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However, the most common and famous problem in the fuzzy control is how to design the member..."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678895","https://noah.nrw/i3f/v20/3560985/canvas/3678896","https://noah.nrw/i3f/v20/3560985/canvas/3678898","https://noah.nrw/i3f/v20/3560985/canvas/3678899","https://noah.nrw/i3f/v20/3560985/canvas/3678900","https://noah.nrw/i3f/v20/3560985/canvas/3678902","https://noah.nrw/i3f/v20/3560985/canvas/3678905","https://noah.nrw/i3f/v20/3560985/canvas/3678908","https://noah.nrw/i3f/v20/3560985/canvas/3678910","https://noah.nrw/i3f/v20/3560985/canvas/3678911"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3678897","https://noah.nrw/i3f/v20/3560985/range/3678901","https://noah.nrw/i3f/v20/3560985/range/3678903","https://noah.nrw/i3f/v20/3560985/range/3678904","https://noah.nrw/i3f/v20/3560985/range/3678906","https://noah.nrw/i3f/v20/3560985/range/3678907","https://noah.nrw/i3f/v20/3560985/range/3678909"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678894","@type":"sc:Range","label":"3.4.2.     Introduction to Fuzzy Sets"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678898","https://noah.nrw/i3f/v20/3560985/canvas/3678899","https://noah.nrw/i3f/v20/3560985/canvas/3678900"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678897","@type":"sc:Range","label":"As it is known, the fuzzy logic is a powerful and strong problem-solving methodology for several applications in control and information processing; more simplicity, it does resemble decision making from approximate data. In addition, it has not only ..."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678902"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678901","@type":"sc:Range","label":"Definition 11 (\u03b1-cut and strong \u03b1 -cut) [102, 103, 114]: If the fuzzy set A is defined on X and a number \u03b1 \u03f5 [0, 1], the \u03b1-cut (A\u03b1) and the strong \u03b1-cut (A\u03b1+) can be called the crisp sets and they are given by:"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678905"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678904","@type":"sc:Range","label":"Definition 13 (Core) [102, 103, 114]: Lets A be a fuzzy subset of X, the core of A{Core(A)} is the crisp subset of X whose all elements have one membership grades in A. The support of A is the same as the \u03b1\u2013cut of A for \u03b1 =1."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678908"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678907","@type":"sc:Range","label":"Definition 15 (Trapezoidal Fuzzy Number) [102, 103, 114]: The fuzzy set might be called trapezoidal fuzzy, when membership function has following form:"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678910","https://noah.nrw/i3f/v20/3560985/canvas/3678911"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678909","@type":"sc:Range","label":"Where ith is the rules and jth is the input variables. ,a-ij.\u2264,b-ij.\u2264,c-ij.\u2264,d-ij. are known as the breakpoints of the membership functions."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678913"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678912","@type":"sc:Range","label":"3.4.3.     Fuzzy Implication Functions"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678915","https://noah.nrw/i3f/v20/3560985/canvas/3678916","https://noah.nrw/i3f/v20/3560985/canvas/3678917"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678914","@type":"sc:Range","label":"3.4.4.     Fuzzy Reasoning"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678919","https://noah.nrw/i3f/v20/3560985/canvas/3678920","https://noah.nrw/i3f/v20/3560985/canvas/3678921","https://noah.nrw/i3f/v20/3560985/canvas/3678922","https://noah.nrw/i3f/v20/3560985/canvas/3678923","https://noah.nrw/i3f/v20/3560985/canvas/3678924"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678918","@type":"sc:Range","label":"3.4.5.     