臺灣博碩士論文加值系統

物體辨識已在工業自動化領域上引起廣泛的興趣。雖然有很多不同的方法已被用來處理此辨識問題，但在某些情況下，如重疊物體、關節物體及低解析度之影像，很難以傳統之辨識方法來達成。在此領域中處理此複雜影像卻仍是一項考驗。
為了從此影像中辨識物體，本論文提出一新的整合性方法。對於具重疊物體、關節物體之影像，可將圖形之比對問題視為求解組合最佳化問題，而霍菲爾類神經網路和遺傳演算法在解此問題上被視為是一很強的工具。但不幸地，此兩者工具都有其嚴重之缺點，霍菲爾類神經網路對於起始狀態之設定非常敏感，若起始狀態選取不恰當，則系統將限於局部最小值。另一方面，遺傳演算法則僅只找到近似最佳解，且對於較大尺寸問題之處理非常耗時。因此本論文提出整合此兩者之演算法，刪除兩者之缺點，並保留其優點來解此複雜辨識問題。當然在整合前，必要有一些準備工作。例如，本方法使用二維遺傳演算法之演算子來加速其收斂。並且，僅從遺傳演算法之解中擷取”種子”作為霍菲爾類神經網路之初始狀態，藉此程序可進一步改善系統之效率。此外，本系統亦包含數個使所求解更精確的後處理演算法。
為求解同形的圖形比對問題(homomorphic graph matching)，亦即有一物體重覆多次出現在一影像中，霍菲爾類神經網路將重複執行多次直到系統滿足停止條件。本方法不僅可用來得到樣本圖形與影像圖形間之同形比對，亦可用來作為關節物體辨識。在此，我們事先並不知樣本是否為關節物體。本提議法可偵測出此關節物體之運動特性，如關節物體中關節之位置，相對的線性位移和角位移。有關於關節物體辨識之研究主題，在過去文獻中很少被提及，特別是在仿射轉換之條件下。
在本文中亦包含本提議法之另一應用，即低解析度影像之辨識。對於低解析度之影像，物體的輪廓很容易受雜訊所影響。為增加執行性能，本文使用六角格子來處理低解析度影像。一模擬之六角快速傅立葉轉換首先被提出，其可用來作為辨識六角格子影像之前處理。利用特徵向量比對法及相似比對法來辨識具孤立物體之簡單影像。對於具重疊物體之低解析度影像，則修改此整合法使其適用於六角格子之影像辨識。此低解析六角格子之整合法已被證實是適用且具強健性的。

Object recognition is of much interest in recent industrial automation. Although a variety of approaches have been proposed to tackle the recognition problem, some cases such as overlapping objects, articulated objects, and low-resolution images, are still not easy for the existing schemes. Coping with these more complex images has remained a challenging task in the field.
This dissertation, aiming to recognize objects from such images, proposes a new integrated method. For images with overlapping or articulated objects, graph matching methods are often used, seeing them as solving a combinatorial optimization problem. Both Hopfield network and the genetic algorithm are decent tools for the combinatorial optimization problems. Unfortunately, they both have intolerable drawbacks. The Hopfield network is sensitive to its initial state and stops at a local minimum if it is not properly given. The GA, on the other hand, only finds a near-global solution, and it is time-consuming for large-scale tasks. This dissertation proposes to combine these two methods, while eliminating their bad and keeping their good, to solve some complex recognition problems. Before the integration, some arrangements are required. For instance, specialized 2-D GA operators are used to accelerate the convergence. Also, the “seeds” of the solution of the GA is extracted as the initial state of the Hopfield network. By doing so the efficiency of the system is greatly improved. Additionally, several fine-tuning post matching algorithms are also needed.
In order to solve the homomorphic graph matching problem, i.e., multiple occurrences in a single scene image, the Hopfield network has to repeat itself until the stopping criteria are met. The method can not only be used to obtain the homomorphic mapping between the model and the scene graphs, but it can also be applied to articulated object recognition. Here we do not need to know in advance if the model is really an articulated object. The proposed method has been applied to measure some kinematic properties, such as the positions of the joints, relative linear and angular displacements, of some simple machines. The subject about articulated object recognition has rarely been mentioned in the literature, particularly under affine transformations.
Another unique application of the proposed method is also included in the dissertation. It is about using low-resolution images, where the contour of an object is easily affected by noise. To increase the performance, we use the hexagonal grid in dealing with such low-resolution images. A hexagonal FFT simulation is first presented to pre-process the hexagonal images for recognition. A feature vector matching scheme and a similarity matching scheme are also devised to recognize simpler images with only isolated objects. For complex low-resolution images with occluded objects, the integrated method has to be tailored to go with the hexagonal grid. The low-resolution, hexagonal version of the integrated scheme has also been shown to be suitable and robust.

