1211237
High Resolution Acquisition, Learning and Transfer of Dynamic 3D Facial Expressions
Synthesis and re-targeting of facial expressions is central to facial animation and often involves significant manual work in order to achieve realistic expressions, due to the difficulty of capturing high quality dynamic expression data. In this paper we address fundamental issues regarding the use of high quality dense 3-D data samples undergoing motions at video speeds, e.g. human facial expressions. In order to utilize such data for motion analysis and re-targeting, correspondences must be established between data in different frames of the same faces as well as between different faces. We present a data driven approach that consists of four parts: 1) High speed, high accuracy capture of moving faces without the use of markers, 2) Very precise tracking of facial motion using a multi-resolution deformable mesh, 3) A unified low dimensional mapping of dynamic facial motion that can separate expression style, and 4) Synthesis of novel expressions as a combination of expression styles. The accuracy and resolution of our method allows us to capture and track subtle expression details. The low dimensional representation of motion data in a unified embedding for all the subjects in the database allows for learning the most discriminating characteristics of each individual's expressions as that person's “expression style”. Thus new expressions can be synthesized, either as dynamic morphing between individuals, or as expression transfer from a source face to a target face, as demonstrated in a series of experiments. 
 
 
 
Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Animation; I.3.5 [Computer Graphics]: Curve, surface, solid, and object representations; I.3.3 [Computer Graphics]: Digitizing and scanning; I.2.10 [Artificial intelligence]: Motion ; I.2.10 [Artificial intelligence]: Representations, data structures, and transforms; I.2.10 [Artificial intelligence]: Shape; I.2.6 [Artificial intelligence]: Concept learning
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.
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.
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.

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.
12030912
Local optimization of triangular surface meshes for general quadrics in lp norm
For a set S of distinct points in the x-y plane and a 3-dimensional surface with the general quadratic equation f ey dx cxy by ax z + + + + + = 2 2 we study the problem of finding a locally optimal triangulation of S for the linear approximation of the surface under the Lp norm. We show that the Lp norm error is independent of the translation and rotation by 180 degrees of a single triangle in the x-y plane. This observation allows us to substantially simply the local optimization procedure, and in particular allows us to explicitly compute the separation curves. Locally optimal triangulations are important in the data dependent approximation methods. The paper generalizes earlier results by P. Desnogues and O. Devillers (presented at the Canadian Conference on Computational Geometry CCCG ‘95), who studied optimal local triangulations for the unit hyperbolic paraboloid in the L2 norm error.
15234434
Metadata driven memory optimizations in dynamic binary translator
A dynamic binary translator offers solutions for translating and running source architecture binaries on target architecture at runtime. Regardless of its growing popularity, practical dynamic binary translators usually suffer from the limited optimizations performed when generating the translated code due to the lack of useful information available in the executable files and the requirement to conform to the binary-level compatibility. Trying to generate more efficient translated code, we propose in this paper a novel method of passing performance critical information to a dynamic binary translator through the metadata section generated during the static compilation phase. With the performance critical metadata, the dynamic binary translator is able to perform aggressive optimizations to generate higher quality code. We implemented a general and extensible framework in GCC 4.0 and IA-32® Execution Layer, and selected metadata related to memory optimizations as our target. The metadata enables IA-32 EL to perform memory optimizations such as registerization, memory ordering relaxation and address disambiguation of memory instructions. Experimental data shows an overall performance improvement of 15.03% for SPECfp2000 and 1.21% for SPECint2000. For some specific benchmarks, the performance improvement is even up to 37.09%.
15440093
Pattern discovery: a data driven approach to decision support
Decision support nowadays is more and more targeted to large scale complicated systems and domains. The success of a decision support system relies mainly on its capability of processing large amounts of data and efficiently extracting useful knowledge from the data, especially knowledge which is previously unknown to the decision makers. With a large scale system, traditional knowledge acquisition models become inefficient and/or more biased, due to the subjectivity of the experts or the pre-assumptions of certain ideas or algorithmic procedures. Today, with the rapid development of computer technologies, the capability of collecting data has been greatly advanced. Data becomes the most valuable resource for an organization. We present a fundamental framework toward intelligent decision support by analyzing a large amount of mixed-mode data (data with a mixture of continuous and categorical values) in order to bridge the subjectivity and objectivity of a decision support process. By considering significant associations of artifacts (events) inherent in the data as patterns, we define patterns as statistically significant associations among feature values represented by joint events or hypercells in the feature space. We then present an algorithm which automatically discovers statistically significant hypercells (patterns) based on: 1) a residual analysis, which tests the significance of the deviation when the occurrence of a hypercell differs from its expectation, and 2) an optimization formulation to enable recursive discovery. By discovering patterns from data sets based on such an objective measure, the nature of the problem domain will be revealed. The patterns can then be applied to solve specific problems as being interpreted or inferred with.
16388786
Collecting Internet Malware Based on Client-side Honeypot
With the improvement of software security, attacks based on RPC vulnerabilities declined, however, attacks based on client application software vulnerabilities have increased. Such client application software includes Web browsers, email client and office. The spread of malware using these software vulnerabilities has become a severe threat to todaypsilas Internet. In allusion to this kind of threat, this paper designed an Internet malware collecting system based on client-side honeypot. This system can not only collect malware but also detect malicious Web site. It uses a unique network crawler based on client-side attack techniques to collect source of URL, and it collects URL and attachments from emails, then it creates software processes to open URL or files, and uses a device-drive monitor to detect malicious behaviors. It gives an alarm and locates the malicious file, and sends the malware coming through the Internet to the collecting server. We introduce the design and implement of this system and give the results.
16798311
Parameter Inference of Cost-Sensitive Boosting Algorithms
Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can be reduced most rapidly. This is the most crucial step of AdaBoost in converting a weak learning algorithm into a strong one. However, most reported cost-sensitive boosting algorithms ignore such a property. In this paper, we come up with three versions of cost-sensitive AdaBoost algorithms where the parameters for sample weight updating are induced. Then, their identification abilities on the small classes are tested on four “real world” medical data sets taken from UCI Machine Learning Database based on F-measure. Our experimental results show that one of our proposed cost-sensitive AdaBoost algorithms is superior in achieving the best identification ability on the small class among all reported cost-sensitive boosting algorithms.