Fuzzy Rule -Based System"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678926","https://noah.nrw/i3f/v20/3560985/canvas/3678927","https://noah.nrw/i3f/v20/3560985/canvas/3678928"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678925","@type":"sc:Range","label":"3.4.6.     A simple example"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678931","https://noah.nrw/i3f/v20/3560985/canvas/3678933","https://noah.nrw/i3f/v20/3560985/canvas/3678937"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3678930","https://noah.nrw/i3f/v20/3560985/range/3678932","https://noah.nrw/i3f/v20/3560985/range/3678934","https://noah.nrw/i3f/v20/3560985/range/3678935","https://noah.nrw/i3f/v20/3560985/range/3678936"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678929","@type":"sc:Range","label":"3.4.2.     Fuzzy Measures and Evidence Theory:"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678931"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678930","@type":"sc:Range","label":"The fuzzy measure theory takes into account several special parameters: the plausibility of measures, the belief in these measures, fuzzy membership functions, and classical probability measures. In such theory, even though the conditions are precise,..."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678933"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678932","@type":"sc:Range","label":"In addition, the evidence theory based on two dual non additive measures: belief measures (Bel) and plausibility measures (Pl). Given a finite universal set \u03a9 and a subset A \u2208 P(\u03a9) then:"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678937"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678936","@type":"sc:Range","label":"where m,B. is the evidence degree or the belief that there is an element belongs to the set A alone, and Bel,A. is the belief that an element belongs to A and also to the various special subsets A. However, the Pl,A. is the belief that an element belo..."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678941","https://noah.nrw/i3f/v20/3560985/canvas/3678944"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3678939","https://noah.nrw/i3f/v20/3560985/range/3678940","https://noah.nrw/i3f/v20/3560985/range/3678942","https://noah.nrw/i3f/v20/3560985/range/3678943"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678938","@type":"sc:Range","label":"3.4.3.     Possibilities Theory:"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678941"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678940","@type":"sc:Range","label":"For simplicity, assume there is a set U, and then the possibility distribution is the characteristic function of a subset E of U. It will model the situation in which all is known about x is that it cannot be exist outside E, thus, (x(u) = 1 if x \u03f5 E ..."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678944"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678943","@type":"sc:Range","label":"Furthermore, from (3.30 and 3.31) as a special case:"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678948","https://noah.nrw/i3f/v20/3560985/canvas/3678955","https://noah.nrw/i3f/v20/3560985/canvas/3678961","https://noah.nrw/i3f/v20/3560985/canvas/3678964","https://noah.nrw/i3f/v20/3560985/canvas/3678965","https://noah.nrw/i3f/v20/3560985/canvas/3678967","https://noah.nrw/i3f/v20/3560985/canvas/3678969","https://noah.nrw/i3f/v20/3560985/canvas/3678970"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3678946","https://noah.nrw/i3f/v20/3560985/range/3678947","https://noah.nrw/i3f/v20/3560985/range/3678949","https://noah.nrw/i3f/v20/3560985/range/3678950","https://noah.nrw/i3f/v20/3560985/range/3678951","https://noah.nrw/i3f/v20/3560985/range/3678952","https://noah.nrw/i3f/v20/3560985/range