Image processing

The early trade of color image processing of digital images was in the newspaper industry and images were transmitted by submarine cables between London and New York. In early processing digital image produced a coded tape by a telegraph printer. In the mid trade of color image processing which improves to the Bartlane system resulted in higher quality images and photographic process. Digital image processing used in medical imaging, remote earth resources and astronomy. Today's trade of digital image processing has grow vigorously. Today it is used in geography, biology, nuclear machine, archeology, law enforcement, defence and industry.

the image processing techniques. Image enhancement would increase the sharpness and intensity of the underwater images. By increasing the factors of the pixels, the object can be visible clearly in the underwater images. Photograph fusion is the mixture of two or extra unique graphics to kind new snapshot through making use of a detailed algorithm. Photograph fusion is the approach of producing a single fused snapshot utilising a set of input pics which are assumed to be registered. Enter image would be multi-sensor, multimodal, multifocal or multi-temporal. This paper grants a literature overview on one of the most photo fusion procedures and photograph enhancement systems. The input image is first pre-processed. Then HWD transform is applied on it for sharpening the image. The low- frequency background is removed using a highpass filter. Image histograms are then mapped based on the intermediate color channel to reduce the gap between the inferior and dominant color channels. Then Wavelet fusion is applied followed by adaptive local histogram specification process. The resultant images processed through the proposed approach could be further used for detection and recognition to extract more valuable information. Image Enhancement is to procedure the input image in any such means that the output image is more compatible for interpretation by the humans as well as by way of machines.

A digital image is the representation of two dimensional image as a finite set of digital values, known as picture elements or pixels. The processing of the image initiates with news paper industry got necessary impetus with arrival of computers and soon entered into the domain of critical professions. Image processing serves as a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts. Image Processing has become essential feature for retrieving maximum information suitable for various professions like Remote Sensing, Medical Imaging, Non-destructive Evaluation, Forensic Studies, Textiles, Material Science, Military, Film industry, Document processing, Graphic arts and Printing Industry. The common steps in image processing are image scanning, enhancing, interpretation and storage. Either traditional methods or modern technological tools are used for enhancing the quality of the images. Accordingly, processing is categorized into two types: Analog Image Processing: It involves the alteration of images with the aid of electrical variation technology. The most common example is television image. The television signal is a voltage level which varies in amplitude to represent brightness through the image. By electrically varying the signal, the displayed image appearance is altered. The brightness and contrast controls on a TV set serve to adjust the amplitude and reference of the video signal resulting in the brightening, darkening and alteration of the brightness range of the displayed image. Digital Image Processing: The sine qua non of this processing involves the availability of computer for the subsequent Processing of 2-dimensional picture.

Fractal geometry is a new language used to describe, model and analyze complex forms found in nature. The term fractal is commonly used to describe the family of non-differentiable functions that are infinite in length. Over the past few years fractal geometry has been used as a language in theoretical, numerical and experimental investigations. It provides a set of abstract forms that can be used to represent a wide range of irregular objects. Fractal objects contain structures that are nested within one another. Each smaller structure is a miniature form of the entire structure. The use of fractals as a descriptive tool is diffusing into various scientific fields, from astronomy to biology. Fractal concepts can be used not only to describe the irregular structures but also to study the dynamic properties of these structures. The applications of fractals can be divided into two groups. One is to analyze data sets with completely irregular structures and the other is to generate data by recursively duplicating certain patterns. In image processing and analysis, fractal techniques are applied to image compression, image coding, modeling of objects, representation and classification of images ( Falconer, 1992 ; Sato et al., 1996 ; Li et al., 1997 ; Cochran et al., 1996 ; Lee and Lee, 1998 ; Luo, 1998).

The offline data we got it from the hospital, we had done it with image processing was not upto our requirement, so we downloaded the EEG data from the PHYSIONET which is a online resource having online EEG data (.Mat files) contains both normal and abnormal. We continued our research with this online data by doing signal processing, and we detected the start and end of seizure from each of the brain waves by concatenating the 20-channel EEG data from a patient.

Abstract - Image Processing includes changing the nature of an image in order to improve its pictorial information for human interpretation, for autonomous machine perception. Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Interest in digital image processing methods stems from two principals applications areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception. Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. In this paper investigates different steps of digital image processing.like, a high-speed non-linear Adaptive median filter implementation is presented. Then Adaptive Median Filter solves the dual purpose of removing the impulse noise from the image and reducing distortion in the image.

MB91680, the first product targeted for single-lens reflex and prestige-class compact cameras from FUJITSU, is fully capable of processing large, 2-line output CCD, and HDTV signals. MB91680 mounts 16-bit high-speed DSP to enable advanced image processing and audio processing by software. This year, FUJITSU plans to enhance its lineup of digital

different values such as image, color of a pixel. We will examine some basic set operations and their usefulness in image processing and we will deal here only with Morphological filtering operators such as Erosion , dilation ,Opening , closing Hit-or-miss Boundary extraction . Morphological operations for binary images and its applications.

