52850595
LOD Canny Edge Based Boundary Edge Selection for Human Body Tracking
We propose a simple method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Our method is based on level-of-detail (LOD) Canny edge maps and graph-based routing operations on the LOD maps. LOD Canny edge maps are generated by changing scale parameters for a given image. Simple (strong) Canny edge map has the smallest number of edge pixels while the most detailed Canny edge map, Wcanny N , has the biggest number of edge pixels. We start our basic tracking using strong Canny edges generated from large image intensity gradients of an input image, called Scanny edges to reduce side-effects because of irrelevant edges. Starting from Scanny edges, we get more edge pixels ranging from simple Canny edge maps until the most detailed Canny edge maps. LOD Canny edge pixels become nodes in routing, and LOD values of adjacent edge pixels determine routing costs between the nodes. We find a best route to follow Canny edge pixels favoring stronger Canny edge pixels. Our accurate tracking is based on reducing effects from irrelevant edges by selecting the stronger edge pixels, thereby relying on the current frame edge pixel as much as possible contrary to other approaches of always combining the previous contour. Our experimental results show that this tracking approach is robust enough to handle a complex-textured scene.
34356748
Model-Based Human Motion Capture from Monocular Video Sequences
Generating motion and capturing motion of an articulated body for computer animation is an expensive and time-consuming task. Conventionally, animators manually generate intermediate frames between key frames, but this task is very labor-intensive. This paper presents a model-based singularity-free automatic-initialization approach to capturing human motion from widely-available, static background monocular video sequences. A 3D human body model is built and projected on a 2D projection plane to find the best fit with the foreground image silhouette. We convert the human motion capture problem into two types of parameter optimization problems: static optimization and dynamic optimization. First, we determine each model body configuration using static optimizations for every input image. Then, to obtain better description of motion, the results from all static optimizations are fed into a dynamic optimization process where the entire sequence of motion is considered for the user-specified motion. The user-specified motion is defined by the user and the final form of the motion they want. A cost function for static optimization is used to estimate the degree of overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. A cost function for dynamic optimization is the user-specified motion based on the static optimization results as well as image fitting. Our method is used to capture various human motions: walking, pushing, kicking, and handshaking.
8794763
Video Retrieval of Human Interactions Using Model-Based Motion Tracking and Multi-layer Finite State Automata
Recognition of human interactions in a video is useful for video annotation, automated surveillance, and content-based video retrieval. This paper presents a model-based approach to motion tracking and recognition of human interactions using multi-layer finite state automata (FA). The system is used for widely-available, static-background monocular surveillance videos. A three-dimensional human body model is built using a sphere and cylinders and is projected on a two-dimensional image plane to fit the foreground image silhouette. We convert the human motion tracking problem into a parameter optimization problem without the need to compute inverse kinematics. A cost functional is used to estimate the degree of the overlap between the foreground input image silhouette and a projected three-dimensional body model silhouette. Motion data obtained from the tracker is analyzed in terms of feet, torso, and hands by a behavior recognition system. The recognition model represents human behavior as a sequence of states that register the configuration of individual body parts in space and time. In order to overcome the exponential growth of the number of states that usually occurs in single-level FA, we propose a multi-layer FA that abstracts states and events from motion data at multiple levels: low-level FA analyzes body parts only, and high-level FA analyzes the human interaction. Motion tracking results from video sequences are presented. Our recognition framework successfully recognizes various human interactions such as approaching, departing, pushing, pointing, and handshaking.
62242004
Object boundary edge selection using normal direction derivatives of a contour in a complex scene
Nguyen proposed a method [H.T., Nguyen et al., 2002] for tracking a nonparameterized object (subject) contour in a single video stream. Nguyen's approach combined outputs of two steps: creating a predicted contour and removing background edges. We propose a method to increase object tracking accuracy by improving the background edge removal process. Nguyen's background edge removal method of leaving many irrelevant edges is subject to inaccurate contour tracking. Our accurate tracking is based on reducing affects from irrelevant edges by selecting the boundary edge only. We select high-valued edge pixels of average image intensity gradients in the contour normal direction. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.
