ScholarBank@NUShttps://scholarbank.nus.edu.sgThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sat, 21 May 2022 09:42:18 GMT2022-05-21T09:42:18Z5071- Video repeat recognition and mining by visual featureshttps://scholarbank.nus.edu.sg/handle/10635/67347Title: Video repeat recognition and mining by visual features
Authors: Yang, X.; Tian, Q.
Abstract: Repeat video clips such as program logos and commercials are widely used in video productions, and mining them is important for video content analysis and retrieval. In this chapter we present methods to identify known and unknown video repeats respectively. For known video repeat recognition, we focus on robust feature extraction and classifier learning problems. A clustering model of visual features (e.g. color, texture) is proposed to represent video clip and subspace discriminative analysis is adopted to improve classification accuracy, which results in good results for short video clip recognition. We also propose a novel method to explore statistics of video database to estimate nearest neighbor classification error rate and learn the optimal classification threshold. For unknown video repeat mining, we address robust detection, searching efficiency and learning issues. Two detectors in a cascade structure are employed to efficiently detect unknown video repeats of arbitrary length, and this approach combines video segmentation, color fingerprinting, self-similarity analysis and Locality-Sensitive Hashing (LSH) indexing. A reinforcement learning approach is also adopted to efficiently learn optimal parameters. Experiment results show that very short video repeats and long ones can be detected with high accuracy. Video structure analysis by short video repeats mining is also presented in results. © 2010 Springer-Verlag Berlin Heidelberg.
Fri, 01 Jan 2010 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/673472010-01-01T00:00:00Z
- Approximations of the diffeomorphic metric and their applications in shape learning.https://scholarbank.nus.edu.sg/handle/10635/66930Title: Approximations of the diffeomorphic metric and their applications in shape learning.
Authors: Yang, X.; Goh, A.; Qiu, A.
Abstract: In neuroimaging studies based on anatomical shapes, it is well-known that the dimensionality of the shape information is much higher than the number of subjects available. A major challenge in shape analysis is to develop a dimensionality reduction approach that is able to efficiently characterize anatomical variations in a low-dimensional space. For this, there is a need to characterize shape variations among individuals for N given subjects. Therefore, one would need to calculate (2(N)) mappings between any two shapes and obtain their distance matrix. In this paper, we propose a method that reduces the computational burden to N mappings. This is made possible by making use of the first- and second-order approximations of the metric distance between two brain structural shapes in a diffeomorphic metric space. We directly derive these approximations based on the so-called conservation law of momentum, i.e., the diffeomorphic transformation acting on anatomical shapes along the geodesic is completely determined by its velocity at the origin of a fixed template. This allows for estimating morphological variation of two shapes through the first- and second-order approximations of the initial velocity in the tangent space of the diffeomorphisms at the template. We also introduce an alternative representation of these approximations through the initial momentum, i.e., a linear transformation of the initial velocity, and provide a simple computational algorithm for the matrix of the diffeomorphic metric. We employ this algorithm to compute the distance matrix of hippocampal shapes among an aging population used in a dimensionality reduction analysis, namely, ISOMAP. Our results demonstrate that the first- and second-order approximations are sufficient to characterize shape variations when compared to the diffeomorphic metric constructed through (2(N)) mappings in ISOMAP analysis.
Sat, 01 Jan 2011 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/669302011-01-01T00:00:00Z
- Approximations of the diffeomorphic metric and their applications in shape learninghttps://scholarbank.nus.edu.sg/handle/10635/88232Title: Approximations of the diffeomorphic metric and their applications in shape learning
Authors: Yang, X.; Goh, A.; Qiu, A.
Abstract: In neuroimaging studies based on anatomical shapes, it is well-known that the dimensionality of the shape information is much higher than the number of subjects available. A major challenge in shape analysis is to develop a dimensionality reduction approach that is able to efficiently characterize anatomical variations in a low-dimensional space. For this, there is a need to characterize shape variations among individuals for N given subjects. Therefore, one would need to calculate (2 N) mappings between any two shapes and obtain their distance matrix. In this paper, we propose a method that reduces the computational burden to N mappings. This is made possible by making use of the first- and second-order approximations of the metric distance between two brain structural shapes in a diffeomorphic metric space. We directly derive these approximations based on the so-called conservation law of momentum, i.e., the diffeomorphic transformation acting on anatomical shapes along the geodesic is completely determined by its velocity at the origin of a fixed template. This allows for estimating morphological variation of two shapes through the first- and second-order approximations of the initial velocity in the tangent space of the diffeomorphisms at the template. We also introduce an alternative representation of these approximations through the initial momentum, i.e., a linear transformation of the initial velocity, and provide a simple computational algorithm for the matrix of the diffeomorphic metric. We employ this algorithm to compute the distance matrix of hippocampal shapes among an aging population used in a dimensionality reduction analysis, namely, ISOMAP. Our results demonstrate that the first- and second-order approximations are sufficient to characterize shape variations when compared to the diffeomorphic metric constructed through (2 N) mappings in ISOMAP analysis. © 2011 Springer-Verlag.
