L. Vincent, "Morphological transformations of binary images with arbitrary structuring elements", Signal Processing, Vol. 22, pp. 3-23, 1991.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....but rather as the supremum of translated versions of a function . Although all these algorithms were developed for a function they are applicable to binary openings. Algorithms dedicated to binary openings have also been proposed. VAN VLIET [12] et al. SCHMITT [7] and later VINCENT [13] provided methods which analyze the border of both and . LAY [5] developed an algorithm based on the distance function. The geometric interpretation, that an opening is the union of translated . included in , led VAN DROOGENBROECK [9] to propose a propagation algorithm for binary openings ....

L. Vincent. Morphological transformations of binary images with arbitrary structuring elements. Signal Processing, 22(1):3--23, January 1991.

....to horizontal and vertical grid lines to detect zero crossing points reliably, even in the presence 6 H USEYIN TEK AND BENJAMIN B. KIMIA (a) b) Fig. 4. This figure illustrates the dilation of an ellipse by a structuring element of the same shape, implemented using the algorithm presented in [37]. a) While the results when dilation is implemented in a single step are highly accurate, the iterations of a small structuring element in this case do not produce desirable results, as the ellipse is not digitally scalable. b) The geometric evolution produce accurate results [3] Fig. 5. a) ....

L. Vincent. Morphological transformations of binary images with arbitrary structuring elements. Signal Processing, 22(1):3--23, 1991.

....1D structuring elements is also proposed to minimize the error of the reconstructed object. 1 Introduction Mathematical Morphology is a powerful image processing and analysis tool [1] 2] and plenty of implementations and applications in different fields have been presented in the literature [3], 4] 5] 6] However, the application of mathematical morphology in 3D image processing and analysis is rather slow, since the 3D morphological operations are time consuming by current standards. Therefore, fast implementations of 3D morphology are very important. Recently, a fast algorithm ....

L. Vincent. Morphological transformations of binary images with arbitrary structuring elements. Image Procesing, 22:3--23, January 1991.

....North Ryde NSW 2113, Australia. 2 I. Introduction Erosion and dilation form the basis of all mathematical morphology operators [11] 7] Therefore, there has been a considerable amount of literature reporting techniques that enhance the speed of these fundamental operators [17] 9] 8] 4] [16], 5] 10] 14] 6] Van Herk [15] has shown how to perform grey scale erosions and dilations with a linear structuring element (SE) of arbitrary length using only 3 min max operations per pixel. However, van Herk s algorithm only treats horizontal, vertical, and diagonal structuring elements. ....

L. Vincent. Morphological transformations of binary images with arbitrary structuring elements. Signal Processing, 22(1):3--23, January 1991.

....to two dimensions. Many di#erent techniques have been proposed for implementing the basic morphological operations more e#ciently than by using their definition. Many of them involve the use of parallel computers or specialized hardware. Other techniques are strictly restricted to 2D images [4, 5]. Also, most techniques are applicable only for structuring elements of specific shape or size, although the use of structuring elements of arbitrary size and shape can be very interesting in several applications [4] For large structuring elements, decomposition in small structuring elements can ....

....or specialized hardware. Other techniques are strictly restricted to 2D images [4, 5] Also, most techniques are applicable only for structuring elements of specific shape or size, although the use of structuring elements of arbitrary size and shape can be very interesting in several applications [4]. For large structuring elements, decomposition in small structuring elements can be applied [3, 6] which is generally computationally intensive. One of the most interesting and e#cient algorithms for the fast calculation on conventional computers of the basic morphological operations for 2D ....

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L. Vincent, "Morphological transformations of binary images with arbitrary structuring elements", Image Procesing, vol. 22, no. 1, pp. 3--23, January 1991.

....a given type. That is, recognizing the generic person or car in Fig. 1 is different than recognizing an image of your car from among several car images. Particular object recognition from among a prespecified class of objects is amenable to a variety of template and iconic matching techniques [29, 27, 94]. These are based on metric properties, and thus are inappropriate for the generic problem. A theory of generic object recognition must be robust to variations within scenes, e.g. due to the hand drawn character of the images in Fig. 1, or (more realistically) to small changes in viewpoint, to the ....

....of the object without losing its partial representations. For generic object recognition, the representation must Boundary Based Primitives Region Based Primitives Volume Based Primitives Codons [73] Generalized Ribbons [17] Generalized Cylinders [12, 61, 95] Arcs of circles [25] Superquadrics [66, 94] Primitive Curvature Changes [6] MLD parts [67] Geons [11] Polygons [71] SAT [14] Polyhedra[98] Table 2: A review of shape primitives. degrade gracefully as portions of the object are occluded. 2.3 Primitives vs Transformations One way to characterize shape is to somehow define its constituent ....

L. Vincent. Morphological transformations of binary images with arbitrary structuring elements. Signal Processing, 22(1):3--23, 1991.

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L. Vincent, "Morphological transformations of binary images with arbitrary structuring elements", Signal Processing, Vol. 22, pp. 3-23, 1991.

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Vincent, L., Morphological transformations of binary images with arbitrary structuring elements. Signal Processing, 1991. 22(1): p. 3-23.

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Vincent L, 1991, "Morphological transformations of binary images with arbitrary structuring elements", Signal Processing, 22, 3-23

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