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Image Processing

Image Processing

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Image Processing

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  1. Image Processing المعالجة الصورية

  2. Syllabus Introduction to Image Processing, Comparession between Computer Image and Computer Vision, Major topics for Computer Vision, Major topic for image processing, Image restoration, Image Enhancement, Image Comprission, Image Representation, Digitization, Type of digital image, Binary Image, Gray Image, Color Image, HSL, Digital Image File Format, Spatial Domain, Frequency Domain, Region of interest image geometry (Crop, enlarge , shrinking , translate , rotate), Zoom algorithm, Zero order hold, First order hold, Convolution, Image Analysis: Image analysis steps, Preprocessing, Data reduction, Feature Analysis, Image algebra operation, Arithmetic operation, Logical operation, Spatial Filters, Mean Filters, Median Filters,Enhancement filters, Laplacian Filter, Difference Filter, Gray level reduction, Spatial reduction, Image sharpening (High pass filter, High frequency emphasis, Homomorphic filter), Image smoothing (Mean and median filters, Low pass filter, Image Restoration), Edge line detection technique, Robert operator (Sobel operator, Prewitt operator, Krisch compass, Robinson compass mask, Laplacian operator, Freichen mask), Image Equalization,Histogram(Histogram features, Histogram Equalization, Histogram advantage), Image enhancement: Introduction, Gray scale modification, Histogram modification, Adaptive contrast enhancement, Color, Segmentation, Region growing, Clustering methods, Boundaries detects, Combined approach, Image Compression, Discrete transformation, Fourier transform (Walsh Hadamard transform, Wavelet transform). '' steganography and steganalysis.

  3. Introduction to Computer Vision and Image Processing Computer Imaging Can be defined a acquisition and processing of visual information by computer. Computer representation of an image requires the equivalent of many thousands of words of data, so the massive amount of data required for image is a primary reason for the development of many sub areas with field of computer imaging, such as image compression and segmentation . .

  4. Another important aspect of computer imaging involves the ultimate “receiver” of visual information in some case the human visual system and in others the computer itself. • Computer imaging can be separate into two primary categories: • 1. Computer Vision. • 2. Image Processing. • (In computer vision application the processed images output for use by a • computer, whereas in image processing applications the output images are • for human consumption). • These two categories are not totally separate and distinct. • Historically, the field of image processing grew from electrical engineering as an extension of the signal processing branch, whereas are the computer science discipline was largely responsible for developments in computer vision.

  5. Computer Vision Computer vision is computer imaging where the application doses not involve a human being in visual loop. One of the major topics within this field of computer vision is image analysis. Image Analysis: involves the examination of the image data to facilitate solving vision problem. The image analysis process involves two other topics: • Feature Extraction: is the process of acquiring higher level image information, such as shape or color information. • Pattern Classification: is the act of taking this higher –level information and identifying objects within the image.

  6. Computer vision systems are used in many and various types of environments, such as: 1. Manufacturing Systems 2. Medical Community 3. Law Enforcement 4. Infrared Imaging 5. Satellites Orbiting.

  7. Image processing Image processing is computer imaging where application involves a human being in the visual loop. In other words the image are to be examined and acted upon by people .Image processing also refers to the manipulation, improvement and analysis of pictorial information. Pictorial information means a two-dimensional visual image. Digital image processing is concerned with the improvement of quality of a picture that is digitally represented, as that represented in the digital computer. Image Processing The major topics within the field of image processing include: 1. Image restoration. 2. Image enhancement. 3. Image compression

  8. 1. Image Restoration Is the process of taking an image with some known, or estimated a degradation, and restoring it to its original appearance. Image restoration is often used in the field of photography or publishing where an image was somehow degraded but needs to be improved before it can be printed Figure(1,2)

  9. 2 Image Enhancement Involves taking an image and improving it visually, typically by taking advantages of human Visual Systems responses. One of the simplest enhancement techniques is to simply stretch the contrast of an image. Enhancement methods tend to be problem specific. For example, a method that is used to enhance satellite images may not suitable for enhancing medical images. Although enhancement and restoration are similar in aim, to make an image look better. They differ in how they approach the problem. Restoration method attempt to model the distortion to the image and reverse the degradation, where enhancement methods use knowledge of the human visual systems responses to improve an image visually.

  10. 1.3 Image Compression Involves reducing the typically massive amount of data needed to represent an image. This done by eliminating data that are visually unnecessary and by taking advantage of the redundancy that is inherent in most images. Image processing systems are used in many and various types of environments, such as: 1. Medical community 2. Computer – Aided Design 3. Virtual Reality 4. Image Processing

  11. Computer Imaging Systems Computer imaging systems are comprised of two primary components types, hardware and software. The hard ware components can be divided into image acquiring sub system (computer, scanner, and camera) and display devices (monitor, printer).The software allows us to manipulate the image and perform any desired processing on the image data. Digitization The process of transforming a standard video signal into digital image .This transformation is necessary because the standard video signal in analog (continuous) form and the computer requires a digitized or sampled version of that continuous signal. The analog video signal is turned into a digital image by sampling the continuous signal at affixed rate. In the figure below we see one line of a video signal being sampled (digitized) by instantaneously measuring the voltage of the signal at fixed intervals in time. The value of the voltage at each instant is converted into a number that is stored, corresponding to the brightness of the image at that point. Note that the image brightness of the image at that point depends on both the intrinsic properties of the object and the lighting conditions in the scene.