/3678953","https://noah.nrw/i3f/v20/3560985/range/3678954","https://noah.nrw/i3f/v20/3560985/range/3678956","https://noah.nrw/i3f/v20/3560985/range/3678957","https://noah.nrw/i3f/v20/3560985/range/3678958","https://noah.nrw/i3f/v20/3560985/range/3678959","https://noah.nrw/i3f/v20/3560985/range/3678960","https://noah.nrw/i3f/v20/3560985/range/3678962","https://noah.nrw/i3f/v20/3560985/range/3678963","https://noah.nrw/i3f/v20/3560985/range/3678966","https://noah.nrw/i3f/v20/3560985/range/3678968"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678945","@type":"sc:Range","label":"3.4.4.     Possibility Approach"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678948"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678947","@type":"sc:Range","label":"Definition 1 [147-150]: In order to derive a possibility distribution from an empirical data, it is indispensable to demonstrate the general measuring record subsets ,\ud835\udc14-\ud835\udc2c.\u2286\ud835\udec0 as defined below:"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678955"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678954","@type":"sc:Range","label":"Where ,\ud835\udec7-\ud835\udc2c. is the evidence function ,\u03b6-s.=\u03b6(,U-s.)."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678961"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678960","@type":"sc:Range","label":"Definition 9 [147-150]: Even though the possibilistic histograms are similar to ordinary stochastic histograms, but they are really performed from overlapping interval observations; thus they are ruled by the mathematics of random sets. If \u03c0 is a poss..."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678964","https://noah.nrw/i3f/v20/3560985/canvas/3678965"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678963","@type":"sc:Range","label":"For example let us consider that we have done an experiment and collect the observed data. These data categorized into two main intervals A1 = {a, b} and A2= {c, d}. If each intervals observed once that means m(A1)=m(A2)=0.5. The interval ,\ud835\udc72-\u2032.=,,,\ud835\udc94..."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678967"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678966","@type":"sc:Range","label":"As it was mentioned previously that the membership functions are trapezoidal and could be designed by possibilities histograms. These kinds of membership functions are general enough and widely used besides they need less computational time compare to..."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678969","https://noah.nrw/i3f/v20/3560985/canvas/3678970"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678968","@type":"sc:Range","label":"Where ,\ud835\udc4e-\ud835\udc56\ud835\udc57.,,\ud835\udc4f-\ud835\udc56\ud835\udc57.,,\ud835\udc50-\ud835\udc56\ud835\udc57., ,\ud835\udc51-\ud835\udc56\ud835\udc57. are the breakpoints of the trapezoidal membership functions and ,\ud835\udc39-\ud835\udc56\ud835\udc57. and ,\ud835\udc3b-\ud835\udc56\ud835\udc57. are the new breakpoints for left and right sides for ith rule and jth input variables. An example for bacterial algo..."},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678972","https://noah.nrw/i3f/v20/3560985/canvas/3678973"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678971","@type":"sc:Range","label":"3.5. Communications"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678976","https://noah.nrw/i3f/v20/3560985/canvas/3678977","https://noah.nrw/i3f/v20/3560985/canvas/3678979","https://noah.nrw/i3f/v20/3560985/canvas/3678982","https://noah.nrw/i3f/v20/3560985/canvas/3678983","https://noah.nrw/i3f/v20/3560985/canvas/3678984","https://noah.nrw/i3f/v20/3560985/canvas/3678985","https://noah.nrw/i3f/v20/3560985/canvas/3678986","https://noah.nrw/i3f/v20/3560985/canvas/3678987","https://noah.nrw/i3f/v20/3560985/canvas/3678988","