In this paper, we consider a natural paradigm for lifting of crisp-set binary filters to fuzzy filters for hardware implementation and process the gray-scale realizations of binary images as [0,1]-valued fuzzy binary images. We present the implementation of the filtering algorithms for smoothing, peak detection and edge detection of such fuzzy images using the Xilinx Virtex series of FPGA for real-time processing of image sequences. The erosion filter forms the core for all of the filtering algorithms and the dilation filter itself is implemented as a function of the erosion filter. Smoothing is achieved using fuzzy opening of the input image using the user defined fuzzy structuring element. A fuzzy top-hat transform is used for peak detection. As opposed to gray-scale top-hat, which detects only the narrow peaks, the fuzzy top-hat is shown to detect both the narrow as well as wide peaks within the same image. Edge detection algorithm uses the fuzzy morphological gradient wherein the set minus operation has been performed between the dilated and the eroded images. Pipelined architectures are used for the erosion filter design and the use of flops has been maximized to achieve a high clock rate. The throughput measurements and the results generated by the implemented filters are also presented.
The introduction of new, more powerful personal computers and workstations has ushered in a new era of computing. New machines are now capable of supporting full-motion video. The problem of video compression is a difficult and important one, and has inspired a great deal of research and development activity. A number of video compression techniques and standards have been introduced in the past few years, particularly MPEG for interactive multimedia and for digital NTSC and HDTV applications, and H.261/H.263 for video telecommunications. These techniques use motion estimation techniques to reduce the amount of data that is stored and transmitted for each frame of video.
This paper is about these motion estimation techniques, their implementations, their complexity, advantages, and drawbacks. An overview of the MPEG video compression standard is first presented with an emphasis on how it utilizes motion compensation to achieve its high compression gains. Then a survey of current motion estimation techniques is presented, including the exhaustive search and a number of fast block-based search algorithms.
Real-time imaging has application in areas such as multimedia, virtual reality, medical imaging, and remote sensing and control. Recently, the imaging community has witnessed a tremendous growth in research and new ideas in these areas. To lend
structure to this growth, we outline a classification scheme and provide an overview of current research in real-time imaging. For convenience, we have categorized references by research area and application.
KEYWORDS: Image compression, Image processing, Edge detection, Real time image processing, Image enhancement, Image filtering, Image restoration, Real-time computing, Digital image processing, Real time imaging
In real-time image processing, an application must satisfy a set of timing constraints while ensuring the semantic correctness of the system. Because of the natural structure of digital data, pure data and task parallelism have been used extensively in real-time image processing to accelerate the handling time of image data. These types of parallelism are based on splitting the execution load performed by a single processor across multiple nodes. However, execution of all parallel threads is mandatory for correctness of the algorithm. On the other hand, speculative execution is an optimistic execution of part(s) of the program based on assumptions on program control flow or variable values. Rollback may be required if the assumptions turn out to be invalid. Speculative execution can enhance average, and sometimes worst-case, execution time. In this paper, we target various image processing techniques to investigate applicability of speculative execution. We identify opportunities for safe and profitable speculative execution in image compression, edge detection, morphological filters, and blob recognition.
A general paradigm for lifting binary morphological algorithms to fuzzy algorithms is employed to construct fuzzy versions of several standard morphological operations. The lifting procedure is based upon an epistemological interpretation of both image and filter fuzzifications. Algorithms are discussed for the following image processing tasks: shape detection, edge detection, and filtering union noise.
Processing gray-scale realizations of images that are ideally binary (such as gray-scale realizations of printed characters) is problematic due to the fact that gray-scale processing should be consistent with the binary nature of the ideal image. Essentially, any final decision (such as the recognition of a specific character at a specific location) should reflect the content of the ideal image, which is generally unknown. Too often, a gray-scale realization of an ideal binary image is processed using methods appropriate for gray-scale realizations of ideal gray- scale images. These should not be expected to lead to decision procedures appropriate for binary images. Fuzzy morphological algorithms do not assume probabilistic knowledge of the degradation process; however, they mirror the processing that one would have performed were the ideal binary image known. Thus, they lead to decision procedures consistent with those that would have been taken following processing of the ideal binary image. In this paper we discuss the fuzzy hit-or-miss transform based shape detectors that are capable of detecting geometric shapes in the presence of considerable additive as well as subtractive random noise. There exists an infinite number of realizations of this shape detector and the determination of which detector is suitable is application dependant; nevertheless, there exist a general set of heuristics for selecting the appropriate realization. We also carry out extensive noise- sensitivity analysis for a few of these shape detectors.
A line sketch of a 3-dimensional scene provides important information about objects in the scene and about their spatial relationships. Considerable effort has been reported in the literature describing methods of extracting line sketches from 2-dimensional projected images of scenes. In this paper a projection based approach is described for detecting boundaries in an image having linear segments. This method can be utilized for the recognition of polyhedral objects or for the identification of familiar objects for mobile robot location. The computational performance of this algorithm is greatly enhanced by operating directly on the gray-scale image and by starting with a coarse (low resolution) image and moving successively to higher resolutions at regions of interest (i.e., brightness discontinuities). A complete circular projection of the higher level image is computed and the discontinuities in this projection are located using the derivatives of the projection at fixed angles. Peaks in this function correspond to lines in the original image. A vertex extraction algorithm has been developed to establish the edge extent in the image. These methods have been implemented and tested with real and synthetic images and are believed to be better than methods that use edge enhancement/thresholding and then employ the Hough transform to isolate lines in the image.
The theory of fuzzy mathematical morphology is well-developed, including the characterization of fuzzy Minkowski algebra and extensions of the basic Matheron representation theorems. The present paper provides a natural paradigm for the lifting of crisp-set binary filters to fuzzy filters. The paradigm is based on considering gray-scale realizations of binary images as [0,1]-valued fuzzy images and then processing them in a manner compatible with their interpretation as fuzzy binary images. Various filters are implemented in the paper: smoothing, edge detection, peak detection, and object detection.
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