Contents …………………………………………………………………….. i
List of Figures …………………………………………………………….. v
List of Tables ……………………………………………………..………. ix
Notation ……………………………………………………..……..……… x
Abstract (English) ………………………………………………….…... xiii
Abstract (Chinese) ………………………………………………….……. xv
Chapter 1 Introduction …………………………………………………... 1
1.1 Motives and Goals ………………………………………………………... 1
1.2 Backgrounds and Paper Review ………………………………………... 3
1.3 Thesis Outline …………………………………………………………….. 8
Chapter 2 Preprocessing for Object Recognition ……………….…….. 10
2.1 Introduction ………………………………………………………. ……... 10
2.2 The Improved Method for Connected Component Labeling ………... 12
2.3 The Improved Method for Calculating CSS Images ………………….. 17
Chapter 3 Graph Isomorphic Matching under Affine Transformation ……………………………………………………... 32
3.1 Introduction ……………………………………………………………….. 32
3.2 Affine Transformation and Its Invariants ………………………………. 33
3.3 Discrete Hopfield Neural Network …………………………………….. 36
3.4 Operation of 2-D Genetic Algorithm ………………………………….… 39
3.5 Integration of GA and DHN ……………………………………………… 43
3.5.1 Post GA Modification ……………………………………………. 44
3.5.2 Post DHN Refinement ………………………………………….… 45
3.6 Experimental Results …………………………………………………….. 48
3.6.1. Synthetic Images …………………………………………………… 48
3.6.2. Real World Images ………………………………………………… 51
Chapter 4 Homomorphic Graph Matching …………………….……… 73
4.1 Introduction ……………………………………………………….. …….. 73
4.2 An Integrated Scheme for Homomorphic Graph Matching …. ……… 74
4.3 Recognition of the Articulated Objects …………………………. ……… 77
4.4 Experimental Results …………………………………………….. ……… 80
4.4.1. Examples of Synthesized Images ……………………………….. 80
4.4.2. Examples of Real World Images …………………………. …….. 82
Chapter 5 Mathematical Foundation for Hexagonal Image Processing …………………………………………………… 103
5.1 Introduction ……………………………………………………………… 103
5.2 The Hexagonal Grid …………………………………………….. …….. 104
5.3 The Symmetrical Hexagonal Coordinate Frame ……………….. …….. 105
5.4 The Geometry of 60° Triangles ………………………………………… 107
5.5 A Simulated Fast Hexagonal Fourier Transform …………….. ……… 111
5.5.1 The Comparison of the Rectangular FFT and Traditional Hexagonal FFT …………………………………………………… 111
5.5.2 The Hexagonal FFT via 3-D Rectangular FFT Tools …. …….. 112
5.5.3 A Simplification of Hexagonal FFT via 2-D Rectangular FFT ………………………………………………………………… 113
Chapter 6 Low-resolution Contour Recognition for Hexagonal Grid Images ………………………………………………………. 126
6.1 Introduction ……………………………………………………….. ……… 126
6.2 The CBF on The Hexagonal Frame ……………………………………… 126
6.3 A subpixel Improvement ………………………………………………… 129
6.4 Contour Recognition ……………………………………………………… 131
6.4.1 Feature Vector Matching ……………………………….. ……… 131
6.4.2 Similarity Matching ……………………………………………… 133
6.5 Graph Matching for Low-Resolution Occluded Objects ……. ………….. 134
6.6 Experimental Results ……………………………………………………… 136
6.6.1 Isolated Objects ………………………………………….. ……… 136
6.6.2 Occluded Objects ………………………………………………… 138
Chapter 7 Conclusions …………………………………………..……… 147
7.1 Contributions ………………………………………………………. 147
7.2 Possible Future Research …………………………………………. 148
Bibliography …………………………………………………….. ……… 150

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