https://noah.nrw/i3f/v20/3560985/canvas/3678989","https://noah.nrw/i3f/v20/3560985/canvas/3678990","https://noah.nrw/i3f/v20/3560985/canvas/3678991","https://noah.nrw/i3f/v20/3560985/canvas/3678992","https://noah.nrw/i3f/v20/3560985/canvas/3678993","https://noah.nrw/i3f/v20/3560985/canvas/3678994"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3678975","https://noah.nrw/i3f/v20/3560985/range/3678978","https://noah.nrw/i3f/v20/3560985/range/3678980","https://noah.nrw/i3f/v20/3560985/range/3678981"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678974","@type":"sc:Range","label":"3.6. Wireless Sensor Networks Tracking"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678976","https://noah.nrw/i3f/v20/3560985/canvas/3678977"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678975","@type":"sc:Range","label":"3.6.1.     System Model and Problem Formulation"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678979"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678978","@type":"sc:Range","label":"3.6.2.     Processing at Sensors"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678982","https://noah.nrw/i3f/v20/3560985/canvas/3678983","https://noah.nrw/i3f/v20/3560985/canvas/3678984","https://noah.nrw/i3f/v20/3560985/canvas/3678985","https://noah.nrw/i3f/v20/3560985/canvas/3678986","https://noah.nrw/i3f/v20/3560985/canvas/3678987","https://noah.nrw/i3f/v20/3560985/canvas/3678988","https://noah.nrw/i3f/v20/3560985/canvas/3678989","https://noah.nrw/i3f/v20/3560985/canvas/3678990","https://noah.nrw/i3f/v20/3560985/canvas/3678991","https://noah.nrw/i3f/v20/3560985/canvas/3678992","https://noah.nrw/i3f/v20/3560985/canvas/3678993","https://noah.nrw/i3f/v20/3560985/canvas/3678994"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678981","@type":"sc:Range","label":"3.6.4.     Cramer-Rao Lower Bound (CRLB)"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678996"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678995","@type":"sc:Range","label":"3.7 Conclusion"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678999","https://noah.nrw/i3f/v20/3560985/canvas/3679002","https://noah.nrw/i3f/v20/3560985/canvas/3679003","https://noah.nrw/i3f/v20/3560985/canvas/3679004","https://noah.nrw/i3f/v20/3560985/canvas/3679005","https://noah.nrw/i3f/v20/3560985/canvas/3679007","https://noah.nrw/i3f/v20/3560985/canvas/3679008","https://noah.nrw/i3f/v20/3560985/canvas/3679009","https://noah.nrw/i3f/v20/3560985/canvas/3679010","https://noah.nrw/i3f/v20/3560985/canvas/3679011","https://noah.nrw/i3f/v20/3560985/canvas/3679012","https://noah.nrw/i3f/v20/3560985/canvas/3679013","https://noah.nrw/i3f/v20/3560985/canvas/3679015","https://noah.nrw/i3f/v20/3560985/canvas/3679016","https://noah.nrw/i3f/v20/3560985/canvas/3679017","https://noah.nrw/i3f/v20/3560985/canvas/3679019","https://noah.nrw/i3f/v20/3560985/canvas/3679020","https://noah.nrw/i3f/v20/3560985/canvas/3679021","https://noah.nrw/i3f/v20/3560985/canvas/3679022","https://noah.nrw/i3f/v20/3560985/canvas/3679023","https://noah.nrw/i3f/v20/3560985/canvas/3679025","https://noah.nrw/i3f/v20/3560985/canvas/3679027","https://noah.nrw/i3f/v20/3560985/canvas/3679029","https://noah.nrw/i3f/v20/3560985/canvas/3679030","https://noah.nrw/i3f/v20/3560985/canvas/3679031","https://noah.nrw/i3f/v20/3560985/canvas/3679032","https://noah.nrw/i3f/v20/3560985/canvas/3679034","https://noah.nrw/i3f/v20/3560985/canvas/3679035","https://noah.nrw/i3f/v20/3560985/canvas/3679036","https://noah.nrw/i3f/v20/3560985/canvas/3679039","https://noah.nrw/i3f/v20/3560985/canvas/3679040","https://noah.nrw/i3f/v20/3560985/canvas/3679041","https://noah.nrw/i3f/v20/3560985/canvas/3679042","https://noah.nrw/i3f/v20/3560985/canvas/3679043","https://noah.nrw/i3f/v20/3560985/canvas/3679044","https://noah.nrw/i3f/v20/3560985/canvas/3679046","https://noah.nrw/i3f/v20/3560985/canvas/3679047","https://noah