Accuracy of mandibular repositioning surgery using new technology: Computer-aided design and manufacturing customized surgical cutting guides and fixation plates.

INTRODUCTION: Recent 3-dimensional technology advancements have resulted in new techniques to improve the accuracy of intraoperative transfer. This study aimed to validate the accuracy of computer-aided design and manufacturing (CAD-CAM) customized surgical cutting guides and fixation plates on mandibular repositioning surgery performed in isolation or combined with simultaneous maxillary repositioning surgery. METHODS: Sixty patients who underwent mandibular advancement surgery by the same surgeon were retrospectively evaluated by 3-dimensional surface-based superimposition. A 3-point coordinate system (x, y, z) was used to identify the linear and angular discrepancies between the planned movements and actual outcomes. Wilcoxon rank sum test was used to compare the outcomes between the mandible-only and the bimaxillary surgery groups with significance at P <0.05. Pearson correlation coefficient compared planned mandible advancement to the outcome from advancement planned. The centroid, which represents the mandible as a single unit, was computed from 3 landmarks, and the discrepancies were evaluated by the root mean square error (RMSE) for clinical significance set at 2 mm for linear discrepancies and 4° for angular discrepancies. RESULTS: There was no statistically significant difference between the planned and actual position of the mandible in either group when considering absolute values of the differences. When considering raw directional data, a statistically significant difference was identified in the y-axis suggesting a tendency for under-advancement of the mandible in the bimaxillary group. The largest translational RMSE for the centroid was 0.77 mm in the sagittal dimension for the bimaxillary surgery group. The largest rotational RMSE for the centroid was 1.25° in the transverse dimension for the bimaxillary surgery group. Our results show that the precision and clinical feasibility of CAD-CAM customized surgical cutting guides and fixation plates on mandibular repositioning surgery is well within clinically acceptable parameters. CONCLUSION: Mandibular repositioning surgery can be performed predictably and accurately with the aid of CAD-CAM customized surgical cutting guides and fixation plates with or without maxillary surgery.

Face recognition using sparse approximated nearest points between image sets.

We propose an efficient and robust solution for image set classification. A joint representation of an image set is proposed which includes the image samples of the set and their affine hull model. The model accounts for unseen appearances in the form of affine combinations of sample images. To calculate the between-set distance, we introduce the Sparse Approximated Nearest Point (SANP). SANPs are the nearest points of two image sets such that each point can be sparsely approximated by the image samples of its respective set. This novel sparse formulation enforces sparsity on the sample coefficients and jointly optimizes the nearest points as well as their sparse approximations. Unlike standard sparse coding, the data to be sparsely approximated are not fixed. A convex formulation is proposed to find the optimal SANPs between two sets and the accelerated proximal gradient method is adapted to efficiently solve this optimization. We also derive the kernel extension of the SANP and propose an algorithm for dynamically tuning the RBF kernel parameter while matching each pair of image sets. Comprehensive experiments on the UCSD/Honda, CMU MoBo, and YouTube Celebrities face datasets show that our method consistently outperforms the state of the art.

An efficient multimodal 2D-3D hybrid approach to automatic face recognition.

We present a fully automatic face recognition algorithm and demonstrate its performance on the FRGC v2.0 data. Our algorithm is multimodal (2D and 3D) and performs hybrid (feature-based and holistic) matching in order to achieve efficiency and robustness to facial expressions. The pose of a 3D face along with its texture is automatically corrected using a novel approach based on a single automatically detected point and the Hotelling transform. A novel 3D Spherical Face Representation (SFR) is used in conjunction with the SIFT descriptor to form a rejection classifier which quickly eliminates a large number of candidate faces at an early stage for efficient recognition in case of large galleries. The remaining faces are then verified using a novel region-based matching approach which is robust to facial expressions. This approach automatically segments the eyes-forehead and the nose regions, which are relatively less sensitive to expressions, and matches them separately using a modified ICP algorithm. The results of all the matching engines are fused at the metric level to achieve higher accuracy. We use the FRGC benchmark to compare our results to other algorithms which used the same database. Our multimodal hybrid algorithm performed better than others by achieving 99.74% and 98.31% verification rates at 0.001 FAR and identification rates of 99.02% and 95.37% for probes with neutral and non-neutral expression respectively.

Three-dimensional model-based object recognition and segmentation in cluttered scenes.

Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency.