Automatic Reconstruction of 3D Human Face from CT and Color Photographs (original) (raw)
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3D-3D Registration: Surface Rendering Plus Skull and Soft Tissue Registration
2006 1ST IEEE Conference on Industrial Electronics and Applications, 2006
The CT-skull and 3D-soft tissue from digital surface scan has been registered together in order to see the bones inside the facial surface. This can be done using 3 main methods. Firstly, by applying the segmentation to the CT data, which is the DICOM images, for the skull and skin parts. Secondly, by developing the 3D surface rendering based on Marching cube technique to form the 3D objects of skull and skin respectively. Next, by applying the semi-automatic registration technique to register CT-skin with the 3D-soft tissue from digital surface scanner. The last method requires the knowledge on how to let the user interact with the object and get the depth in 3D. Also the knowledge on the 3D rotation is needed in order to match the directions between two objects. Then the result can be shown the bone under the facial soft tissue, which could be applied to other applications of medical treatment simulation. How they might aid in the simulation after the surgical treatment or moving bone.
Registration of 3-Dimensional Facial Photographs for Clinical Use
2010
Purpose: To objectively evaluate treatment outcomes in oral and maxillofacial surgery, pre-and post-treatment 3-dimensional (3D) photographs of the patient's face can be registered. For clinical use, it is of great importance that this registration process is accurate (Ͻ 1 mm). The purpose of this study was to determine the accuracy of different registration procedures. Materials and Methods: Fifteen volunteers (7 males, 8 females; mean age, 23.6 years; range, 21 to 26 years) were invited to participate in this study. Three-dimensional photographs were captured at 3 different times: baseline (T 0 ), after 1 minute (T 1 ), and 3 weeks later (T 2 ). Furthermore, a 3D photograph of the volunteer laughing (T L ) was acquired to investigate the effect of facial expression. Two different registration methods were used to register the photographs acquired at all different times: surface-based registration and reference-based registration. Within the surface-based registration, 2 different software packages (Maxilim [Medicim NV, Mechelen, Belgium] and 3dMD Patient [3dMD LLC, Atlanta, GA]) were used to register the 3D photographs acquired at the various times. The surface-based registration process was repeated with the preprocessed photographs. Reference-based registration (Maxilim) was performed twice by 2 observers investigating the inter-and intraobserver error. Results: The mean registration errors are small for the 3D photographs at rest (0.39 mm for T 0 -T 1 and 0.52 mm for T 0 -T 2 ). The mean registration error increased to 1.2 mm for the registration between the 3D photographs acquired at T 0 and T L . The mean registration error for the reference-based method was 1.0 mm for T 0 -T 1 , 1.1 mm for T 0 -T 2 , and 1.5 mm for T 0 and T L . The mean registration errors for the preprocessed photographs were even smaller (0.30 mm for T 0 -T 1 , 0.42 mm for T 0 -T 2 , and 1.2 mm for T 0 and T L ). Furthermore, a strong correlation between the results of both software packages used for surface-based registration was found. The intra-and interobserver error for the reference-based registration method was found to be 1.2 and 1.0 mm, respectively. Conclusion: Surface-based registration is an accurate method to compare 3D photographs of the same individual at different times. When performing the registration procedure with the preprocessed photographs, the registration error decreases. No significant difference could be found between both software packages that were used to perform surface-based registration.
IMAGE BASED 3D FACE RECONSTRUCTION: A SURVEY
International Journal of Image and Graphics, 2009
The use of 3D data in face image processing applications has received considerable attention during the last few years. A major issue for the implementation of 3D face processing systems is the accurate and real time acquisition of 3D faces using low cost equipment. In this paper we provide a survey of 3D reconstruction methods used for generating the 3D appearance of a face using either a single or multiple 2D images captured with ordinary equipment such as digital cameras and camcorders. In this context we discuss various issues pertaining to the general problem of 3D face reconstruction such as the existence of suitable 3D face databases, correspondence of 3D faces, feature detection, deformable 3D models and typical assumptions used during the reconstruction process. Different approaches to the problem of 3D reconstruction are presented and for each category the most important advantages and disadvantages are outlined. In particular we describe example-based methods, stereo methods, video-based methods and silhouette-based methods. The issue of performance evaluation of 3D face reconstruction algorithms, the state of the art and future trends are also discussed.
