2947494
High Resolution Tracking of Non-Rigid Motion of Densely Sampled 3D Data Using Harmonic Maps
Abstract
We present a novel automatic method for high resolution, non-rigid dense 3D point tracking. High quality dense point clouds of non-rigid geometry moving at video speeds are acquired using a phase-shifting structured light ranging technique. To use such data for the temporal study of subtle motions such as those seen in facial expressions, an efficient non-rigid 3D motion tracking algorithm is needed to establish inter-frame correspondences. The novelty of this paper is the development of an algorithmic framework for 3D tracking that unifies tracking of intensity and geometric features, using harmonic maps with added feature correspondence constraints. While the previous uses of harmonic maps provided only global alignment, the proposed introduction of interior feature constraints allows to track non-rigid deformations accurately as well. The harmonic map between two topological disks is a diffeomorphism with minimal stretching energy and bounded angle distortion. The map is stable, insensitive to resolution changes and is robust to noise. Due to the strong implicit and explicit smoothness constraints imposed by the algorithm and the high-resolution data, the resulting registration/deformation field is smooth, continuous and gives dense one-to-one inter-frame correspondences. Our method is validated through a series of experiments demonstrating its accuracy and efficiency.

20945105
Real-Time Moving Vehicle Detection With Cast Shadow Removal in Video Based on Conditional Random Field
This paper presents an approach of moving vehicle detection and cast shadow removal for video based traffic monitoring. Based on conditional random field, spatial and temporal dependencies in traffic scenes are formulated under a probabilistic discriminative framework, where contextual constraints during the detection process can be adaptively adjusted in terms of data-dependent neighborhood interaction. Computationally efficient algorithm has been developed to discriminate moving cast shadows and handle nonstationary background processes for real-time vehicle detection in video streams. Experimental results show that the proposed approach effectively fuses contextual dependencies and robustly detects moving vehicles under heavy shadows even in grayscale video.
13511504
Non-Rigid Object Alignment with a Mismatch Template Based on Exhaustive Local Search
Non-rigid object alignment is especially challenging when only a single appearance template is available and target and template images fail to match. Two sources of discrepancy between target and template are changes in illumination and non-rigid motion. Because most existing methods rely on a holistic representation for the alignment process, they require multiple training images to capture appearance variance. We developed a patch-based method that requires only a single appearance template of the object. Specifically, we fit the patch-based face model to an unseen image using an exhaustive local search and constrain the local warp updates within a global warping space. Our approach is not limited to intensity values or gradients, and therefore offers a natural framework to integrate multiple local features, such as filter responses, to increase robustness to large initialization error, illumination changes and non-rigid deformations. This approach was evaluated experimentally on more than 100 subjects for multiple illumination conditions and facial expressions. In all the experiments, our patch-based method outperforms the holistic gradient descent method in terms of accuracy and robustness of feature alignment and image registration.
46437694
Estimation of multiple directional illuminants from a single image
We present a new method for the detection and estimation of multiple directional illuminants, using only one single image of an object with known geometry. It obviates the need to modify the imaged scene by inserting calibration objects of any particular geometry, relying instead on partial knowledge of the geometry of the scene. We first develop our method for the case of a Lambertian sphere with known size, illuminated by a set of directional light sources. A novel and robust way is proposed to segment the surface into regions, with each region illuminated by a different set of sources. Our region-based least-squares method is impervious to noise and missing data, which is crucial to extending the method to arbitrary smooth geometry and to surfaces having both Lambertian and specular properties. We propose a novel methodology that integrates information from shadows and shading in the presence of strong directional sources of illumination, even when significant non-directional sources exist in the scene and the object surface is not Lambertian. We demonstrate experimentally the accuracy of our method, both in detecting the number of light sources and in estimating their directions, by testing on images of a variety of synthetic and real objects.
20961254
Strictly Nonblocking Multirate logd(N, m, p) Networks
We give necessary and sufficient conditions for the d-nary multilog network to be strictly nonblocking under the discrete multirate model, and we give sufficient conditions for the same under the continuous multirate model.
