Skeleton-based Template Retrieval for Virtual Maize Modeling

Journal of Computational Information Systems6:5(2010) 1577-1582 Available at http://www.jofcis.com Skeleton-based Template Retrieval for Virtual Maize Modeling Boxiang XIAO 1,2, Chunjiang ZHAO 1,2,, Xinyu GUO 1,2, Shenglian LU 1,2, Weiliang WEN 1,2 1 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 2 Open Key Lab of Information Technology in Agriculture, Ministry of Agriculture,Beijing 100097, China Abstract Template-based modeling is an effective approach for complex plants structures. A skeleton-based template retrieval method is presented for virtual maize modeling. The retrieval is performed based on a template database composed of many template models by three steps: affine transformation, curve discretization and curve matching. The main process of skeleton-based template retrieval method as well as assistant processing such as template generation is introduced in detail. To examine the algorithm, some experimental results are given and illustrated. The experiments show that the algorithm retrieves similar templates in database exactly and it is an effective and robust approach for template retrieval. Finally, main advantages and limitations are discussed. Keywords: Skeleton-based; Template-based; Retrieval; Virtual Plant. 1. Introduction In the past few years, Virtual Reality (VR) has promoted developments in many areas such as industry, education, as well as agriculture. As an important research issue, virtual plant attracted more increasing interest of researchers in many intersectant areas. Related work. Many significant works in virtual plant are carried out in past decades. For instance, Lima and colleagues [1] provided a forest landscape visualization procedure capable of walk-through simulations and its application. They developed a forest landscape visualization system using virtual reality modeling language (VRML), and the visualization system works with data of forest stands rather than individual trees. Deussen [2] developed a framework for geometry generation and rendering of plants with applications in landscape architecture. Methods for creating virtual vegetation using techniques of computer graphics were presented. Birch and partner [3] researched the concepts and applications for modeling the kinetics of plant canopy architecture, and consequently studied the development and parameterization of individual organs in terms of environmental variables and plant characteristics. Pommel [4] used the virtual 3D maize canopies to assess the effect of plot heterogeneity on radiation interception. Although the research is fruitful in virtual plant, however, virtual modeling of complex plants structures which hard to parameterlized is still a heavy challenge both for computer graphics and plant biology. Common methods can not deal with the modeling of complex structures hard to parameterlized, thus the template-based approach is involved for this task. The template-based method is widely used in many works [5,6]. For instance, Chu and Lin [7] implemented an example-based deformation transfer for 3D polygon models. The template-based approach brings the problems of template management and retrieval, and the 3D model retrieval is also a hard task for researchers. Funkouser and colleagues [8] developed a search engine Corresponding author. Email addresses: xiaobx@nercita.org.cn (Boxiang XIAO), zhaocj@nercita.org.cn (Chunjiang ZHAO). 1553-9105/ Copyright 2010 Binary Information Press May, 2010

1578 B. Xiao et al. /Journal of Computational Information Systems 6:5(2010) 1577-1582 for 3D models. Chu and Lin [9] implemented an example-based deformation transfer for 3D polygon models. Pan et al. [8] proposed a 3D mesh model retrieval method based on block. Wu and He [10] presented a method of signal boosting for robust data fusion in speech retrieval. Samatsu et al.[11] developed an approach of visualization for fuzzy retrieval using self-organizing maps. To implement the template model retrieval, we present a skeleton-based retrieval approach, and introduce the method in detail by example maize leaf as follow. 2. Overview The aim of this work is to implement a skeleton-based retrieval approach for template models of maize leaves. The main retrieval algorithm includes three key steps: affine transformation, curve discretization and curve matching. The retrieval is executed based on a template database composed of many template models, and the models are generated by remeshed parameter surfaces from scanner data. For retrieval, an object curve is generated by user as a retrieval instance, and then one or several similar templates in database are recalled for subsequent template-based modeling. Fig 1 shows the main flowchart of this work, and the template-based modeling and retrieval process will be introduced in detail as follow. Fig.1 Flowchart of Skeleton-based Template Retrieval. 3. Template-based Modeling and Template Generation 3.1. Template-based Modeling In past decades, template-based approach is widely used in mechanical design, pattern recognition, biological modeling and morphological modeling. Because of the complexity of plant structure, common methods can not deal with the modeling of complex structures hard to parameterlized, and the modeling based on some templates which generated according to corresponding complex structures of plants can achieve this modeling. Such as maize leaf, the drape is hard to expressed by parameters, and a template-based method is implemented specially for maize leaf modeling. A series of leaf models is constructed by three-dimensional data which collected by digitizer or laser scanner. Based on the templates, a new virtual maize leaf model is reconstructed by control of relative morphological feature parameters according agricultural knowledge. The template-based modeling of new leaf is implemented by a series of affine transformations including translating, rotating and scaling, as shown in Fig.2, where (a) are template leaf model and object midrib curve, (b) is the new leaf model result by affine transformation F. The transformation of a whole leaf is decomposed into all control points.

