Publisher's Synopsis
Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two dimensional signals while applying already set signal processing methods to them. It is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too. Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans). In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data). Examples include microarray data in genetic research, or real-time multi-asset portfolio trading in finance. Typical image processing procedures include image acquisition, image alignment/stitching, image contrast enhancement, grey scale thresholding, and/or image subtraction. The characteristic of the specimens determines which image processing techniques to be used. For example, for the images with background noise, image subtraction can be applied when the background can be evaluated; otherwise thresholding might be applied to eliminate the background influence. Advanced Image Acquisition, Storage and Retrieval will provide mathematical foundations and practical techniques for digital manipulation of images such as image acquisition; image storage and image retrieval. In many areas of commerce, government, academia, hospitals, and homes, large collections of digital images are being created. However, in order to make use of it, the data should be organized for efficient searching and retrieval. An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Due to diversity in content and increase in the size of the image collections, annotation became both ambiguous and laborious. With this, the focus shifted to Content Based Image Retrieval (CBIR), in which images are indexed according to their visual content. In this volume, the image processing techniques will be reviewed. Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two dimensional signals while applying already set signal processing methods to them. It is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too. Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans). In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data). Examples include microarray data in genetic research, or real-time multi-asset portfolio trading in finance. Typical image processing procedures include image acquisition, image alignment/stitching, image contrast enhancement, grey scale thresholding, and/or image subtraction. The characteristic of the specimens determines which image processing techniques to be used. For example, for the images with background noise, image subtraction can be applied when the background can be evaluated; otherwise thresholding might be applied to eliminate the background influence. Advanced Image Acquisition, Storage and Retrieval will provide mathematical foundations and practical techniques for digital manipulation of images such as image acquisition; image storage and image retrieval. In many areas of commerce, government, academia, hospitals, and homes, large collections of digital images are being created. However, in order to make use of it, the data should be organized for efficient searching and retrieval. An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Due to diversity in content and increase in the size of the image collections, annotation became both ambiguous and laborious. With this, the focus shifted to Content Based Image Retrieval (CBIR), in which images are indexed according to their visual content. In this volume, the image processing techniques will be reviewed. Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two dimensional signals while applying already set signal processing methods to them. It is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too. Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans). In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data). Examples include microarray data in genetic research, or real-time multi-asset portfolio trading in finance. Typical image processing procedures include image acquisition, image alignment/stitching, image contrast enhancement, grey scale thresholding, and/or image subtraction. The characteristic of the specimens determines which image processing techniques to be used. For example, for the images with background noise, image subtraction can be applied when the background can be evaluated; otherwise thresholding might be applied to eliminate the background influence. Advanced Image Acquisition, Storage and Retrieval will provide mathematical foundations and practical techniques for digital manipulation of images such as image acquisition; image storage and image retrieval. In many areas of commerce, government, academia, hospitals, and homes, large collections of digital images are being created. However, in order to make use of it, the data should be organized for efficient searching and retrieval. An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Due to diversity in content and increase in the size of the image collections, annotation became both ambiguous and laborious. With this, the focus shifted to Content Based Image Retrieval (CBIR), in which images are indexed according to their visual content. In this volume, the image processing techniques will be reviewed.
