On automatic actions retrieval of martial arts

[[abstract]]Martial art actions can be represented via VRML animations or extracted by video tracking. We propose an action retrieval method, which allows users to retrieve similar martial art actions. The mechanism is based on a similarity function that compares animation tracks. A representation of the human skeleton includes head, knee, elbow, wrist, etc and further aggregates important features in martial art actions. Different weights are dynamically calculated according to motion sensitivity of feature points. As a result, the system can automatically retrieve similar martial art actions. The results are tested by a professional kung fu master with good satisfaction.[[conferencetype]]國際[[conferencedate]]20040627~20040630[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

Local Color Voxel and Spatial Pattern for 3D Textured Recognition

3D textured retrieval including shape, color dan pattern is still a challenging research. Some approaches are proposed, but voxel-based approach has not much been made yet, where by using this approach, it still keeps both geometry and texture information. It also maps all 3D models into the same dimension. Based on this fact, a novel voxel pattern based is proposed by considering local pattern on a voxel called local color voxel pattern (LCVP). Voxels textured is observed by considering voxel to its neighbors. LCVP is computed around each voxel to its neighbors. LCVP value will indicate uniq pattern on each 3D models. LCVP also quantizes color on each voxel to generate a specific pattern. Shift and reflection circular also will be done. In an additional way, inspired by promising recent results from image processing, this paper also implement spatial pattern which utilizing Weber, Oriented Gradient to extract global spatial descriptor. Finally, a combination of local spectra and spatial and established global features approach called multi Fourier descriptor are proposed. For optimal retrieval, the rank combination is performed between local and global approaches. Experiments were performed by using dataset SHREC'13 and SHREC'14 and showed that the proposed method could outperform some performances to state-of-the-art

Active multiple kernel learning for interactive 3D object retrieval systems

An effective relevance feedback solution plays a key role in interactive intelligent 3D object retrieval systems. In this work, we investigate the relevance feedback problem for interactive intelligent 3D object retrieval, with the focus on studying effective machine learning algorithms for improving the user's interaction in the retrieval task. One of the key challenges is to learn appropriate kernel similarity measure between 3D objects through the relevance feedback interaction with users. We address this challenge by presenting a novel framework of Active multiple kernel learning (AMKL), which exploits multiple kernel learning techniques for relevance feedback in interactive 3D object retrieval. The proposed framework aims to efficiently identify an optimal combination of multiple kernels by asking the users to label the most informative 3D images. We evaluate the proposed techniques on a dataset of over 10,000 3D models collected from the World Wide Web. Our experimental results show that the proposed AMKL technique is significantly more effective for 3D object retrieval than the regular relevance feedback techniques widely used in interactive content-based image retrieval, and thus is promising for enhancing user's interaction in such interactive intelligent retrieval systems. </jats:p

Automated separation of bone joint structures for medical image reconstruction

Automated separation of reconstructed bone joints from 3D medical images is a challenging task due to surrounding soft tissue and adjacent bones that can affect the clarity of bone boundaries. Existing approaches typically require human intervention to correct improper results of segmentation before the joint model is reconstructed. This dissertation presents a new methodology for separating bone joint models using a completely automated approach. Rather than trying to offer a solution for segmenting medical images, the proposed method first allows errors in the reconstructed model and later removes these errors without the help of a medical expert or technician. This method utilizes known anatomical information from a generic CAD model, which is a properly generated model of the anatomy of a similar human subject, with regard to age, gender, height, etc. The intent is to aid in the separation of bones in the joint areas by comparing the reconstructed model that might contain errors to the generic model which has individual bones separated properly. The human hip joint is employed as an example of algorithm implementation in this dissertation. The proposed method is a general approach that should be adequately flexible to extend to other type of joints such as knee and elbow

3D shape matching and registration : a probabilistic perspective

Dense correspondence is a key area in computer vision and medical image analysis. It has applications in registration and shape analysis. In this thesis, we develop a technique to recover dense correspondences between the surfaces of neuroanatomical objects over heterogeneous populations of individuals. We recover dense correspondences based on 3D shape matching. In this thesis, the 3D shape matching problem is formulated under the framework of Markov Random Fields (MRFs). We represent the surfaces of neuroanatomical objects as genus zero voxel-based meshes. The surface meshes are projected into a Markov random field space. The projection carries both geometric and topological information in terms of Gaussian curvature and mesh neighbourhood from the original space to the random field space. Gaussian curvature is projected to the nodes of the MRF, and the mesh neighbourhood structure is projected to the edges. 3D shape matching between two surface meshes is then performed by solving an energy function minimisation problem formulated with MRFs. The outcome of the 3D shape matching is dense point-to-point correspondences. However, the minimisation of the energy function is NP hard. In this thesis, we use belief propagation to perform the probabilistic inference for 3D shape matching. A sparse update loopy belief propagation algorithm adapted to the 3D shape matching is proposed to obtain an approximate global solution for the 3D shape matching problem. The sparse update loopy belief propagation algorithm demonstrates significant efficiency gain compared to standard belief propagation. The computational complexity and convergence property analysis for the sparse update loopy belief propagation algorithm are also conducted in the thesis. We also investigate randomised algorithms to minimise the energy function. In order to enhance the shape matching rate and increase the inlier support set, we propose a novel clamping technique. The clamping technique is realized by combining the loopy belief propagation message updating rule with the feedback from 3D rigid body registration. By using this clamping technique, the correct shape matching rate is increased significantly. Finally, we investigate 3D shape registration techniques based on the 3D shape matching result. Based on the point-to-point dense correspondences obtained from the 3D shape matching, a three-point based transformation estimation technique is combined with the RANdom SAmple Consensus (RANSAC) algorithm to obtain the inlier support set. The global registration approach is purely dependent on point-wise correspondences between two meshed surfaces. It has the advantage that the need for orientation initialisation is eliminated and that all shapes of spherical topology. The comparison of our MRF based 3D registration approach with a state-of-the-art registration algorithm, the first order ellipsoid template, is conducted in the experiments. These show dense correspondence for pairs of hippocampi from two different data sets, each of around 20 60+ year old healthy individuals

Similarity Assessment and Retrieval of CAD Models

Ph.DDOCTOR OF PHILOSOPH

Indexing and retrieval of 3D models aided by active learning

Proceedings of the ACM International Multimedia Conference and ExhibitionIV615-61