A precise definition of digital skeletons and a mathematical framework for the analysis of a class of thinning algorithms, based on morphological set transformation, are presented. A particular thinning algorithm (alg...
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A precise definition of digital skeletons and a mathematical framework for the analysis of a class of thinning algorithms, based on morphological set transformation, are presented. A particular thinning algorithm (algorithm A) is used as an example in the analysis. Precise definitions and analyses associated with the thinning process are presented, including the proof of convergence, the condition for one-pixel-thick skeletons, and the connectedness of skeletons. In addition, a necessary and sufficient condition for the thinning process in general is derived, and an algorithm (algorithm B) based on this condition is developed. Experimental results are used to compare the two thinning algorithms, and issues involving noise immunity and skeletal bias are addressed.
A comparative evaluation of some fast parallel thinning algorithms for elongated shapes on an overlapped shared memory multiprocessor architecture (OSMMA) is presented. The evaluation is based on the execution time an...
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A comparative evaluation of some fast parallel thinning algorithms for elongated shapes on an overlapped shared memory multiprocessor architecture (OSMMA) is presented. The evaluation is based on the execution time and multiprocessor speed-up of the algorithms, as well as the quality of the skeletonized image structures. Issues relating to efficient algorithm implementation on the distributed memory architecture and refinements in the algorithms for better results are also discussed. The algorithms implemented include our own thinning technique based on the concept of object edges width compresssion that achieves efficient results while significantly decreasing execution timings. Experimental results for some real images are presented.
Hilditch thinning algorithm is widely used in image pre-processing process. In this paper, we propose an improved Hilditch algorithm to acquire a more stable output. We conclude that the skeletons obtained from our al...
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ISBN:
(纸本)9780769539072
Hilditch thinning algorithm is widely used in image pre-processing process. In this paper, we propose an improved Hilditch algorithm to acquire a more stable output. We conclude that the skeletons obtained from our algorithms give better results in Chinese characters and numbers. The advantage of this algorithm corrects the beard by modifying the warp in the part of intersection.
thinning is a frequently applied technique for extracting skeleton-like shape features (i.e., centerline, medial surface, and topological kernel) from volumetric binary images. Subfield-based thinning algorithms parti...
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ISBN:
(纸本)9783642137716
thinning is a frequently applied technique for extracting skeleton-like shape features (i.e., centerline, medial surface, and topological kernel) from volumetric binary images. Subfield-based thinning algorithms partition the image into some subsets which are alternatively activated, and some points in the active subfield are deleted. This paper presents a set of new 3D parallel subfield-based thinning algorithms that use four and eight subfields. The three major contributions of this paper are: 1) The deletion rules of the presented algorithms are derived from some sufficient conditions for topology preservation. 2) A novel thinning scheme is proposed that uses iteration-level endpoint checking. 3) Various characterizations of endpoints yield different algorithms.
In this paper, a theorem on approximation to Boolean functions by neural networks and its proof are proposed. A Boolean function, f:{0,1}(n) --> {0,1}, is proved to be approximated by a three layer neural network w...
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ISBN:
(纸本)0780358902
In this paper, a theorem on approximation to Boolean functions by neural networks and its proof are proposed. A Boolean function, f:{0,1}(n) --> {0,1}, is proved to be approximated by a three layer neural network with 2(n) hidden nodes. With the theorem, a thinning algorithm using the neural network technique is concluded. A hard processor implementing the thinning algorithm is designed to raise the thinning efficiency, which can meet the practical needs better This makes the algorithm suitable for real-time image processing.
thinning is a widely used technique to obtain skeleton-like shape features (i.e., centerlines and medial surfaces) from digital binary objects. Conventional thinning algorithms preserve endpoints to provide important ...
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ISBN:
(纸本)9783642331404
thinning is a widely used technique to obtain skeleton-like shape features (i.e., centerlines and medial surfaces) from digital binary objects. Conventional thinning algorithms preserve endpoints to provide important geometric information relative to the object to be represented. An alternative strategy is also proposed that preserves isthmuses (i.e., generalization of curve/surface interior points). In this paper we present ten 3D parallel isthmus-based thinning algorithm variants that are derived from some sufficient conditions for topology preserving reductions.
In this report three thinning algorithms are developed: one each for use with rectangular, hexagonal, and triangular arrays. The approach to the development of each algorithm is the same. Pictorial results produced by...
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In this report three thinning algorithms are developed: one each for use with rectangular, hexagonal, and triangular arrays. The approach to the development of each algorithm is the same. Pictorial results produced by each of the algorithms are presented and the relative performances of the algorithms are compared. It is found that the algorithm operating with the triangular array is the most sensitive to image irregularities and noise, yet it will yield a thinned image with an overall reduced number of points. It is concluded that the algorithm operating in conjunction with the hexagonal array has features which strike a balance between those of the other two arrays. [ABSTRACT FROM AUTHOR]
This paper investigates three different methods of comparing preference structures for thinning algorithms. The first method involves a series of experiments with human subjects. The second makes use of neural network...
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This paper investigates three different methods of comparing preference structures for thinning algorithms. The first method involves a series of experiments with human subjects. The second makes use of neural networks and the third is based on dissimilarities and distance measures that is computer generated. Several statistical tests have been performed to analyze the preference structures exhibited by the data. This study highlights human coherence in comparing skeletons and the novelty of using reference skeletons to facilitate the evaluation of thinning algorithms. None of the automatic approaches provides a useful insight although a measure of information content manifests some consistency. The overall study suggests a systematic protocol involving human coherence to evaluate preprocessing algorithms.
An efficient data reduction scheme for the simplification of a surface given by a large set X of 3D point-samples is proposed. The data reduction relies on a recursive point removal algorithm, termed thinning, which o...
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An efficient data reduction scheme for the simplification of a surface given by a large set X of 3D point-samples is proposed. The data reduction relies on a recursive point removal algorithm, termed thinning, which outputs a data hierarchy of point-samples for multiresolution surface approximation. The thinning algorithm works with a point removal criterion, which measures the significances of the points in their local neighbourhoods, and which removes a least significant point at each step. For any point x in the current point set Y subset of X, its significance reflects the approximation quality of a local surface reconstructed from neighbouring points in Y. The local surface reconstruction is done over an estimated tangent plane at x by using radial basis functions. The approximation quality of the surface reconstruction around x is measured by using its maximal deviation from the given point-samples X in a local neighbourhood of x. The resulting thinning algorithm is meshfree, i.e., its performance is solely based upon the geometry of the input 3D surface point-samples, and so it does not require any further topological information, such as point connectivities. Computational details of the thinning algorithm and the required data structures for efficient implementation are explained and its complexity is discussed. Two examples are presented for illustration.
We show that thinning of increments of the fractional Brownian motion with Hurst exponent H not equal 1/2 breaks its H-self-similarity property. As a result, we obtain a new Gaussian process with stationary increments...
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We show that thinning of increments of the fractional Brownian motion with Hurst exponent H not equal 1/2 breaks its H-self-similarity property. As a result, we obtain a new Gaussian process with stationary increments which is not the fractional Brownian motion for any H. Moreover, in the subdiffusion case (H < 1/2), the new process statistically resembles the classical Brownian motion (H = 1/2). To this end, we study analytically the second moment of such processes. Finally, Monte Carlo simulations show that the H estimator obtained by mean square displacement is close to the Brownian motion case with H = 1/2. These results show that stationary data describing anomalous diffusion phenomenon can lead to different statistical conclusions for different resolution of measurement. Therefore, one should be very careful in statistical inference, especially in strong subdiffusion regimes (H approximate to 0).
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