Digital image processing

digital prototype processing pot is the most dynamic of all our senses since it provides us with a huge amount of entropy about what surrounds us. It is non surprising that an ancient Chinese proverb that quotes A picture is worth a thousand words is still widely utilise. All this information is valuable for elementary procedures (for example think our everyday activities), but in any case for much complex processes as the emergence of our intelligence. At the take of favorable organization, kitchen ranges atomic number 18 also important as a means of persuadeting information, and almost all of todays media be based on our imagery. The huge amount of visual information and the need for its processing, lead scientists and technicians towards research in order to discover a means for digital see to it storage and processing using computers. This elbow grease resulted in a new Information Engineering Industry called digital see to it Processing and abstract. This ind ustry began to grow fifteen old age ago. However, it has shown a dynamic development, especially during the most recent years and it is considered a science and technology with a promising future and many potential. As the title indicates, digital Image Processing is concentrated on digital designs and their processing by a computer. Therefore, both the gossip and output of this process are digital realizes. digital plan processing flocknister be used for unhomogeneous reasons improvement of the quality of go outs, filtering of noise caused by transmission, condensation of image information, image storage and digital transmission. On the other hand, digital image compendium deals with the rendering and recognition of the content of an image. This description is uncouthly symbolic. Therefore, the input when it comes to digital image analysis, is a digital image and the output is a symbolic description. Image analysis principally tries to mimic human vision. Therefore, an identical term which is often used is Computer Vision. It has to be underlined that computer vision is a complex neuro-physiological mechanism driven by upper level knowledge (high level vision). The characteristics of this mechanism are not known and existing mathematical models are save inadequately accurate. As a result, it is uncorrectable to simulate high level vision by a computer. For this reason, the methods used for image analysis when it comes to machine vision and human vision vary significantly. Image analysis is easier in the case of applications where the environment, objectives and lighting conditions are fixed. This is usually the case of a production process in industry. The branch of computer vision which is used in industry is called Robotic Vision. The analysis is much more difficult in applications where the environment is unknown and there is a large number of objects or the distinguishable objects are unclear or difficult to separate (for example in bio medical applications or in outdoor / natural expressions). In such applications, even experts find it difficult to recognize objects. For these reasons, it is still difficult to obtain a general image analysis system. Most existing systems are designed for specialise applications.OTHER RELATED RESEARCH AREASDigital image processing and analysis are related to various other scientific compasss because of their subject of research. Recently, there is a tendency, at least in terms of applications, for digital image processing to become an interdisciplinary industry. Some related research areas areDigital head ProcessingGraphicsPattern Recognition unlifelike IntelligenceTelecommunications and MediaMultimedia SystemsWe will examine the relation back of each of these areas with digital image processing and image analysis independently, since the way they are related is not very clear.Digital Image Processing Vs Digital Signal ProcessingEvery image can be set forth as a two-dimension al sharpen. Therefore, for the analysis and processing of digital images all the techniques of digital signal processing can be used. This area provides the theoretical and programming base for image processing.Digital Image Processing Vs GraphicFundamentally, the subject of graphic is digital synthesis. Therefore, the input is a symbolic description and the output is a digital image. For this purpose a geometric modelling of the display object takes place, as well as a digital description of the lighting conditions and digital production of the objects illuminants in the simulated position of the camera.Digital Image Processing Vs Pattern RecognitionPattern recognition deals with the classification of an object to a class of models (class pattern). For example, trying to recognize whether a new object is a resistor, a capacitor, or an integrated circuit. For this purpose, an object has to be described using certain characteristics (features), mostly numbers (for example diameter and area), and then it can be classified based on these characteristics.Digital Image Processing Vs Artificial IntelligenceArtificial intelligence and image understanding are areas where a symbolic representation of an image is converted to another more complex representation or a representation more comfortably comprehensible to humans. Usually, techniques for representation of human knowledge (knowledge representation) and reasoning (inference) are used for this purpose. The analysis of a scene requires higher cognitive processes and that is why it is also known as high-level vision. On the other hand, image processing is more related to the lower levels of vision, that take place in the human eye and ocular nerve and as a result it is also known as low level vision.Digital Image Processing Vs TelecommunicationsThe field of telecommunications is related to digital image transmission in telecommunication networks that transmit voice and data. The resulting networks are called Int egrated Services Digital Networks (ISDN). A key puzzle concerning image transmissions is the crush of the images content, since a colour image requires about 750 Kbytes for its description. The construction of special algorithms for coding and decoding is also required. Digital image processing is also directly connected to the HDTV (High Definition