Sat, 01 Jan 2011 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/882322011-01-01T00:00:00Z
- CSF and Brain Structural Imaging Markers of the Alzheimer's Pathological Cascadehttps://scholarbank.nus.edu.sg/handle/10635/66987Title: CSF and Brain Structural Imaging Markers of the Alzheimer's Pathological Cascade
Authors: Yang, X.; Tan, M.Z.; Qiu, A.
Abstract: Cerebral spinal fluid (CSF) and structural imaging markers are suggested as biomarkers amended to existing diagnostic criteria of mild cognitive impairment (MCI) and Alzheimer's disease (AD). But there is no clear instruction on which markers should be used at which stage of dementia. This study aimed to first investigate associations of the CSF markers as well as volumes and shapes of the hippocampus and lateral ventricles with MCI and AD at the baseline and secondly apply these baseline markers to predict MCI conversion in a two-year time using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our results suggested that the CSF markers, including Aβ42, t-tau, and p-tau, distinguished MCI or AD from NC, while the Aβ42 CSF marker contributed to the differentiation between MCI and AD. The hippocampal shapes performed better than the hippocampal volumes in classifying NC and MCI, NC and AD, as well as MCI and AD. Interestingly, the ventricular volumes were better than the ventricular shapes to distinguish MCI or AD from NC, while the ventricular shapes showed better accuracy than the ventricular volumes in classifying MCI and AD. As the CSF markers and the structural markers are complementary, the combination of them showed great improvements in the classification accuracies of MCI and AD. Moreover, the combination of these markers showed high sensitivity but low specificity for predicting conversion from MCI to AD in two years. Hence, it is feasible to employ a cross-sectional sample to investigate dynamic associations of the CSF and imaging markers with MCI and AD and to predict future MCI conversion. In particular, the volumetric information may be good for the early stage of AD, while morphological shapes should be considered as markers in the prediction of MCI conversion to AD together with the CSF markers. © 2012 Yang et al.
Wed, 19 Dec 2012 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/669872012-12-19T00:00:00Z
- Variations in eye volume, surface area, and shape with refractive error in young children by magnetic resonance imaging analysishttps://scholarbank.nus.edu.sg/handle/10635/88214Title: Variations in eye volume, surface area, and shape with refractive error in young children by magnetic resonance imaging analysis
Authors: Lim, L.S.; Yang, X.; Gazzard, G.; Lin, X.; Sng, C.; Saw, S.-M.; Qiu, A.
Abstract: Purpose. To determine variations in eye volume, surface area, and shape with refractive error in young children using a three-dimensional magnetic resonance imaging (MRI) model. Methods. A subset of Singaporean Chinese boys enrolled in the population-based Strabismus, Amblyopia, and Refractive Error in Singapore (STARS) study underwent MRI using a 3-Tesla whole body scanner with a 32-channel head coil. Eye volume and surface area were measured. Eye shape was assessed qualitatively from the three-dimensional models and quantitatively by measurement of the longitudinal axial length (LAL), horizontal width, and vertical height along the cardinal axes. Results. One hundred thirty-four eyes of 67 subjects (mean age, 77.9 ± 3.9 months) were analyzed. The mean spherical equivalent (SE) refraction was 0.65 ± 0.92 D (range, -2.31 to 4.13 D). More myopic SE was associated with larger surface area (-20.59 [-37.09 to -4.10] mm 2/D; P = 0.01) but not volume. In age-height adjusted models, more myopic SE was associated with longer LAL (-1.94 [-2.47 to -1.41] mm/D; P < 0.001) and greater width (-1.12 [-1.26 to -0.99] mm/D; P < 0.001) but not height (0.64 [-2.55 to 3.82] mm/D; P = 0.70). In nonmyopic subjects, less hyperopic SE was associated with longer AL (-0.40 [-0.71 to -0.10] mm/D; P = 0.01), width (-0.59 [-0.84 to -0.34] mm/D; P < 0.001), and height (-0.40 [-0.64 to -0.17] mm/D; P = 0.001). In three-dimensional models, myopic eyes conformed to an axial elongation model with a prolate profile in the axial plane, whereas nonmyopic eyes showed global expansion. Conclusions. Eye surface area increases with myopia in young children. Eye shape is different in myopia, even in its early stages. Axial globe enlargement occurs in myopic eyes leading to a prolate shape, whereas nonmyopic eyes enlarge globally in length, width, and height. © 2011 The Association for Research in Vision and Ophthalmology, Inc.
