventions exist in a system of traffic monitoring, the fundamental job of which is for acquisition of traffic data and detection of incident. Further application of image processing methods for the vehicle detection, from the series of video images, as well as the acq an tec uisition of traffic data and detection of individual facet, the present develo distinctively, the traffic incident. In pment provides a hnique of processing images recognized from a system of traffic monitoring which is video based. In one more feature, the current development is headed to a Region of Interest i.e, ROI for judgment of a vehicle which is moving and a added feature is directed night position in monitoring a traffic application of a variety of algorithms to a traffic monitoring system based on video is also measured imaginative. Other creative characteristic of the present mo system is sketched in the asserts. to a technique of detecting day or system. It is the nitoring of traffic 2.11 Face Detection:
3. MEDICAL APPLICATIONS
3. MEDICAL APPLICATIONS
Figure-4: Flowchart for Brain tumor segmentation Process
Figure-4: Flowchart for Brain tumor segmentation Process
4. GOALS OF IMAGE PROCESSING
4. GOALS OF IMAGE PROCESSING

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