B. Xiao et al. /Journal of Computational Information Systems 6:5(2010) 1577-1582 1579 3.2. Template Generation Fig.2 Template-based Modeling for Maize Leaf. Template-based modeling depends on many template models, and the template generation is an important steps for database construction. In ours work, the templates are generated by scattered scanner data which collected by 3D laser scanner FastScan. The generation of templates includes three stepes: scan, mesh simplification and parameterization. Firstly, original scattered data are collected by scanner, as shown in Fig.3 (a). Generally, the original data contains about 100,000-400,000 points, thus the preprocessing and mesh simplification are necessary. The mesh simplification reduce the number of points in model to about 100-200, as shown in (b). Finally, a new parametric surface is fitted according to simplified mesh model. Here, a NURBS(Non-Uniform Rational B-Spline) surface described by Eq.(1) is used to parameterize the template model, (c) and (d) respectively show the control points and the interpolated surface. Eq.(1) is the NURBS expression, where p is the interpolated point on surface, B are the B-Spline basis function, V are control points of surface and W are corresponding weight factors. Puw (, ) = n m B ( u) B ( w) W V i, k j, l i, j i, j i= 0 j= 0 n m Bik, ( u) Bjl, ( w) Wi, j i= 0 j= 0 (1) 4. Skeleton-based Template Retrieval Fig.3 Template Models of Maize Leaves. For retrieval, an object curve is specified by user as a retrieval instance. The proposed skeleton-based template retrieval is implemented by comparison between main feature curve of template models and object curve. For maize leaf, the main feature curve is the middle leaf vein which is also the middle curve of the NURBS surface. The similarity is evaluated by offset values defined as Offset these two curves: object curve, defined as Co, and template middle curve, defined as Ct. The retrieval algorithm includes three key steps: affine transformation, curve discretization and curve matching. The steps of process will be introduced in detail as follow.

1580 B. Xiao et al. /Journal of Computational Information Systems 6:5(2010) 1577-1582 4.1. Affine Transformation Generally, the template middle curve does not have a same position to the specified object curve, and it is hard to evaluate the offset value of two curves. In order to make a direct comparison, we transform both the template middle curve Ct and the object curve Co into XOZ plane with end of curve on the OX axis. For a template middle curve, it may not be planar, so we transform them by a triangle as a reference, which constructed by two end points and a farmost point on curve to the line linked by two end points. The transformation is implemented by a series of affine transformations including translating, rotating and scaling, and the results of transform Fig.4 shows a couple of object curve and template middle curve, and they are transformed to a same position for direct comparison and offset evaluation. 4.2. Curve Discretization In order to compare two curves, we discretize two curves firstly and then calculate the offset value of discretized curves. For template middle curve, we use the NURBS curve to discretize by pace Pt=0.02, and the discretized curve includes 50 points on the original curve. For object curve, it is specified by user using a Bezier curve described in Eq.(2) to discretize by pace Po=0.02, with 50 points also. Eq.(2) is a typical Bezier expression where P is interpolated points, B are Bernstein basis functions and V are control points. Fig.4 (b) illustrates the discretization process and a symbolistic result. Fig.4 Skeleton-based Template Matching.. n in, i (2) i= 0 Pt () = B () t V t [0,1] 4.3. Curve Matching Based on the discretized curve, we evaluate the difference between two curves. We define an evaluating value Offset as a target value, and it can be calculated as an average distance by following steps: Step 1. The discretized points on template middle curve are noted as Pi, i [1,50], and the discretized points on object curve are noted as Qj, j [1,50]; Step 2. For a point P on template middle curve, the minimum Euclidian distance from P to object curve is calculated, and it is just the distance from P to nearest discretized points Q on object curve, as shown in Fig.4 (b); 2 2 2 dmin( P) = dis( P, Q) = ( Px Qx) + ( Py Qy) + ( Pz Qz) (3) Step 3. The value of Offset is calculated by average of all minimum Euclidian distance of Pi, described in Eq.(4) Offset = avg( d min( Pi )) i [1,50] (4) The Offset value is adopted to evaluate the difference between two curves. The smaller Offset, the larger similarity two curves have. As a result, we adopt the template with a minimum Offset which belongs in a confident threshold as the retrieved template model.

B. Xiao et al. /Journal of Computational Information Systems 6:5(2010) 1577-1582 1581 5. Results To prove and examine the approach, we have developed an experimental module for retrieval by use of C++ program language and OpenGL Graphic Library and fixed it on a maize modeling system. We have construct an example database including 10 maize leaf models, and perform the retrieval process with 10 different object curves. Part results are displayed as follow. Three retrieval experiments are given in Fig 5, and every retrieval experiment respectively includes two results with values of curve offset. Shown in Fig 5, elements in the left row are retrieval object curves, leaves in the middle row are most similar models to corresponding object curves, and right row secondly. The experiments show that the proposed method can retrieve the similar template models exactly in model database. At the same time, the robustness and efficiency are also examined in experiments. Fig.4 Part of Retrieval Results of 3 Object Curves with Offsets. 6. Conclusions and Future Work To sum up, a skeleton-based retrieval approach for template models is presented in this paper. A maize modeling system is developed by C++ program language based on OpenGL graphic library, and template retrieval module is integrated. For a typical retrieval, an object curve is specified by user as a retrieval instance, and the similar models in database are recalled after curve matching. Finally, part experimental results are displayed including retrieval instances and corresponding results with values of curve offset. The main contribution of this work is that the algorithm as well as system provides a convenient way for rapid three-dimensional template data retrieval in s large database and promote the template-based modeling for virtual maize. Furthermore, the retrieval approach also provides a basic tool to manage and control large three-dimensional models database for virtual plants. The main limitation of this system is that the algorithm and system are only implemented and applied for virtual maize leaf. In future, we focus on the study of applicability extension of retrieval algorithm, promotion of feature extraction algorithm and development of corresponding retrieval systems for other plants. Acknowledgement This work is supported by National Natural Science Foundation of China (Grant No. 30700493); by National Science and Technology Support Program (Grant No. 2009BAK43B18); by National Agricultural Science and Technology Achievements Transformation Fund Programs (Grant No. 2009GB2A000001) ; by Beijing Municipal Natural Science Foundation (Grant No. 4081001). References

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