Recognition of Planar Segments in Point Cloud Based on Wavelet Transform

Within industrial automation systems, three-dimensional (3-D) vision provides very useful feedback information in autonomous operation of various manufacturing equipment (e.g., industrial robots, material handling devices, assembly systems, and machine tools). The hardware performance in contemporary 3-D scanning devices is suitable for online utilization. However, the bottleneck is the lack of real-time algorithms for recognition of geometric primitives (e.g., planes and natural quadrics) from a scanned point cloud. One of the most important and the most frequent geometric primitive in various engineering tasks is plane. In this paper, we propose a new fast one-pass algorithm for recognition (segmentation and fitting) of planar segments from a point cloud. To effectively segment planar regions, we exploit the orthonormality of certain wavelets to polynomial function, as well as their sensitivity to abrupt changes. After segmentation of planar regions, we estimate the parameters of corresponding planes using standard fitting procedures. For point cloud structuring, a z-buffer algorithm with mesh triangles representation in barycentric coordinates is employed. The proposed recognition method is tested and experimentally validated in several real-world case studies

Statistical Shape Spaces for 3D Data: A Review

International audienceMethods and systems for capturing 3D geometry are becoming increasingly commonplace–and with them a plethora of 3D data. Much of this data is unfortunately corrupted by noise, missing data, occlusions or other outliers. However, when we are interested in the shape of a particular class of objects, such as human faces or bodies, we can use machine learning techniques, applied to clean, registered databases of these shapes, to make sense of raw 3D point clouds or other data. This has applications ranging from virtual change rooms to motion and gait analysis to surgical planning depending on the type of shape. In this chapter, we give an overview of these techniques, a brief review of the literature, and comparative evaluation of two such shape spaces for human faces

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Statistical shape analysis of neuroanatomical structures based on spherical wavelet transformation

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008.Includes bibliographical references.Evidence suggests that morphological changes of neuroanatomical structures may reflect abnormalities in neurodevelopment, or relate to a variety of disorders, such as schizophrenia and Alzheimer's disease (AD). Advances in high-resolution Magnetic Resonance Imaging (MRI) techniques allow us to study these alterations of brain structures in vivo. Previous work in studying the shape variations of brain structures has provided additional localized information compared with traditional volume-based study. However, challenges remain in finding an accurate shape presentation and conducting shape analysis with sound statistical principles. In this work, we develop methods for automatically extracting localized and multi-scale shape features and conducting statistical shape analysis of neuroanatomical structures obtained from MR images. We first develop a procedure to extract multi-scale shape features of brain structures using biorthogonal spherical wavelets. Using this wavelet-based shape representation, we build multi-scale shape models and study the localized cortical folding variations in a normal population using Principal Component Analysis (PCA). We then build a shape-based classification framework for detecting pathological changes of cortical surfaces using advanced classification methods, such as predictive Automatic Relevance Determination (pred-ARD), and demonstrate promising results in patient/control group comparison studies. Thirdly, we develop a nonlinear temporal model for studying the temporal order and regional difference of cortical folding development based on this shape representation. Furthermore, we develop a shape-guided segmentation method to improve the segmentation of sub-cortical structures, such as hippocampus, by using shape constraints obtained in the wavelet domain.(cont.) Finally, we improve upon the proposed wavelet-based shape representation by adopting a newly developed over-complete spherical wavelet transformation and demonstrate its utility in improving the accuracy and stability of shape representations. By using these shape representations and statistical analysis methods, we have demonstrated promising results in localizing shape changes of neuroanatomical structures related to aging, neurological diseases, and neurodevelopment at multiple spatial scales. Identification of these shape changes could potentially lead to more accurate diagnoses and improved understanding of neurodevelopment and neurological diseases.by Peng Yu.Ph.D

Medical image analysis using statistical shape model based on subdivision surface wavelet

Ph.DDOCTOR OF PHILOSOPH

A New Multiscale Representation for Shapes and Its Application to Blood Vessel Recovery

In this paper, we will first introduce a novel multiscale representation (MSR) for shapes. Based on the MSR, we will then design a surface inpainting algorithm to recover 3D geometry of blood vessels. Because of the nature of irregular morphology in vessels and organs, both phantom and real inpainting scenarios were tested using our new algorithm. Successful vessel recoveries are demonstrated with numerical estimation of the degree of arteriosclerosis and vessel occlusion.Comment: 12 pages, 3 figure