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.
6287453
A dynamic assignment problem in a mobile system with limited bandwidth
The assignment problem originally arising from parallel and distributed computing has been investigated intensively since the 70's when Harold Stone proposed a method to solve it with the aid of network flow algorithms. In this paper, we discuss this problem under a mobile environment with limited capacity of wireless links. The assignment problem is to schedule a group of related objects from a resource-constrained mobile device to its powerful proxy machine so that program activity migrates among processors as execution proceeds, and hence the overall performance will improve. First, we reduce this optimization problem to the 0/1 knapsack problem, and therefore show that it is NP-hard. Second, two heuristic algorithms are proposed. One is an adaptive heuristic algorithm with complexity O(NE), here, N and E represent the number of nodes and edges in the object graph abstracted from the mobile application. The other is a non-adaptive heuristic algorithm with complexity O(E), i.e, N times faster than the adaptive counterpart. Finally, we provide a simple distributed algorithm based on Raymond's tree-based distributed mutual exclusion algorithm for solving this problem with message complexity O(N/sup 2/ log N).
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.
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.
19020033
Estimation of multiple directional light sources for synthesis of augmented reality images
We present a new method for the detection and estimation of multiple directional illuminants, using a single image of any object with known geometry and Lambertian reflectance. We use the resulting highly accurate estimates to modify virtually the illumination and geometry of a real scene and produce correctly illuminated mixed reality images. Our method 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. Thus, the recovered multiple illuminants can be used both for image-based rendering and for shape reconstruction. Our method combines information both from the shading of the object and from shadows cast on the scene by the object. Initially we use a method based on shadows and a method based on shading independently. The shadow based method utilizes brightness variation inside the shadows cast by the object, whereas the shading based method utilizes brightness variation on the directly illuminated portions of the object. We demonstrate how the two sources of information complement each other in a number of occasions. We then describe an approach that integrates the two methods, with results superior to those obtained if the two methods are used separately. The resulting illumination information can be used (i) to render synthetic objects in a real photograph with correct illumination effects, and (ii) to virtually re-light the scene.
28218
IA-32 Execution Layer: a two-phase dynamic translator designed to support IA-32 applications on Itanium-based systems
IA-32 Execution Layer (IA-32 EL) is a newtechnology that executes IA-32 applications onIntel® Itanium® processor family systems.Currently, support for IA-32 applications onItanium-based platforms is achieved usinghardware circuitry on the Itanium processors.This capability will be enhanced with IA-32EL-software that will ship with Itanium-basedoperating systems and will convert IA-32instructions into Itanium instructions viadynamic translation.In this paper, we describeaspects of the IA-32 Execution Layertechnology, including the general two-phasetranslation architecture and the usage of asingle translator for multiple operatingsystems.The paper provides details of someof the technical challenges such as preciseexception, emulation of FP, MMXTM, and Intel®Streaming SIMD Extension instructions, andmisalignment handling.Finally, the paperpresents some performance results.
36297757
Density Prediction of Selective Laser Sintering Parts Based on Artificial Neural Network
Selective laser sintering (SLS), one of rapid prototyping technologies, employs laser beam to selectively fuse fully powder into a solid object layer by layer. However, density prediction of SLS parts using finite elements analysis (FEA) having been reported, heavily depends on the precision of the FEA model. An Artificial neural network (ANN) approach presented in this paper has been developed for density prediction of SLS parts. Two-layer supervised neural networks are used, and the inputs to the neural network are known SLS process parameters such as laser power, scan speed, scan spacing and layer thickness. Orthogonal experimental method is employed for collection of experimental training and test sets. The construction of network is also investigated. Comparison of predicted and experimental data has confirmed the accuracy of the ANN approach.
19667746
Study on the Harmonious Operation of Logistics Finance Management of Supply Chain in Ports-Hinterland Based on System Dynamics
Abstract-Taking China's ports-hinterland logistics system as an example, the paper discusses the harmonious operation of logistics finance management of supply chain which is composed of the functional entity activities such as transportation, warehousing, packaging, circulation and information transmission, as well as the goods movement of all sectors of the logistics process. The logistics finance management is complex operating system with dynamics features, which is established on the basis of a "virtual organization" made up by nodes in supply chain. In order to provide a new research perspective on the coordination development of ports-hinterland logistics system, a dynamics model of logistics finance management of supply chain was set up based on the theory of system dynamics.
9978380
Learning a discriminative hidden part model for human action recognition
We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditional random field (hCRF) for object recognition. Similar to hCRF for object recognition, we model a human action by a flexible constellation of parts conditioned on image observations. Different from object recognition, our model combines both large-scale global features and local patch features to distinguish various actions. Our experimental results show that our model is comparable to other state-of-the-art approaches in action recognition. In particular, our experimental results demonstrate that combining large-scale global features and local patch features performs significantly better than directly applying hCRF on local patches alone.
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.