.nrw/i3f/v20/3560985/canvas/3679048","https://noah.nrw/i3f/v20/3560985/canvas/3679049","https://noah.nrw/i3f/v20/3560985/canvas/3679050","https://noah.nrw/i3f/v20/3560985/canvas/3679051","https://noah.nrw/i3f/v20/3560985/canvas/3679053","https://noah.nrw/i3f/v20/3560985/canvas/3679054","https://noah.nrw/i3f/v20/3560985/canvas/3679055","https://noah.nrw/i3f/v20/3560985/canvas/3679056","https://noah.nrw/i3f/v20/3560985/canvas/3679058"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3678998","https://noah.nrw/i3f/v20/3560985/range/3679000","https://noah.nrw/i3f/v20/3560985/range/3679037","https://noah.nrw/i3f/v20/3560985/range/3679057"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678997","@type":"sc:Range","label":"Chapter 4 Computer Vision for Blimp Robot"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3678999"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3678998","@type":"sc:Range","label":"4.1. Introduction"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679002","https://noah.nrw/i3f/v20/3560985/canvas/3679003","https://noah.nrw/i3f/v20/3560985/canvas/3679004","https://noah.nrw/i3f/v20/3560985/canvas/3679005","https://noah.nrw/i3f/v20/3560985/canvas/3679007","https://noah.nrw/i3f/v20/3560985/canvas/3679008","https://noah.nrw/i3f/v20/3560985/canvas/3679009","https://noah.nrw/i3f/v20/3560985/canvas/3679010","https://noah.nrw/i3f/v20/3560985/canvas/3679011","https://noah.nrw/i3f/v20/3560985/canvas/3679012","https://noah.nrw/i3f/v20/3560985/canvas/3679013","https://noah.nrw/i3f/v20/3560985/canvas/3679015","https://noah.nrw/i3f/v20/3560985/canvas/3679016","https://noah.nrw/i3f/v20/3560985/canvas/3679017","https://noah.nrw/i3f/v20/3560985/canvas/3679019","https://noah.nrw/i3f/v20/3560985/canvas/3679020","https://noah.nrw/i3f/v20/3560985/canvas/3679021","https://noah.nrw/i3f/v20/3560985/canvas/3679022","https://noah.nrw/i3f/v20/3560985/canvas/3679023","https://noah.nrw/i3f/v20/3560985/canvas/3679025","https://noah.nrw/i3f/v20/3560985/canvas/3679027","https://noah.nrw/i3f/v20/3560985/canvas/3679029","https://noah.nrw/i3f/v20/3560985/canvas/3679030","https://noah.nrw/i3f/v20/3560985/canvas/3679031","https://noah.nrw/i3f/v20/3560985/canvas/3679032","https://noah.nrw/i3f/v20/3560985/canvas/3679034","https://noah.nrw/i3f/v20/3560985/canvas/3679035","https://noah.nrw/i3f/v20/3560985/canvas/3679036"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3679001","https://noah.nrw/i3f/v20/3560985/range/3679006","https://noah.nrw/i3f/v20/3560985/range/3679014","https://noah.nrw/i3f/v20/3560985/range/3679018","https://noah.nrw/i3f/v20/3560985/range/3679024"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679000","@type":"sc:Range","label":"4.2. Image Based Visual Tracking"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679002","https://noah.nrw/i3f/v20/3560985/canvas/3679003","https://noah.nrw/i3f/v20/3560985/canvas/3679004","https://noah.nrw/i3f/v20/3560985/canvas/3679005"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679001","@type":"sc:Range","label":"4.2.1.     Optical Flow and Appearance Tracking"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679007","https://noah.nrw/i3f/v20/3560985/canvas/3679008","https://noah.nrw/i3f/v20/3560985/canvas/3679009","https://noah.nrw/i3f/v20/3560985/canvas/3679010","https://noah.nrw/i3f/v20/3560985/canvas/3679011","https://noah.nrw/i3f/v20/3560985/canvas/3679012","https://noah.nrw/i3f/v20/3560985/canvas/3679013"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679006","@type":"sc:Range","label":"4.2.2.     Feature Descriptors and Tracking"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679015","https://noah.nrw/i3f/v20/3560985/canvas/3679016","https://noah.nrw/i3f/v20/3560985/canvas/3679017"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679014","@type":"sc:Range","label":"4.2.3.     