TV). Its basic aim is the compression of the vast amount of information and the improvement of the quality of images that are received.Digital Image Processing Vs unfermented Generation DatabasesThe new generation of databases includes image, signal (voice) and data storage. In this field, digital image processing deals with image coding and analysis by finding smart ways of recovery (retrieval) of images.DIFFERENT AREAS OF DIGITAL bod PROCESSINGDigital image processing includes several(prenominal) areas that are closely related. Some of those areas are mentioned beneath sire of the imageDigital Filtering of the imageEdge DetectionR egion SegmentationShape expositionTexture AnalysisMotion Analysis stereoscopic visionIt is logical that the description of all these areas is not practicable in a short presentation. However, the literature is so wide that several books would be requisite in order to describe adequately the digital image processing. Moreover, image processing is a cognitive area that makes extensive use of specialized mathematical, which makes it difficult to be presented to an audience. For this reason the description of the area it is purely qualitative.Capture of the ImageThe first thing that has to be described is the capturing mechanism of the images. The most classic means of capturing an image is by a photographic camera and a film. However, this technique is not very useful in the field of digital image processing, since the captured image cannot be easily processed by computer. On the other hand, electronic capture is particularly interesting because the image can be digitized and then processed by a computer. For this reason, conventional electronic video cameras are widely used. Electronic video cameras scan the image and produce an galvanisingal signal as an output. There are various camera technologies (for example Orthicon, Vidicon, CCD). The electric signal produced by the camera is then led to a frame grabber. During the process of digitalization, the analogue signal is converted to a digital signal using an A / D converter. Thus, the image is converted into a matrix of 256256 or 512512 points (spots). Each point is typically represented by 8 snaps, i.e. 256 levels of brightness. However, a common technique in some fields (e.g. robotics) is a binary representation of images that uses only 1 bit / position. This representation is used in order to save memory and speed in the case of simple applications. In some other cases where the colour of an image is critical, colour cameras and three A / D converters are used. In this case the three primary RGB colour s (red-green-blue) are saved with 38 bits / position. As a result, digital image processing has large memory requirements, even for black and purity images. The digitized image is stored as a file on the computers local disk. To be able to see the image, we need to transfer it to a special RAM memory (image memory) connected to a monitor. Such monitors may be black and white-hot or colour (RGB). Colour monitors are mostly used even in black and white applications because they have the ability to show pseudocolours. Finally, the image in any program of image processing appears as a two-dimensional table (array) 256256 or 512512 which is filled by the computers local disk or by the image memory in which the image is stored.The process of capturing an image can cause the following distortionsBlurring frayGeometric DistortionsTherefore, before any application the correction of these distortions is essential. Geometric corrections are mostly needed where geometric information is import ant, e.g. stereoscopes, topography. The reduction of blurring is done through the process of recovery (restoration). The recovery process is particularly important in applications where there is movement, (e.g. a scene of a road) because the motion introduces blurring. In most cases the filtering of the image is also very important in order to remove noise. This can be done by various linear or nonlinear filters. Usually, nonlinear filters are mostly used because they maintain the line of credit of the edges, which is a very important factor for human vision. The overall image contrast can also be improved by special non-linear techniques (contrast enhancement).Edge DetectionAnother important process of image analysis is the recognition (tracing) of contours. There are many techniques that can be used for edge detection. The development of various edge detection techniques was imperative due to the important information about the objects used for identification, which can be found in the contours. The dual problem of edge recognition is the recognition of regions in an image. This problem is called image segmentation. Usually the different regions of an image are coloured with pseudocolours.Texture AnalysisIn several industrial applications the recognition (or analysis) of the texture is very important. An example of the importance of texture recognition in industrial applications is its use in recognition of different fabrics, or recognition of flaws in a cloth.Recognition of dealing is also a very important field of computer vision for many applications, e.g. relations monitoring, robotic driving, recognition of moving objects, digital television, videoconferencing, telephone with image compression and broadcast animation. Is should be noticed, that recognition of traffic has large memory requirements for storage and real time processing. This can only be achieved through double image processing and use of special VLSI chips.Shape DescriptionAnother are a of computer vision which is particularly useful in pattern recognition is the description of shape (shape representation). A shape is described either by its border, or by the area it covers. The edge of a shape can be described in different ways, e.g. Fourier descriptors, splines. The area of a shape can be described by methods of mathematical morphology, decomposition with simple shapes, etc. These methods are used either for the storage of a shape, or for its identification.StereoscopesMany applications require measurement of depth. In this case stereoscopy with two cameras can be used. Stereoscopy is particularly useful in photogrammetry and robot movement in a three dimensional space.