2000
A scheme for texture mapping a 3D individualized color photo-realistic facial model from real color portraits and CT data is described. First, 3D CT images including both soft and hard tissues should be reconstructed from sequential CT slices, using a surface rendering technique. Facial features are extracted from 3D soft tissue. A generic mesh is individualized by correspondence matching and interpolation from those feature vertices. Three digitized color portraits with the ''third'' dimension from reconstructed soft tissue are blended and texture-mapped onto the 3D head model (mesh). A color
A novel approach for registration of 3D face images
2012
in case of 2.5D as well as 3D meshes. No normalization process is applied and the process correctly localizes nose tip across any pose (including rotation in any direction in 3D space namely about x-axis, y-axis and z-axis).The present technique works by taking a facial image as input, and after which a thresholding process is applied to remove irrevelant details and finally the nose tip is detected using a maximum intensity technique as illustrated in Section III.c.1. Three dimensional face registration is a critical step in 3Dface recognition. In fact 3D faces still require being pose normalized and correctly registered for further face analysis.The 3D data may have different translation, rotation or scaling due to the controlled environment parameters such as the acquisition setup, device properties or due to uncontrolled conditions parameters such as the pose variations of the acquired subjects. In either case, the 3D shapes need to be aligned to each other and should be brought into a common coordinate frame before a comparison can be made. Registration is the alignment procedure of two similar shapes.Normally there is an importance of locating facial features e.g. lips , nose-tip which is required for face registration depending upon which alignment and consecutively registration has to be performed. The task of face registration is an issue due to the inherent elasticity present in human skin and the range of motion available to the human jaw. The aim of this paper is locating the facial points i.e. the nose tip .The present technique works by taking a facial image as input and after which a thresholding process is applied to remove irrelevant details and finally the nose tip is detected using a maximum intensity technique as is described in Section III.c.1 below. Experimented on nearly about 472 faces consisting of different poses (including rotation about x-axis, y-axis and z-axis) selected from the FRAV3D (Face Recognition and Artificial Vision Database), the present technique recognizes nose tip in 468 of the cases thus displaying a 99.15% of good nose tip localization.
A Study on Computerized Three-Dimensional Facial Reconstruction
International Association of Scientists and Researchers, 2018
Facial reconstruction is the method used for the purpose of identification of unknown human remains. The facial techniques are developed day by day and hence with the manual method of facial reconstruction, there introduce computerized three-dimensional facial reconstruction method for the purpose of identified the unknown skeletal remain. These method are used in the cases related with mass disaster, accidental cases, archeological research of skeletal remains etc.This technique comprises of both the scientific as well as artistic method skill. In the present study, there discuss about the advancement of 3D technology because of its cost effective, efficient and moreover fast services. The paper followed on the basis of the role of computer modeling skills in the anthropological search of the reconstruction of the face. There is also a need to be the high rate of improvement and validation in the working of computerized 3D technique of facial reconstruction so as to make the identification more efficient and quick and less labor intensive.
Three dimensional digitization of the face and skull
Journal of maxillofacial surgery, 1985
The possibility of using computer-aided design as a tool for the planning and simulation of facial reconstruction surgery is discussed and has been shown to be feasible. Methods of acquiring the essential measurements on the facial surface and the underlying bone structure in a computer-compatible form are described, including a new approach which has been implemented using a system of fanned laser beams and a television camera for data acquisition. A mathematical analysis of the properties of this kind of imaging system is given. Finally the requirements of a complete aid to surgery system based on this approach are outlined and plans for the implementation of such a system are described.
International Journal of Oral and Maxillofacial Surgery, 2013
The aim of this study was to evaluate the impact of simultaneous capture of the threedimensional (3D) surface of the face and cone beam computed tomography (CBCT) scan of the skull on the accuracy of their registration and superimposition. 3D facial images were acquired in 14 patients using the Di3d (Dimensional Imaging, UK) imaging system and i-CAT CBCT scanner. One stereophotogrammetry image was captured at the same time as the CBCT and another one hour later. The two stereophotographs were then individually superimposed over the CBCT using VRmesh. Seven patches were isolated on the final merged surfaces. For the whole face and each individual patch; maximum and minimum range of deviation between surfaces, absolute average distance between surfaces, and standard deviation for the 90 th percentile of the distance errors were calculated. The superimposition errors of the whole face for both captures revealed statistically significant differences (P=0.00081). The absolute average distances in both separate and simultaneous captures were 0.47mm and 0.27mm, respectively. The level of superimposition accuracy in patches from separate captures ranged between 0.3 and 0.9mm, while that of simultaneous captures was 0.4mm. Simultaneous capture of Di3d and CBCT images significantly improved the accuracy of superimposition of these image modalities.
AUTOMATIC REGISTRATION AND MERGING OF 3D SURFACE SCANS OF HUMAN HEAD
itib.edu.pl
The article presents a method of registration of 3D surface scans obtained from different viewpoints and a method to merge them properly into one 3D photograph of patient head for orthodontic diagnosis. Our task concentrates on obtaining automatically the precise and repeatable examination results. So far methods of registration and merging range images into a 3D photograph was performed semi-automatically by a qualified person. The presented method is automatic and based on the analysis of redundant and uncertain data.
3D Face Modeling: Comprehensive Description
Journal of Global Research in Computer Sciences, 2011
To provide a comprehensive survey, we not only categorize existing modeling techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as biometric modalities, system evaluation, and issues of illumination and pose variation are covered. 3D models hold more information of the face, like surface information, that can be used for face recognition or subject discrimination. This paper, gives the survey based techniques or methods for 3D face modeling, in this paper first step namely Model Based Face Reconstruction, secondly Methods of 3d Face models divided into three parts Holistic matching methods, Feature-based (structural) matching methods, Hybrid methods thirdly Other methods categorized into again three parts 2D based class, 3D Based class and 2D+3D based class are discussed. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing l...