三維空間有向點關(guān)系代數(shù)的推理研究
發(fā)布時間：2018-08-04 20:59
【摘要】：空間和時間是人類認(rèn)識世界無法回避的問題,也是人工智能研究的熱門主題。定性時空推理正是在這兩個問題上的嘗試。它首先將時間或空間抽象為對象,然后使用定性的描述符號表示空間或時間間的關(guān)系,最后研究這些關(guān)系之間的變化規(guī)律。定性空間推理可以應(yīng)用于機器人導(dǎo)航、無線傳感器網(wǎng)絡(luò)(WSN),用于處理其中的不確定空間信息。在地理信息系統(tǒng)(GIS)中,定性空間關(guān)系作為查詢謂詞可以用于空間查詢,這得益于定性模型與自然語言接近,易于理解的優(yōu)勢。定性空間推理也應(yīng)用于圖像檢索和分類,因為興趣區(qū)域間的空間關(guān)系可以作為重要的圖像特征。方向關(guān)系是眾多空間關(guān)系之一,而有向點關(guān)系代數(shù)又是方向關(guān)系模型中極為優(yōu)秀的一個。有向點關(guān)系代數(shù)(Oriented Point Relation Algebra,OPRA)研究空間中物體之間的方向關(guān)系以及方向關(guān)系之間的推理問題。它把空間對象建模為帶方向的點,稱為有向點。然后通過兩個有向點之間的連線與有向點自身方向之間的兩個夾角來表示方向關(guān)系,當(dāng)然對于特殊情況有特殊的處理。有向點關(guān)系代數(shù)是一個定性關(guān)系模型,它在使用夾角時,把角度或角度區(qū)間用一個整數(shù)代號來表示。這使得它具有以下幾個優(yōu)勢:(1)與人們認(rèn)知方向的一般模式相吻合。例如,人們走在馬路上時說,“前方”有一輛車,十字路口“左”轉(zhuǎn),這里的“前”與“左”可以看做一個角度區(qū)間的代號。(2)可以從各種數(shù)據(jù)中提取信息建模,例如,文本、語音、攝像頭等,當(dāng)然使用更為精確的GPS也是可以的,這就使得有向點關(guān)系代數(shù)成為一個可以廣泛使用的模型。(3)使用有向點關(guān)系代數(shù)表示的方向關(guān)系易于理解,例如在二維空間,對于粒度為1的模型,其方向代號0,1,2,3分別代表上、左、下、右。(4)能適應(yīng)不同精度需求的場景,有向點關(guān)系代數(shù)有一個可以調(diào)整的粒度參數(shù),如果簡單的上下左右不能滿足對方向的定位需求,可以使用著名的點鐘方向,當(dāng)然該模型還可以提供更多其他的精度。推理問題是關(guān)系模型的一個重要問題。它使用已知的關(guān)系求取未知的關(guān)系。假設(shè)空間對象A和B相互可見,B和C相互可見,但是A和C由于各種原因相互不可見。這時,可以用已知的關(guān)系(A,B),(B,C)推理得出未知的關(guān)系(A,C)。這就是推理問題。很多關(guān)系模型過于復(fù)雜,在其上的推理很難進行,例如Pacheco的Integrating 3D Orientation模型。而有向點關(guān)系代數(shù)的模型簡單,在其上的推理很容易進行。2012年,Mossakowski等人研究了OPRA的復(fù)合運算。2014年,王生生等研究了OPRA的多粒度復(fù)合推理,使得OPRA推理可以應(yīng)用于任意混合粒度之間�，F(xiàn)有的工作大都是在二維空間中進行的,涉及的對象也只有三個,場景多數(shù)也是靜態(tài)的。而現(xiàn)實場景常常是三維的,也常常涉及更多的對象,同時需要處理的場景也常常是動態(tài)的,這就需要新的模型和推理方法。本文針對這些問題展開了如下討論:(1).三維空間中的有向點表示與三維有向點關(guān)系代數(shù)模型(Oriented Point Relation Algebra in 3-Dimension,OPRA3D)(2).OPRA3D的復(fù)合推理(3).涉及多個對象的動態(tài)的OPRA3D關(guān)系的表示和推理對于OPRA3D模型的表示,本文從有向點的建模出發(fā),逐步建立整個表示模型。對于OPRA3D的復(fù)合推理,本文給出了三維空間的兩個幾何約束及其定性化形式,從而使用這兩個約束構(gòu)造了OPRA3D的復(fù)合推理算法,同時,本文還簡單討論了OPRA3D的多粒度推理問題。在面對多對象動態(tài)場景時,OPRA3D也能發(fā)揮作用,本文提出了OPRA3D關(guān)系網(wǎng)絡(luò)及其序列描述這種場景中對象間的方向關(guān)系,提出了關(guān)系網(wǎng)絡(luò)的時空推理來處理多對象動態(tài)場景中的推理問題。最后,本文討論了在工程中使用OPRA3D推理算法需要考慮的一個問題,并給出一個模擬實驗來驗證OPRA3D網(wǎng)絡(luò)時空推理的有效性。本文的建模方法和推理算法在處理三維空間中物體的方向關(guān)系時具有潛在價值。在機器人導(dǎo)航、無人機導(dǎo)航、太空導(dǎo)航、戰(zhàn)場分析等領(lǐng)域,,本文的方法有望發(fā)揮重要作用。
[Abstract]:Space and time are an unavoidable problem in the human understanding of the world. It is also a hot topic in the research of artificial intelligence. Qualitative spatio-temporal reasoning is an attempt on these two problems. It first abstracts time or space as an object, and then uses qualitative description symbols to express the relationship between space or time, and finally studies the changes between these relationships. Qualitative spatial reasoning can be applied to robot navigation and wireless sensor networks (WSN) to deal with uncertain spatial information. In geographic information system (GIS), qualitative spatial relations can be used as query predicates for spatial queries, which benefit from the proximity of qualitative models to natural languages and the predominance of easy understanding. Qualitative space is the advantage. Inter reasoning is also applied to image retrieval and classification, because the spatial relationship between interested regions can be regarded as an important image feature. The direction relation is one of the many spatial relationships, and the directed point relation algebra is a very excellent one in the direction relation model. The Oriented Point Relation Algebra (OPRA) research space The reasoning problem between the direction relation and the direction relationship between the objects. It models the spatial object as the point with direction, which is called the directed point. Then, it expresses the direction relationship by the two angles between the connection lines between two directed points and the direction of the directed point, and of course, it has special treatment for special cases. A number is a qualitative relation model, which is represented by an integer code in the angle or angle interval when using the angle. This makes it have the following advantages: (1) it is in accordance with the general pattern of people's cognitive direction. For example, when people walk on the road, there is a car ahead, the intersection "left", the "front" here. And "left" can be regarded as the code name of an angle interval. (2) it is possible to extract information from various data, such as text, voice, camera, etc., of course, using a more precise GPS, which makes the