An f-edge cover-colouring of a graph G = (V, E) is an assignment of colours to the edges of G such that every colour appears at each vertex v ∈ V at least f(v) times. The maximum number of colours needed to f-edge co...An f-edge cover-colouring of a graph G = (V, E) is an assignment of colours to the edges of G such that every colour appears at each vertex v ∈ V at least f(v) times. The maximum number of colours needed to f-edge cover colour G is called the f-edge cover chromatic index of G, denoted by Xfc(G). This paper gives that min[d(v)-1/f(v)]≤xfc(G)≤展开更多
A proper edge colouring f of a graph G is called acyclic if there are no bichromatic cycles in the graph. The acyclic edge chromatic number or acyclic chromatic index, denoted by , is the minimum number of colours in ...A proper edge colouring f of a graph G is called acyclic if there are no bichromatic cycles in the graph. The acyclic edge chromatic number or acyclic chromatic index, denoted by , is the minimum number of colours in an acyclic edge colouring of G. In this paper, we discuss the acyclic edge colouring of middle, central, total and line graphs of prime related star graph families. Also exact values of acyclic chromatic indices of such graphs are derived and some of their structural properties are discussed.展开更多
This paper discusses a robust technique using entropy-based detection for delineating edges in ocean colour images. The detection process relies on Jhensen-Shannon divergence based image segmentation, which has been f...This paper discusses a robust technique using entropy-based detection for delineating edges in ocean colour images. The detection process relies on Jhensen-Shannon divergence based image segmentation, which has been found to be the most suitable for noisy ocean colour images. In the attempted technique, partial removal of the noise in the images is performed and the edges are detected using entropic method. In our approach, Jhensen-Shannon divergence for the images is calculated, and the divergence image is arrived at after applying an appropriate threshold and filter to estimate the gradients. An attempted case study on retrieving chlorophyll front edges using this technique indicates that entropic method is far superior to conventional edge-enhancement tools, in terms of its insensitivity to impulsive noises and, capability in detecting meso- and micro-scale changes. This procedure would largely decrease the ambiguities associated with the ocean colour edges and hence has promising application potential in targeting fishing zones, sediment dispersion modeling and climate related studies.展开更多
文摘An f-edge cover-colouring of a graph G = (V, E) is an assignment of colours to the edges of G such that every colour appears at each vertex v ∈ V at least f(v) times. The maximum number of colours needed to f-edge cover colour G is called the f-edge cover chromatic index of G, denoted by Xfc(G). This paper gives that min[d(v)-1/f(v)]≤xfc(G)≤
文摘A proper edge colouring f of a graph G is called acyclic if there are no bichromatic cycles in the graph. The acyclic edge chromatic number or acyclic chromatic index, denoted by , is the minimum number of colours in an acyclic edge colouring of G. In this paper, we discuss the acyclic edge colouring of middle, central, total and line graphs of prime related star graph families. Also exact values of acyclic chromatic indices of such graphs are derived and some of their structural properties are discussed.
文摘This paper discusses a robust technique using entropy-based detection for delineating edges in ocean colour images. The detection process relies on Jhensen-Shannon divergence based image segmentation, which has been found to be the most suitable for noisy ocean colour images. In the attempted technique, partial removal of the noise in the images is performed and the edges are detected using entropic method. In our approach, Jhensen-Shannon divergence for the images is calculated, and the divergence image is arrived at after applying an appropriate threshold and filter to estimate the gradients. An attempted case study on retrieving chlorophyll front edges using this technique indicates that entropic method is far superior to conventional edge-enhancement tools, in terms of its insensitivity to impulsive noises and, capability in detecting meso- and micro-scale changes. This procedure would largely decrease the ambiguities associated with the ocean colour edges and hence has promising application potential in targeting fishing zones, sediment dispersion modeling and climate related studies.
