17081981
Matching vehicles under large pose transformations using approximate 3D models and piecewise MRF model
We propose a robust object recognition method based on approximate 3D models that can effectively match objects under large viewpoint changes and partial occlusion. The specific problem we solve is: given two views of an object, determine if the views are for the same or different object. Our domain of interest is vehicles, but the approach can be generalized to other man-made rigid objects. A key contribution of our approach is the use of approximate models with locally and globally constrained rendering to determine matching objects. We utilize a compact set of 3D models to provide geometry constraints and transfer appearance features for object matching across disparate viewpoints. The closest model from the set, together with its poses with respect to the data, is used to render an object both at pixel (local) level and region/part (global) level. Especially, symmetry and semantic part ownership are used to extrapolate appearance information. A piecewise Markov Random Field (MRF) model is employed to combine observations obtained from local pixel and global region level. Belief Propagation (BP) with reduced memory requirement is employed to solve the MRF model effectively. No training is required, and a realistic object image in a disparate viewpoint can be obtained from as few as just one image. Experimental results on vehicle data from multiple sensor platforms demonstrate the efficacy of our method.
31836660
A Global Matching Framework for Stereo Computation
This paper presents a new global matching framework f o r stereo computation. In this framework, the second view is first predicted front the reference view using the depth information. A global match measure is then defined as the similarity function between the predicted image and the actual image. Stereo coniputcction is converted into a search problem where the goal is to find the depth rnup that maximizes the global match measure. The major advantage of this framework is that the global visibility constraint is inherently enforced in the computation. This paper explores several key components of this framework including ( I ) three color segmentation based depth representations, (2) an incremental warping algorithm that dramatically reduces the computational complexity, and (3) scene constraints such as the smoothness constraint and the color sinzilarity constraint. Experimental results using different types of depth representations are presented. The quality of the computed depth maps is demonstrated through image-based rendering from new viewpoints.
5884426
Validation of an optical flow method for tag displacement estimation
Presents a validation study of an optical-flow method for the rapid estimation of myocardial displacement in magnetic resonance tagged cardiac images. This registration and change visualization (RCV) software uses a hierarchical estimation technique to compute the flow field that describes the warping of an image of one cardiac phase into alignment with the next. This method overcomes the requirement of constant pixel intensity in standard optical-flow methods by preprocessing the input images to reduce any intensity bias which results from the reduction in stripe contrast throughout the cardiac cycle. To validate the method, SPAMM-tagged images were acquired of a silicon gel phantom with simulated rotational motion. The pixel displacement was estimated with the RCV method and the error in pixel tracking was <4% 1000 ms after application of the tags, and after 30/spl deg/ of rotation. An additional study was performed using a SPAMM-tagged multiphase slice of a canine left ventricle. The true displacement was determined using a previously validated active contour model (snakes). The error between methods was 6.7% at end systole. The RCV method has the advantage of tracking all pixels in the image in a substantially shorter period than the snakes method.
13996299
Action video retrieval based on atomic action vocabulary
We propose an efficient action retrieval system that is based on a novel action representation and an effective video matching method. We represent actions with a hierarchical encoding scheme that at low-level measures local body parts motions, which then evolves into encoding of instantaneous global body motions and finally high-level description of actions through atomic action vocabulary. Atomic action vocabulary extends the notion of keyframe-based indexing techniques, where a long action video is decomposed into a sequence of atomic sub-actions matched from the vocabulary. Efficient video matching is achieved by exploiting precomputed inter-vocabulary distances so that global video distance between video sequences can be computed in a very efficient manner that is equivalent to index lookup operations with minimal additional computational loads. Combined with atomic action vocabulary, this can provide flexible video matching schemes of finding compound action sequences of arbitrary lengths. The proposed approach is evaluated on surveillance video and a public video dataset.
6283962
Multi-view 3D analysis with applications for augmented reality and enhanced video visualization
The article presents methods for 3D scene geometry recovery/refinement and pose estimation from motion imagery in two representative scenarios. First, we present a method for pose estimation and scene geometry recovery from extended sequences without prior knowledge of the scheme. Second, we discuss how to recover camera poses when a rough scene model is provided. We show how to extend and refine the scene model using the recovered poses. Finally, we present applications of the above techniques for 3D imagery manipulation such as enhanced visualization for video and 3D insertion of synthetic objects in the imagery.
9659860
True multi-image alignment and its application to mosaicing and lens distortion correction
Multiple images of a scene are related through 2D/3D view transformations and linear and non-linear camera transformations. In all the traditional techniques to compute these transformations, especially the ones relying on direct intensity gradients, one image and its coordinate system have been assumed to be ideal and distortion free. In this paper, we present a formulation and an algorithm for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. For instance, in the presence of lens distortion, none of the images can be assumed to be ideal. In our formulation, all the images are modeled as intensity measurements represented in their respective coordinate systems, each of which is related to an ideal coordinate system through an interior camera transformation and an exterior view transformation. The goal of the accompanying algorithm is to compute an image in the ideal coordinate system while solving for the transformations that relate the ideal system with each of the data images. Key advantages of the technique presented in this paper are: (i) no reliance on one distortion free image, (ii) ability to register images and compute coordinate transformations even when the multiple images are of an extended scene with no overlap between the first and last frame of the sequence, and (iii) ability to handle linear and non-linear transformations within the same framework. The new algorithm is evaluated in the context of two applications: (i) correction of lens distortion, and (ii) creation of video mosaics.