Robust Matching and Homography"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679019","https://noah.nrw/i3f/v20/3560985/canvas/3679020","https://noah.nrw/i3f/v20/3560985/canvas/3679021","https://noah.nrw/i3f/v20/3560985/canvas/3679022","https://noah.nrw/i3f/v20/3560985/canvas/3679023"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679018","@type":"sc:Range","label":"4.2.4.     Inverse Perspective Mapping"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679025","https://noah.nrw/i3f/v20/3560985/canvas/3679027","https://noah.nrw/i3f/v20/3560985/canvas/3679029","https://noah.nrw/i3f/v20/3560985/canvas/3679030","https://noah.nrw/i3f/v20/3560985/canvas/3679031","https://noah.nrw/i3f/v20/3560985/canvas/3679032","https://noah.nrw/i3f/v20/3560985/canvas/3679034","https://noah.nrw/i3f/v20/3560985/canvas/3679035","https://noah.nrw/i3f/v20/3560985/canvas/3679036"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3679026","https://noah.nrw/i3f/v20/3560985/range/3679028","https://noah.nrw/i3f/v20/3560985/range/3679033"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679024","@type":"sc:Range","label":"4.2.5.     Fuzzy Image Processing"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679027"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679026","@type":"sc:Range","label":"4.2.5.1.     Applications of Fuzzy Image Processing"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679029","https://noah.nrw/i3f/v20/3560985/canvas/3679030","https://noah.nrw/i3f/v20/3560985/canvas/3679031","https://noah.nrw/i3f/v20/3560985/canvas/3679032"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679028","@type":"sc:Range","label":"4.2.5.2.     Fuzzy Edge Detection"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679034","https://noah.nrw/i3f/v20/3560985/canvas/3679035","https://noah.nrw/i3f/v20/3560985/canvas/3679036"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679033","@type":"sc:Range","label":"4.2.5.3.     Fuzzy Clustering"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679039","https://noah.nrw/i3f/v20/3560985/canvas/3679040","https://noah.nrw/i3f/v20/3560985/canvas/3679041","https://noah.nrw/i3f/v20/3560985/canvas/3679042","https://noah.nrw/i3f/v20/3560985/canvas/3679043","https://noah.nrw/i3f/v20/3560985/canvas/3679044","https://noah.nrw/i3f/v20/3560985/canvas/3679046","https://noah.nrw/i3f/v20/3560985/canvas/3679047","https://noah.nrw/i3f/v20/3560985/canvas/3679048","https://noah.nrw/i3f/v20/3560985/canvas/3679049","https://noah.nrw/i3f/v20/3560985/canvas/3679050","https://noah.nrw/i3f/v20/3560985/canvas/3679051","https://noah.nrw/i3f/v20/3560985/canvas/3679053","https://noah.nrw/i3f/v20/3560985/canvas/3679054","https://noah.nrw/i3f/v20/3560985/canvas/3679055","https://noah.nrw/i3f/v20/3560985/canvas/3679056"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3679038","https://noah.nrw/i3f/v20/3560985/range/3679045","https://noah.nrw/i3f/v20/3560985/range/3679052"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679037","@type":"sc:Range","label":"4.3. Blimp's Embedded Computer Vision"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679039","https://noah.nrw/i3f/v20/3560985/canvas/3679040","https://noah.nrw/i3f/v20/3560985/canvas/3679041","https://noah.nrw/i3f/v20/3560985/canvas/3679042","https://noah.nrw/i3f/v20/3560985/canvas/3679043","https://noah.nrw/i3f/v20/3560985/canvas/3679044"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679038","@type":"sc:Range","label":"4.3.1.     Detect and Follow Ground Robot"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679046","https://noah.nrw/i3f/v20/3560985/canvas/3679047","https://noah.nrw/i3f/v20/3560985/canvas/3679048","https://noah.nrw/i3f/v20/3560985/canvas/3679049","https://noah.nrw/i3f/v20/3560985/canvas/3679050","https://noah.nrw/i3f/v20/3560985/canvas/3679051"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679045","@type":"sc:Range","label":"4.3.2.     