16813459
Scenario-based multitasking for real-time object-oriented models
Abstract Contemporary embedded systems quite often employ extremely complicated software consisting of a number of interrelated components, and this has made object-oriented design methodologies widely used in practice. To implement an object-oriented model in given target hardware, it is imperative to derive a set of tasks from the designed objects. This process of determining tasks and the events they handle greatly influences the real-time performance of the resultant system including response times and real-time guarantees. However, the innate discrepancies between objects and tasks make this exceedingly difficult, and many developers are forced to find their task sets through trial and error. In this paper, we propose Scenario-based Implementation Synthesis Architecture (SISA), an architecture consisting of a method for deriving a task set from a given object-oriented model and the development tools and run-time system architecture to support the method. A system developed with SISA guarantees the optimal response time for each event while deriving the smallest possible number of tasks. We have fully implemented SISA by extending the RoseRT development tool and applied it to an existing industrial PBX (private branch exchange) system. The experimental results show that SISA outperforms the best known conventional techniques by reducing maximum response times an average of 30.3%.
10180647
Specularity elimination in range sensing for accurate 3D modeling of specular objects
We present a novel range sensing method that is capable of constructing accurate 3D models of specular objects. Our method utilizes a new range imaging concept called multipeak range imaging, which accounts for the effects of mutual reflections. False measurements generated by mutual reflections are then eliminated by applying a series of constraint tests based on local smoothness, global coordinate consistency and visibility consistency. We show the usefulness of our method by applying the method to three real objects with specular surfaces. The ground truth data for those three objects were also acquired in order to evaluate the elimination of false measurements and to justify the selection of the parameters in the constraint tests. Experimental results indicate that our method significantly improves upon the traditional methods for constructing reliable 3D models of specular objects with complex shapes.
17481727
Strain analysis and visualization: left ventricle of a heart
Abstract Clinical utility of computational models is crucial in the applications of medical data visualization. Previously we have developed a new class of volumetric models whose parameters are functions in conjunction with physically based deformable modeling framework, and have applied the technique to estimate the left ventricular (LV) wall motion. We have successfully showed that the model parameter functions characterize the LV motion of normal and abnormal states and that no further non-trivial post-processing is required for anatomically meaningful interpretation. In an effort to evaluate the LV model, this paper presents a method and results from a strain analysis based on the nodal displacements of the deformable LV model. Furthermore, in order to visualize the local quantities on the volumetric model for an effective analysis, we also developed a methodology to assist in assessing the cardiac function utilizing principal strains, Von-Mises’ yield criteria, and a smoothing filter. Each strain tensor component was in the range of values observed in other reported studies. The application of a smoothing filter on the model improved in visualizing the overall trend of each strain variation. With our platform for a comprehensive strain analysis, we have augmented a clinical utility to the deformable models with parameter functions.
14413427
Memory Footprint Reduction with Quasi-Static Shared Libraries in MMU-less Embedded Systems
Despite a rapid decrease in the price of solid state memory devices, system memory is still a very precious resource in embedded systems. The use of shared libraries is known to be effective in significantly reducing memory usage. Unfortunately, many resource-constrained embedded systems lack MMU, making it extremely difficult to support this technique. To address this problem, we propose a novel shared library scheme called the quasi-static shared library. In quasi-static shared libraries, global symbols are bound to pseudo-addresses at linking time and the actual physical addresses are bound at loading time. This scheme is made possible by emulating MMU’s memory mapping feature with a Data Section Base Register (DSBR) and a Data Section Base Table (DSBT). Quasi-static shared libraries do not require symbol tables which take up time and space at runtime. We have implemented the proposed scheme in a commercial ADSL (Asymmetric Digital Subscriber Line) home network gateway and conducted a series of experiments measuring its memory usage and performance overhead. The result is drastic: a 35% reduction in flash memory usage and a 10% reduction in RAM usage. These results were achieved with only a negligible performance penalty of less than 4%. Even though this scheme was applied to uClinux-based embedded systems, it can be used for any MMU-less real-time operating system.
14438478
A Certificate-Based Signature Scheme
In this paper, we propose the security notion of certificate-based signature that uses the same parameters and certificate revocation strategy as the encryption scheme presented at Eurocrypt 2003 by Gentry. Certificate-based signature preserves advantages of certificate-based encryption, such as implicit certification and no private key escrow. We present concrete certificate-based signature schemes derived from pairings on elliptic curves and prove their security in the random oracle model assuming that the underlying group is GDH. Additionally, we propose a concrete delegation-by-certificate proxy signature scheme which is derived from a certificate-based signature scheme after simple modifications. Our proxy scheme is provably secure in the random oracle model under the security notion defined by Boldyreva, Palacio and Warinschi.