  12. The image can now be accessed as a two-dimension array of data , where each data point is referred to a pixel (picture element).for digital images we will use the following notation : I(r,c) = The brightness of image at the point (r,c) Where r= row and c= column. “When we have the data in digital form, we can use the software to process the data”. The digital image is 2D- array as: In above image matrix, the image size is (NXN) [matrix dimension] then: Ng Where Ng denotes the number of gray levels. m is the no. of bits contains in digital image matrix.

  13. Why are images often quoted as being 512 X 512, 256 X 256, 128 X 128 etc? • Many image calculations with images are simplified when the size of the image is a power of 2.  • How many bits do we need to store an image? • The number of bits, b, we need to store an image of size N X N with 2mdifferent grey levels is: b =N x N x m • So, for a typical 512 X 512 image with 256 grey levels (m = 8) we need 2,097,152 bits or 262,144 8-bit bytes. That is why we often try to reduce m and N , without significant loss in the quality of the picture.

  14. 1.6 The Human Visual System The Human Visual System (HVS) has two primary components: • Eye. • Brian. * The structure that we know the most about is the image receiving sensors (the human eye). * The brain can be thought as being an information processing unit analogous to the computer in our computer imaging system. These two are connected by the optic nerve, which is really abundleof nerves that contains the path ways for visual information to travel from the receiving sensor (the eye) to the processor (the brain).

  15. 1.7 Image Resolution The resolution has to do with ability to separate two adjacent pixels as being separate, and then we can say that we can resolve the two. The concept of resolution is closely tied to the concepts of spatial frequency. Spatial frequency concept, frequency refers to how rapidly the signal is changing in space, and the signal has two values for brightness-0 and maximum. If we use this signal for one line (row) of an image and then repeat the line down the entire image, we get an image of vertical stripes. If we increase this frequency the strips get closer and closer together, until they finally blend together.

  16. In image we observe many brightness levels and the vision system can adapt to a wide range. If the mean value of the pixels inside the image is around Zero gray level then the brightness is low and the images dark but for mean value near the 255 then the image is light. If fewer gray levels are used, we observe false contours (bogus lines resulting from gradually changing light intensity not being accurately represented.

  17. Digital image Take any image. Look closer. Closer! What do you see? Dots! From far enough away, the dots are Indistinguishable to the average human eye. Called pixels, each are an individual color Pixels are the smallest sample of an image. Generally, more pixels translate to increased detail.

  18. Digital images is pictures that have been converted into a computer-readable binary format consisting of logical 0s and 1s. Usually, by an image we mean, a still picture that does not change with time, Whereas a video evolves with time and generally contains moving and/or changing objects. Digital images is usually obtained by converting continuous signals into digital format, The frequency with which information is transmitted, stored, processed, and displayed in a digital visual format is increasing rapidly. Dimension of Images An important feature of digital images is that it is multidimensional signals, meaning that it is a function of more than a single variable. In the classic study of digital signal processing, the signals are usually one-dimensional functions of time. Images, however, are functions of two, and perhaps three space dimensions . The dimension of a signal is the number of coordinates that are required to index a given point in the image.

  19. Size of data The amount of data in visual signals is usually quite large, and it increases geometrically with the dimensionality of the data. s Mathematically, an image is a two-dimensional light intensity function f (x,y), where, f is the brightness value or gray-level at a point ( x, y ). When both x, y and f are discretized, we get a digital image. Thus a digital image is a two-dimensional array (matrix ) of integers whose row and column indices identify a point in the image and corresponding array element value gives the brightness level at that point. The elements of the array are called pixels or pels (abbreviation for ‘picture element).

  20. Pixels and Depth • 8 bpp [28 =256; (256 colours)], • 16 bpp [216 =65536; (65,536 colours, known as Highcolour)], • 24 bpp [224 =16777216; (16,777,216 colours, known as Truecolour)]. • 48 bpp [248=281474976710656; (281,474,976,710,656 colors, used in many flatbed scanners and for professional work)

  21. A common phrase with digital cameras • One megapixel = 1 million pixels • A camera that can take pictures with a resolution of 2048×1536 pixels is commonly said to have "3.1 mmegapixels" (2048 × 1536 = 3,145,728 • 640x480 - This is the low end on most "real" cameras. This resolution is ideal for e-mailing pictures or posting pictures on a Web site. • 1600x1200 - With almost 2 million total pixels, this is "high resolution." You can print a 4x5 inch print taken at this resolution with the same quality that you would get from a photo lab. • 2240x1680 - Found on 4 megapixel cameras -- the current standard -- this allows even larger printed photos, with good quality for prints up to 16x20 inches. Megapixels" (2048 × 1536 = 3,145,728).

  22. What is the brightness of an image at a pixel position? • Each pixel of an image corresponds to a part of a physical object in the 3D world. This physical object is illuminated by some light which is partly reflected and partly absorbed by it. Part of the reflected light reaches the sensor used to image the scene and is responsible for the value recorded for the specific pixel. The recorded value of course, depends on the type of sensor used to image the scene, and the way this sensor responds to the spectrum of the reflected light. • However, as a whole scene is imaged by the same sensor, we usually ignore these details. What is important to remember is that the brightness values of different pixels have significance only relative to each other and they are meaningless in absolute terms.