9142519
Utilizing field usage patterns for Java heap space optimization
This research studies the characteristics of field usage patterns in the SpecJVM98 benchmarks. It finds that multiple object instances of the same class often exhibit different field-usage patterns. Motivated by this observation, we designed a heap compression mechanism that classifies object instances at runtime based on their field-usage patterns and eliminates unused fields to save space. To achieve the maximum space savings while minimizing the space and time overhead, our design combines three interrelated techniques in a novel manner: runtime object instance classification, field virtualization, and bidirectional object layout. An experimental evaluation reveals that this mechanism can reduce the maximum heap occupancy of SpecJVM98 benchmarks by up to 18% and 14% on average while keeping the application execution overhead low.
2456151
Subdivision Schemes and Refinement Equations with Nonnegative Masks
We consider the two-scale refinement equation f(x)=@?^N"n"="0c"nf(2x-n) with @?"nc"2"n=@?"nc"2"n"+"1=1 where c"0, c"N 0 and the corresponding subdivision scheme. We study the convergence of the subdivision scheme and the cascade algorithm when all c"n>=0. It has long been conjectured that under such an assumption the subdivision algorithm converge, and the cascade algorithm converge uniformly to a continuous function, if and only if only if 00} is 1. We prove the conjecture for a large class of refinement equations.
2680189
Recognition of Coronary Heart Disease Patients by RBF Neural Network Basing on Contents of Microelements in Human Blood
Radial-Basis-Function (RBF) artificial neural network was developed to recognize the coronary heart disease patients basing on the contents of microelements in human blood. Leave-one out method was used to train the model. After training, the RBF model was used to recognize the coronary heart disease patients. Results showed that the RBF model recognized the three samples correctly, and the accuracy of RBF model was higher than the BP model. It showed that the RBF model could recognize the patients more accurately and it has important theoretical meaning and application value.
117805231
Hierarchical methods for three-dimensional surface reconstruction
Surface reconstruction from raw data such as volume data, implicit functions, and scattered data in three-dimensional space is very useful in medical imaging, scientific visualization, and geometric modeling. In this study, several surface reconstruction techniques are investigated. A new surface reconstruction technique and several interpolation schemes are introduced for processing the volume data. A new hierarchical scheme for extracting three-dimensional surfaces from volume data is presented. The hierarchical scheme is used to visualize different levels of surface detail allowing a user to explore different features of the surface at different scales. Estimated surface curvature is used as a smoothness condition, and the distance measurement between different levels of surfaces constructed is used as a stopping criterion. The volume pyramid used by the hierarchical scheme was built and tested by two different data structures: an octree and wavelet transforms. The hierarchical method using three-dimensional wavelet transforms is also useful in multiresolution surface analysis. An adaptive hierarchical method using an octree is implemented to increase the accuracy of the surface by adaptively subdividing regions of high curvature using an appropriate interpolation scheme without substantially increasing the number of polygons.
206733202
Foreground and Shadow Detection Based on Conditional Random Field
This paper presents a conditional random field (CRF) approach to integrate spatial and temporal constraints for moving object detection and cast shadow removal in image sequences. Interactions among both detection (foreground/background/shadow) labels and observed data are unified by a probabilistic framework based on the conditional random field, where the interaction strength can be adaptively adjusted in terms of data similarity of neighboring sites. Experimental results show that the proposed approach effectively fuses contextual dependencies in video sequences and significantly improves the accuracy of object detection.
21082976
Real-time machine learning in embedded software and hardware platforms
This paper describes on-going research work into real-time machine learning using embedded software and reconfigurable hardware. The main focus of the work is to develop real-time incremental learning methods particularly targeted at demonstration in mobile robot environments. Three main areas are described. The first represents reactive robot navigation knowledge using a novel frequency table technique whose memory requirement is known a priori. The second area investigates a Genetic Algorithm (GA) method that combines planning and reactive approaches to allow navigation to proceed even in the face of time constraints. In the third area we are developing novel hardware-based machine learning systems suitable for implementation in reconfigurable platforms.