Ground Robot Navigation"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679053","https://noah.nrw/i3f/v20/3560985/canvas/3679054","https://noah.nrw/i3f/v20/3560985/canvas/3679055","https://noah.nrw/i3f/v20/3560985/canvas/3679056"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679052","@type":"sc:Range","label":"4.3.3.     Blimp Robot Follows 3-D Aerial Object"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679058"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679057","@type":"sc:Range","label":"4.4 Conclusions"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679060","https://noah.nrw/i3f/v20/3560985/canvas/3679063","https://noah.nrw/i3f/v20/3560985/canvas/3679064","https://noah.nrw/i3f/v20/3560985/canvas/3679065","https://noah.nrw/i3f/v20/3560985/canvas/3679066","https://noah.nrw/i3f/v20/3560985/canvas/3679067","https://noah.nrw/i3f/v20/3560985/canvas/3679068","https://noah.nrw/i3f/v20/3560985/canvas/3679069","https://noah.nrw/i3f/v20/3560985/canvas/3679071","https://noah.nrw/i3f/v20/3560985/canvas/3679072","https://noah.nrw/i3f/v20/3560985/canvas/3679073","https://noah.nrw/i3f/v20/3560985/canvas/3679074","https://noah.nrw/i3f/v20/3560985/canvas/3679077","https://noah.nrw/i3f/v20/3560985/canvas/3679078","https://noah.nrw/i3f/v20/3560985/canvas/3679079","https://noah.nrw/i3f/v20/3560985/canvas/3679080","https://noah.nrw/i3f/v20/3560985/canvas/3679081","https://noah.nrw/i3f/v20/3560985/canvas/3679082","https://noah.nrw/i3f/v20/3560985/canvas/3679083","https://noah.nrw/i3f/v20/3560985/canvas/3679084","https://noah.nrw/i3f/v20/3560985/canvas/3679085","https://noah.nrw/i3f/v20/3560985/canvas/3679086","https://noah.nrw/i3f/v20/3560985/canvas/3679087","https://noah.nrw/i3f/v20/3560985/canvas/3679088","https://noah.nrw/i3f/v20/3560985/canvas/3679089","https://noah.nrw/i3f/v20/3560985/canvas/3679091","https://noah.nrw/i3f/v20/3560985/canvas/3679092","https://noah.nrw/i3f/v20/3560985/canvas/3679093","https://noah.nrw/i3f/v20/3560985/canvas/3679094","https://noah.nrw/i3f/v20/3560985/canvas/3679095","https://noah.nrw/i3f/v20/3560985/canvas/3679096","https://noah.nrw/i3f/v20/3560985/canvas/3679097","https://noah.nrw/i3f/v20/3560985/canvas/3679098","https://noah.nrw/i3f/v20/3560985/canvas/3679099","https://noah.nrw/i3f/v20/3560985/canvas/3679101","https://noah.nrw/i3f/v20/3560985/canvas/3679102","https://noah.nrw/i3f/v20/3560985/canvas/3679103","https://noah.nrw/i3f/v20/3560985/canvas/3679104","https://noah.nrw/i3f/v20/3560985/canvas/3679105","https://noah.nrw/i3f/v20/3560985/canvas/3679106","https://noah.nrw/i3f/v20/3560985/canvas/3679107","https://noah.nrw/i3f/v20/3560985/canvas/3679108","https://noah.nrw/i3f/v20/3560985/canvas/3679110","https://noah.nrw/i3f/v20/3560985/canvas/3679111"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3679061","https://noah.nrw/i3f/v20/3560985/range/3679075","https://noah.nrw/i3f/v20/3560985/range/3679100","https://noah.nrw/i3f/v20/3560985/range/3679109"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679059","@type":"sc:Range","label":"Chapter 5 Experiments and Results"},{"canvases":["https://noah.nrw/i3f/v20/3560985/canvas/3679063","https://noah.nrw/i3f/v20/3560985/canvas/3679064","https://noah.nrw/i3f/v20/3560985/canvas/3679065","https://noah.nrw/i3f/v20/3560985/canvas/3679066","https://noah.nrw/i3f/v20/3560985/canvas/3679067","https://noah.nrw/i3f/v20/3560985/canvas/3679068","https://noah.nrw/i3f/v20/3560985/canvas/3679069","https://noah.nrw/i3f/v20/3560985/canvas/3679071","https://noah.nrw/i3f/v20/3560985/canvas/3679072","https://noah.nrw/i3f/v20/3560985/canvas/3679073","https://noah.nrw/i3f/v20/3560985/canvas/3679074"],"ranges":["https://noah.nrw/i3f/v20/3560985/range/3679062","https://noah.nrw/i3f/v20/3560985/range/3679070"],"@id":"https://noah.nrw/i3f/v20/3560985/range/3679061","@type":"sc:Range","label":"5.1 Fuzzy Set Model for Blimp's 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