directed point relation algebra a widely used model. (3) use the direction of the directed point relation algebra. The system is easy to understand, for example, in a two-dimensional space, for a model with granularity 1, its direction code 0,1,2,3 is represented, left, lower, right. (4) can adapt to different precision requirements. There is an adjustable granularity parameter for the point relation algebra. If simple up and down is not enough to meet the orientation requirement of the direction, it can be used famous A and B are visible to each other, but B and C are visible to each other, but A and C are invisible to each other for a variety of reasons. Then, we can use a known relationship (A, B), (B, C) inference is an unknown relationship (A, C). This is a reasoning problem. Many relational models are too complex, and the reasoning on it is difficult to carry out, such as the Integrating 3D Orientation model of Pacheco. The model with the directed point relation algebra is simple, and the reasoning on it is easy to carry out.2012 years, Mossakowski and others have studied the complex operation.201 of OPRA. In the 4 years, Wang Shengsheng has studied the multi granularity complex reasoning of OPRA so that OPRA reasoning can be applied to any mixed granularity. Most of the existing work is carried out in the two-dimensional space, only three objects are involved, and most of the scenes are static. The real scene is often three-dimensional, often involving more objects and needs at the same time. The processing scene is also often dynamic, which requires new models and reasoning methods. This paper discusses these problems as follows: (1) the complex reasoning of the directed point representation in three-dimensional space and the Oriented Point Relation Algebra in 3-Dimension, OPRA3D (2) (3) (3). It involves multiple objects. The representation and reasoning of dynamic OPRA3D relations for the representation of the OPRA3D model, this paper starts from the modeling of the directed point, and gradually establishes the entire representation model. For the complex reasoning of OPRA3D, this paper gives two geometric constraints and the qualitative forms of the three-dimensional space, and then constructs a compound inference algorithm of the OPRA3D by using these two constraints. At the same time, this paper also briefly discusses the multi granularity reasoning problem of OPRA3D. In the face of multi object dynamic scenes, OPRA3D can also play a role. In this paper, the OPRA3D relation network and its sequence are proposed to describe the direction relation among the objects in this scene, and the spatio-temporal reasoning of the relational network is proposed to deal with the reasoning problem in the multi object dynamic scene. Finally, this paper discusses a problem that needs to be considered in the use of OPRA3D reasoning algorithm in engineering, and gives a simulation experiment to verify the effectiveness of OPRA3D network spatiotemporal reasoning. The modeling method and inference algorithm in this paper have potential value when dealing with the direction relation of objects in three-dimensional space. In the field of space navigation and battlefield analysis, the method of this paper is expected to play an important role.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18
本文編號：2165116
[Abstract]:Space and time are an unavoidable problem in the human understanding of the world. It is also a hot topic in the research of artificial intelligence. Qualitative spatio-temporal reasoning is an attempt on these two problems. It first abstracts time or space as an object, and then uses qualitative description symbols to express the relationship between space or time, and finally studies the changes between these relationships. Qualitative spatial reasoning can be applied to robot navigation and wireless sensor networks (WSN) to deal with uncertain spatial information. In geographic information system (GIS), qualitative spatial relations can be used as query predicates for spatial queries, which benefit from the proximity of qualitative models to natural languages and the predominance of easy understanding. Qualitative space is the advantage. Inter reasoning is also applied to image retrieval and classification, because the spatial relationship between interested regions can be regarded as an important image feature. The direction relation is one of the many spatial relationships, and the directed point relation algebra is a