6690431
Balanced Multithreading: Increasing Throughput via a Low Cost Multithreading Hierarchy
A simultaneous multithreading (SMT) processor can issue instructions from several threads every cycle, allowing it to effectively hide various instruction latencies; this effect increases with the number of simultaneous contexts supported. However, each added context on an SMT processor incurs a cost in complexity, which may lead to an increase in pipeline length or a decrease in the maximum clock rate. This paper presents new designs for multithreaded processors which combine a conservative SMT implementation with a coarse-grained multithreading capability. By presenting more virtual contexts to the operating system and user than are supported in the core pipeline, the new designs can take advantage of the memory parallelism present in workloads with many threads, while avoiding the performance penalties inherent in a many-context SMT processor design. A design with 4 virtual contexts, but which is based on a 2-context SMT processor core, gains an additional 26% throughput when 4 threads are run together.
6867265
Servo: a programming model for many-core computing
Conventional programming models were designed to be used by expert programmers for programming for large-scale multiprocessors, distributed computational clusters, or specialized parallel machines. These models, therefore, are deemed either too difficult for an "average" programmer (who will be expected to do parallel programming in the many-core world) or too inefficient to use for many-core architectures [3]. Similarly, conventional execution models were designed for performance, not scalability.
 To address the challenge of performance scalability for many-core architectures, we introduce Servo, a Service Oriented programming model that decomposes every program into a set of components (each with its own local memory, mutable state, and a program counter) that either request services or deliver services. Servo is characterized by the decoupling of logical communication mapping between program modules (or services) from physical communication mapping between modules. This allows the services to be migrated and replicated during execution. The proposed model also allows the granularity of data parallel operations to be changed dynamically. This allows runtime variation in the locking granularity which in turn enables higher write-parallelism. Finally, the models partition even data into services. This can significantly enhance locality and makes it possible to have superlinear speedups with increasing number of cores.
 Our preliminary investigations demonstrate significant performance scalability advantages for Servo.
27580015
Register Multimapping: A technique for reducing register bank conflicts in processors with large register files
In this paper, we investigate Register Multimapping as a technique to reduce register bank conflicts for processors with large register files, but relatively slow clock speeds. Register multimapping involves mapping an architectural register to multiple physical registers belonging to different banks. Reads can proceed using any of the physical registers, thereby minimizing read bank conflicts. Write conflicts can be minimized by allowing delayed allocation of physical registers [5]. Our experiments show that register multimapping can result in performance improvements up to 15% (10% on average) over baseline processors that are port-constrained. Improvements are up to 13% (5.5% on average) over port-constrained processors that support delayed allocation of registers.
16117863
A Comparative Study of Different Sensors for Smart Car Park Management
Wireless sensor networks offer a very attractive, low cost alternative for traffic monitoring and surveillance in car parking, freeways and intersection in different environmental conditions. In such systems, robust and reliable vehicle detection is a critical step, thus it is important to carefully evaluate the different modes of operations in order to address existing issues. This paper proposes a vision of improvements to wireless sensor car parking network system by presenting valuable comparison data for different sensors using different type of software functions and hardware components. The main goal of this work is to present a simple, automated analysis and comparison of power management, communication efficiency and threshold times using different sensors and a corresponding prediction of the above features. The results show a clear difference in various parameters by using different types of sensors for vehicle detection.
65135986
Model dependent inference of three-dimensional information from a sequence of two-dimensional images
In order to autonomously navigate through a complex environment, a mobile robot requires sensory feedback. This feedback will typically include the 3D motion and location of the robot and the 3D structure and motion of obstacles and other environmental features. The general problem considered in this thesis is how this 3D information may be obtained from a sequence of images generated by a camera mounted on a mobile robot. 
The first set of algorithms developed in this thesis are for robust determination of the 3D pose of the mobile robot from a matched set of model and image landmark features. Least-squares techniques for point and line tokens, which minimize both rotation and translation simultaneously are developed and shown to be far superior to the earlier techniques which solved for rotation first and then translation. However, least-squares techniques fail catastrophically when outliers (or gross errors) are present in the match data. Outliers arise frequently due to incorrect correspondences or gross errors in the 3D model. Robust techniques for pose determination are developed to handle data contaminated by fewer than 50.0% outliers. 
To make the model based approach widely applicable, it is necessary to be able to automatically build the landmark models. The approach adopted in this thesis is one of model extension and refinement. A partial model of the environment is assumed to exist and this model is extended over a sequence of frames. As will be shown in the experiments, the prior knowledge of the small partial model greatly enhances the robustness of the 3D structure computations. The initial 3D model may have errors and these too are refined over the sequence of frames. 
Finally, the sensitivity of pose determination and model extension to incorrect estimates of camera parameters is analyzed. It is shown that for small field of view systems, offsets in the image center do not significantly affect the location of the camera and the location of new 3D points in a world coordinate system. Errors in the focal length significantly affect only the component of translation along the optical axis in the pose computation.