Tue, 01 Nov 2011 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/882142011-11-01T00:00:00Z
- Evolution of hippocampal shapes across the human lifespanhttps://scholarbank.nus.edu.sg/handle/10635/67044Title: Evolution of hippocampal shapes across the human lifespan
Authors: Yang, X.; Goh, A.; Chen, S.-H.A.; Qiu, A.
Abstract: Aberrant hippocampal morphology plays an important role in the pathophysiology of aging. Volumetric analysis of the hippocampus has been performed in aging studies; however, the shape morphometry-which is potentially more informative in terms of related cognition-has yet to be examined. In this paper, we employed an advanced brain mapping technique, large deformation diffeomorphic metric mapping (LDDMM), and a dimensionality reduction approach, locally linear diffeomorphic metric embedding (LLDME), to explore age-related changes in hippocampal shape as delineated from magnetic resonance (MR) images of 302 healthy adults aged from 18 to 94 years. Compared with the hippocampal volumes, the hippocampal shapes clearly showed the nonlinear trajectory of biological aging across the human lifespan, where the variation of hippocampal shapes by age was characterized by a cubic polynomial. By integrating of LDDMM and LLDME, we were also able to illustrate the average hippocampal shapes in each individual decade. In addition, LDDMM and LLDME facilitated the identification of 63 years as a threshold beyond which hippocampal morphological changes were accelerated. Adults over 63 years of age showed the inward-deformation bilaterally in the head of the hippocampi and the left subiculum regardless of hippocampal volume reduction when compared to adults younger than 63. Hence, we demonstrated that the shape of anatomical structures added another dimension of structural morphological quantification beyond the volume in understanding aging.© 2012 Wiley Periodicals, Inc.
Fri, 01 Nov 2013 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/670442013-11-01T00:00:00Z
- Locally Linear Diffeomorphic Metric Embedding (LLDME) for surface-based anatomical shape modelinghttps://scholarbank.nus.edu.sg/handle/10635/67141Title: Locally Linear Diffeomorphic Metric Embedding (LLDME) for surface-based anatomical shape modeling
Authors: Yang, X.; Goh, A.; Qiu, A.
Abstract: This paper presents the algorithm, Locally Linear Diffeomorphic Metric Embedding (LLDME), for constructing efficient and compact representations of surface-based brain shapes whose variations are characterized using Large Deformation Diffeomorphic Metric Mapping (LDDMM). Our hypothesis is that the shape variations in the infinite-dimensional diffeomorphic metric space can be captured by a low-dimensional space. To do so, traditional Locally Linear Embedding (LLE) that reconstructs a data point from its neighbors in Euclidean space is extended to LLDME that requires interpolating a shape from its neighbors in the infinite-dimensional diffeomorphic metric space. This is made possible through the conservation law of momentum derived from LDDMM. It indicates that initial momentum, a linear transformation of the initial velocity of diffeomorphic flows, at a fixed template shape determines the geodesic connecting the template to a subject's shape in the diffeomorphic metric space and becomes the shape signature of an individual subject. This leads to the compact linear representation of the nonlinear diffeomorphisms in terms of the initial momentum. Since the initial momentum is in a linear space, a shape can be approximated by a linear combination of its neighbors in the diffeomorphic metric space. In addition, we provide efficient computations for the metric distance between two shapes through the first order approximation of the geodesic using the initial momentum as well as for the reconstruction of a shape given its low-dimensional Euclidean coordinates using the geodesic shooting with the initial momentum as the initial condition. Experiments are performed on the hippocampal shapes of 302 normal subjects across the whole life span (18 - 94. years). Compared with Principal Component Analysis and ISOMAP, LLDME provides the most compact and efficient representation of the age-related hippocampal shapes. Even though the hippocampal volumes among young adults are as variable as those in older adults, LLDME disentangles the hippocampal local shape variation from the hippocampal size and thus reveals the nonlinear relationship of the hippocampal morphometry with age. © 2011 Elsevier Inc.
Sun, 01 May 2011 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/671412011-05-01T00:00:00Z