基于分數(shù)布朗運動的圖象目標檢測
IMAGE OBJECT DETECTION BASED ON FRACTIONAL BROWNIAN MOTION
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摘要: 本文分析了處處連續(xù)而不可導的非平穩(wěn)不規(guī)則信號模型分數(shù)布朗運動(fBm)。fBm具有時間(或空間)長程相關性和指數(shù)功率譜特性,在大尺度輪廓上,有較大的相關性;而觀察尺度越小,變化幅度越小,越表現(xiàn)豐富的細節(jié)。這在自然景物、圖形建模方面具有很好的應用前景。同時自然景物的較好符合fBm的分形模型與人造物體不符合分形模型之間的差異可以用來進行圖象分割。文中給出了一個基于fBm分形參數(shù)的圖象目標檢測方案,并給出了實驗結果。Abstract: Fractional Brownian motion, continuous everywhere and differentiable nowhere, offers a convenient modeling for irregular nonstationary stochastic processes with long-term dependencies and power law behavior of spectrum over wide ranges of frequencies. It shows high correlation at coarse scale and varies slightly at fine scale, which is suitable for and successful in describing and modeling natural scenes. On the other hand, man-made objects can be constructively well described by using a set of regular simple shape primitives such as line, cylinder, etc. and are free of fractal. Based on the difference, a method to discriminate man-made objects from natural scenes is provided. Experimental results verify its efficiency.
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