This work deals with image processing for three medical imaging applications: speckle detection in 3D ultrasound, left ventricle detection in cardiac magnetic resonance imaging MRI and f[r]

Segmentation on trivial images is one of the difficult tasks in image processing. The scope of image processing has been evolved extensively in the field of Agriculture. This paper mainly focuses on crop images. The most important part in perceptual segmentation is feature extraction, color feature extraction, Texture feature extraction and Edge feature extraction etc. For retrieval and image indexing feature extraction is implemented. Feature extraction is a method of capturing the visual content of digital images for retrieval, indexing and for other different purposes. General and specific features are incorporated in this algorithm. Texture can be evaluated based on the characteristics like fine coarse, smooth, rippled, molded and irregular. One of the best tools for texture analysis of the image is GCLM. It defines NxN matrix and the size is equal to the largest gray level. The co-occurrence probability for pixels with gray levels is calculated. Then measure the degree of texture smoothness, contrast is used, and indicated as low when the image has constant gray levels. The Inverse difference gives the local homogeneity; it is high when a distribution of gray level is in limited range over the local images. To identify the color similarity between two images and to differentiate image based on their features the color moments are used. The Histogram is a bar graph that represents the tonal distribution in a digital image. Color Coherence vector is used to measure the spatial coherence of the pixels with a given color. It mixes the evidence from different sources of information to compute the probability of an event.

Websites – related • Image processing: http://www.imageprocessingbook.com • C++ language tutorial: http://www.cplusplus.com/doc/tutorial/index.html • C++ library reference: http://www[r]

be used for digital image processing. Briefly, these advances may be summa- rized as follows: (1) the invention of the transistor at Bell Laboratories in 1948; (2) the development in the 1950s and 1960s of the high-level programming lan- guages COBOL (Common Business-Oriented Language) and FORTRAN (Formula Translator); (3) the invention of the integrated circuit (IC) at Texas Instruments in 1958; (4) the development of operating systems in the early 1960s; (5) the development of the microprocessor (a single chip consisting of the central processing unit, memory, and input and output controls) by Intel in the early 1970s; (6) introduction by IBM of the personal computer in 1981; and (7) progressive miniaturization of components, starting with large scale integra- tion (LI) in the late 1970s, then very large scale integration (VLSI) in the 1980s, to the present use of ultra large scale integration (ULSI). Concurrent with these advances were developments in the areas of mass storage and display sys- tems, both of which are fundamental requirements for digital image processing. The first computers powerful enough to carry out meaningful image pro- cessing tasks appeared in the early 1960s. The birth of what we call digital image processing today can be traced to the availability of those machines and to the onset of the space program during that period. It took the combination of those two developments to bring into focus the potential of digital image processing concepts. Work on using computer techniques for improving im- ages from a space probe began at the Jet Propulsion Laboratory (Pasadena, California) in 1964 when pictures of the moon transmitted by Ranger 7 were processed by a computer to correct various types of image distortion inherent in the on-board television camera. Figure 1.4 shows the first image of the moon taken by Ranger 7 on July 31, 1964 at 9:09 A . M . Eastern Daylight Time

Image processing is spreading in various fields. Image processing is a method which is commonly used to improve raw images which are received from various resources [1]. It is a technique to transform an image into digital form and implement certain actions on it, in order to create an improved image or to abstract valuable information from it. It is a kind of signal dispensation where image is an input and output is also an image or features related with image. The purpose of image processing is distributed into several groups which are given below.

JETIR1602019 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 94 Caulfield Havlicek et al., [26] discuss about Advanced image processing techniques. using multimode IR processing the interested regions are extracted. As long format single color and multicolor sensors challenges in viewing and taking action on a much higher data volume and it also becomes challenging to end users. Reporting on processing techniques, to effectively extract targets that utilize multiple processing modes both multiple spectral and multiple pre-processed data bands from Focal Plane Array (FPA) sensors. Image processing techniques address the key pre-processing requirements, that includes scene based non-uniformity correction of pixels that are static and dynamic, multiband processing is used for object detection, reduction, management of clutter and non-targets in a cluttered environment. The motivating techniques include image pre-processing, extracting small percentage of the image set with high likelihood targets and then transmitting “active” pixel data while ignoring unchanging pixels. These techniques demonstrate significant reductions in the raw data, and allow the end user to more intelligently select data types for object identification without requiring a person in the loop.

In the second problem, we study several methods for detecting the edges of an image. An image consists of a rectangular array of pixels, each with a varying degree of intensity represented as colours and gray tone scale. The problem of edge detection can be stated as searching a set of boundary pixels that separate the high and low intensities of the given image, . Edge detection is one of the most fundamental components in image analysis, as the method contributes in solving problems such as object recognition, noise reduction, multiresolution analysis, image restoration and image reconstruction. Since an image is normally rectangular in shape, the parallel mesh computing network provides a good platform for its solution. Physically, mesh network provides an ideal tool for solving the image processing problem as each of its processor directly maps the pixels of the given image.