very excellent one in the direction relation model. The Oriented Point Relation Algebra (OPRA) research space The reasoning problem between the direction relation and the direction relationship between the objects. It models the spatial object as the point with direction, which is called the directed point. Then, it expresses the direction relationship by the two angles between the connection lines between two directed points and the direction of the directed point, and of course, it has special treatment for special cases. A number is a qualitative relation model, which is represented by an integer code in the angle or angle interval when using the angle. This makes it have the following advantages: (1) it is in accordance with the general pattern of people's cognitive direction. For example, when people walk on the road, there is a car ahead, the intersection "left", the "front" here. And "left" can be regarded as the code name of an angle interval. (2) it is possible to extract information from various data, such as text, voice, camera, etc., of course, using a more precise GPS, which makes the directed point relation algebra a widely used model. (3) use the direction of the directed point relation algebra. The system is easy to understand, for example, in a two-dimensional space, for a model with granularity 1, its direction code 0,1,2,3 is represented, left, lower, right. (4) can adapt to different precision requirements. There is an adjustable granularity parameter for the point relation algebra. If simple up and down is not enough to meet the orientation requirement of the direction, it can be used famous A and B are visible to each other, but B and C are visible to each other, but A and C are invisible to each other for a variety of reasons. Then, we can use a known relationship (A, B), (B, C) inference is an unknown relationship (A, C). This is a reasoning problem. Many relational models are too complex, and the reasoning on it is difficult to carry out, such as the Integrating 3D Orientation model of Pacheco. The model with the directed point relation algebra is simple, and the reasoning on it is easy to carry out.2012 years, Mossakowski and others have studied the complex operation.201 of OPRA. In the 4 years, Wang Shengsheng has studied the multi granularity complex reasoning of OPRA so that OPRA reasoning can be applied to any mixed granularity. Most of the existing work is carried out in the two-dimensional space, only three objects are involved, and most of the scenes are static. The real scene is often three-dimensional, often involving more objects and needs at the same time. The processing scene is also often dynamic, which requires new models and reasoning methods. This paper discusses these problems as follows: (1) the complex reasoning of the directed point representation in three-dimensional space and the Oriented Point Relation Algebra in 3-Dimension, OPRA3D (2) (3) (3). It involves multiple objects. The representation and reasoning of dynamic OPRA3D relations for the representation of the OPRA3D model, this paper starts from the modeling of the directed point, and gradually establishes the entire representation model. For the complex reasoning of OPRA3D, this paper gives two geometric constraints and the qualitative forms of the three-dimensional space, and then constructs a compound inference algorithm of the OPRA3D by using these two constraints. At the same time, this paper also briefly discusses the multi granularity reasoning problem of OPRA3D. In the face of multi object dynamic scenes, OPRA3D can also play a role. In this paper, the OPRA3D relation network and its sequence are proposed to describe the direction relation among the objects in this scene, and the spatio-temporal reasoning of the relational network is proposed to deal with the reasoning problem in the multi object dynamic scene. Finally, this paper discusses a problem that needs to be considered in the use of OPRA3D reasoning algorithm in engineering, and gives a simulation experiment to verify the effectiveness of OPRA3D network spatiotemporal reasoning. The modeling method and inference algorithm in this paper have potential value when dealing with the direction relation of objects in three-dimensional space. In the field of space navigation and battlefield analysis, the method of this paper is expected to play an important role.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18
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