Surface reconstruction means that retrieve the data by scanning an object using a device such as laser scanner and construct it using the computer to gain back the soft copy of data on that particular object It is a reverse process and is very useful especially when that particular object original data is missing without doing any backup Hence, by doing so, the data can be recollected and can be stored for future purposes The type of data can be in the form of structure or unstructured points The accuracy of the reconstructed result should be concerned because if the result is incorrect, hence it will not exactly same like the original shape of the object Therefore, suitable methods should be chosen based on the data used Soft computing methods also have been used in the reconstruction field This papers highlights the previous researches and methods that has been used in the surface reconstruction field

Surface reconstruction techniques: a review

Simulators play an important role in training surgery residents to perform laparoscopy surgery. Some of these simulators have the capability to track tool motion to assess performance. However, most have not utilized the data to analyze trainee performance in a meaningful way. The alpha shape method can be used to construct a geometric surface based on motion data to enable visualization of the performance, while the surface derivative (surface/time to completion)—efficiency—can be used as a metric to evaluate complex surgical performance. The utility of the alpha shape method was demonstrated in a pick-and-place task, where the motion path of laparoscopic graspers was recorded by a position sensor, miniBIRD 500®. An alpha shape method was used to measure the surface area of the 3D points in space occupied by the tool tips during task performance. Results show that the surface derivative measure alone may be able to model the speed-accuracy tradeoff function, thereby simplifying the analysis and evaluation of complex motion in surgical performance.

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Application of the Alpha Shape Method to Visualize and Analyze Surgical Motion

La chirurgie robotisee a ete developpee pour pallier les difficultes de la chirurgie laparoscopique. Le robot da Vinci (Intuitive Surgical) est largement repandu dans les hopitaux nord-americains et europeens pour la chirurgie abdominale. Base sur de la teleoperation, il ameliore la dexterite et la precision des operations en chirurgie mini-invasive. Cependant, des recommandations incompletes et des defauts ergonomiques du systeme, pour positionner les bras du robot a la surface l’abdomen du patient avant l’operation, creent un des principaux problemes de la chirurgie robotisee : des organes inatteignables ou des collisions entre les instruments pendant l’operation. L’objectif de ces travaux de recherche est de proposer une nouvelle methode de placement des bras du robot basee sur une assistance operatoire numerique utilisant l’analyse d’images medicales du patient et la modelisation 3D de la surface de son abdomen afin de calculer des positions des bras optimales en fonction des caracteristiques du robot, de la position des organes et du type de chirurgie.

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3D visualization and interactive image manipulation for surgical planning in robot-assisted surgery

Surface reconstruction is the key technology in the geometry reverse engineering. In order to obtain the object's geometrical model, we have to construct surface by large numbers of measured data points. This paper introduces a new method for the reconstruction of free-form surface. Firstly, it profits scattered and measured data points which come from free-form surface archetype by radius basis function neural networks algorithm. Secondly, it maps the mathematical model of free-form surface by the linear combination of radius basis function and the weights of the hidden layer. Finally it transforms the mathematical model to bicubic B-spline surface. This paper also commentates the feasibility of the above idea that resolves the problems of surface fitting by radius basis function neural networks

Research on the reconstruction method of B-spline surface based on radius basis function neural networks

We present the bibeta spline curved surface which is controlled by double parameters including the algorithm principles, the transform of projection, the treatment of elimination hidden line, the automatic adjustment on the screen, the principle of display and its realization. The freedom surface modeling system (FSMS) in language C is developed, and some examples are given.

An intelligent system of on space surface modeling

This paper discusses how Beta spline curve with geometric continuity is influenced by the change of parameters beta1 and beta2, offers the range of parameters beta1 and beta2 and presents the algorithm principles of bibeta spline curved surface that is controlled by double parameters. Based on all above, a freedom surface modeling system (FSMS) is designed and some examples are verified and analyzed

The influence parameters /spl beta//sub 1 / and /spl beta//sub 2 / have on bibeta spline curved surface

In this paper, the algorithm of building a decision tree is introduced by comparing the information gain or entropy. The produced process of univariate decision tree is given as an example. According to rough sets theory, the method of constructing multivariate decision tree is discussed. Using the algorithm, the complexity of decision tree is decreased, the construction of decision tree is optimized and the rule of data mining could be built.

Xu-min Liu

Papers

Research on the reconstruction method of B-spline surface based on radius basis function neural networks

An intelligent system of on space surface modeling

The influence parameters /spl beta//sub 1 / and /spl beta//sub 2 / have on bibeta spline curved surface

A contribution to decision tree construction based on rough set theory