The use of experts in identifying landmarks is common in medical image processing, but due to intra-expert and inter-expert variability, it is often desirable to find an automatic method. The paper by Sanchez Castro et al. entitled “A Cross Validation Study of Deep Brain Stimulation Targeting: From Experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms’’ presents a validation study comparing expert performance with non-rigid registration in the task of identifying the subthalamic nuclei. Since the subthalamic nuclei are usually not clearly identifiable in clinical MRI, the issue of an appropriate reference standard is raised. In this work, landmark localization performance is assessed in both a limited test data set where the subthalamic nuclei are clearly visible, and by examination of the influence of alignment of the surrounding anatomy upon the accuracy of localization of the subthalamic nuclei. The validation study carried out by the authors enables them to conclude that automatic localization of the subthalamic nuclei can be achieved with an accuracy not different from that of interactive localization by experts. In “Generalised Overlap Measures for Evaluation and Validation in Medical Image Analysis”, Crum et al. present a framework in which a single figure-of-merit and a complementary measure of error (the Overlap Distance) can be used to capture the extent of non-overlapping parts when registering MR brain images. The process is demonstrated by constructing ground truth for a set of brain atlas images that can then be used to evaluate various segmentation algorithms that others may wish to use for algorithm performance comparisons. Deligianni et al. also deal with registration issues in their paper “Non-Rigid 2D/3D Registration for Patient Specific Bronchoscopy Simulation with Statistical Shape Modelling: Phantom Validation”. This paper proposes and validates a practical 2D/3D registration framework that incorporates patient- specific deformations captured by 3D tomographic imaging and catheter tip electromagnetic tracking. The incorporation of data from the catheter tip tracking reduces the number of parameters that control airway deformation (modelled by an Active Shape Model), significantly simplifying the optimization problem.

Allsop [Allsop91a] implemented a basic image processing library called FUPT (Functional Language Image Processing Toolkit) using SML and discussed basic concepts of image processing by comparing SML programs with Apply (Hamey89a] and C. Breuel [Breuel92a] implemented a library of functions for computer vision both in SML and C++ and discussed how elements of functional programming benefit the programming of various vision algorithms. They both concluded that functional programs are modular and easier to understand because the language has strong mechanisms, such as higher-order functions and polymorphism, to integrate from diverse sources. Breuel, in particular, stated that the language support for closures with unlimited extent enables one to implement lazy data structures which are particularly desirable when an algorithm does not require information at eveiy pixel. However, SML is a strict language and so any laziness needs to be explicitly programmed (See Table 1-2). As a consequence, he did not demonstrate this in the programs. The overall programming style of SML, though it is functional, is quite different from lazy languages, and so separate work using lazy languages would be necessaiy to discuss effects of laziness.

The PixelGrabber class is a utility for converting an image into an array of pixels. This is useful in many situations. If you are writing a drawing utility that lets users create their own graphics, you probably want some way to save a drawing to a file. Likewise, if you’re implementing a shared whiteboard, you’ll want some way to transmit images across the Net. If you’re doing some kind of image processing, you may want to read and alter individual pixels in an image. The PixelGrabber class is an ImageConsumer that can capture a subset of the current pixels of an Image . Once you have the pixels, you can easily save the image in a file, send it across the Net, or work with individual points in the array. To recreate the Image (or a modified version), you can pass the pixel array to a MemoryImageSource . Prior to Java 1.1, PixelGrabber saves an array of pixels but doesn’t save the image’s width and height—that’s your responsibility. You may want to put the width and height in the first two elements of the pixel array and use an offset of 2 when you store (or reproduce) the image.

The sampling rate is used to determine the spatial resolution of the digitized image and the quantization level is used to determine the number of grey levels in the digitized image. A magnitude of the sampled image is expressed as a digital value in image processing and the transition between continuous values of the image function and its digital equivalent is called quantization. The quantization levels should be high enough for human perception of fine shading details in the image. The occurrence of false contours acts as the main problem in image which has been quantized with insufficient brightness levels. [3]

mage processing [3] is a form of signal processing for which the input is an image, such as photographs; 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. This is done through the development and implementation of processing means necessary to operate on the image. Processing image using a digital computer provides the greatest flexibility and power for general image processing application, since the programming of a computer can be changed easily which allows operation to be modified quickly. Interest in image processing technique dates back to early 1920’s when digitized pictures of world news events were first transmitted by submarine cable between New York and London.[4]. However, application of digital image processing concepts did not become widespread until the middle 1960’s, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth and has been subjected of study and research in various fields. Image processing and computer vision practitioners tend to concentrate on a particular area of specialization [5] research interests as “texture”, “surface mapping”, “video tracking”, anthe like. Nevertheless, there is a strong need to appreciate the spectrum and hierarchy of processing levels.