15702195
OCR in a Hierarchical Feature Space
This paper describes hierarchical OCR, a character recognition methodology that achieves high speed and accuracy by using a multiresolution and hierarchical feature space. Features at different resolutions, from coarse to fine-grained, are implemented by means of a recursive classification scheme. Typically, recognizers have to balance the use of features at many resolutions (which yields a high accuracy), with the burden on computational resources in terms of storage space and processing time. We present in this paper, a method that adaptively determines the degree of resolution necessary in order to classify an input pattern. This leads to optimal use of computational resources. The hierarchical OCR dynamically adapts to factors such as the quality of the input pattern, its intrinsic similarities and differences from patterns of other classes it is being compared against, and the processing time available. Furthermore, the finer resolution is accorded to only certain "zones" of the input pattern which are deemed important given the classes that are being discriminated. Experimental results support the methodology presented. When tested on standard NIST data sets, the hierarchical OCR proves to be 300 times faster than a traditional K-nearest-neighbor classification method, and 10 times taster than a neural network method. The comparison uses the same feature set for all methods. Recognition rate of about 96 percent is achieved by the hierarchical OCR. This is at par with the other two traditional methods.
14381469
Using lexical similarity in handwritten word recognition
Recognition using only visual evidence cannot always be successful due to limitations of information and resources available during training. Considering relation among lexicon entries is sometimes useful for decision making. In this paper we present a method to capture lexical similarity of a lexicon and reliability of a character recognizer which serve to capture the dynamism of the environment. A parameter, lexical similarity, is defined by measuring these two factors as edit distance between lexicon entries and separability of each character's recognition results. Our experiments show that a utility function considering lexical similarity in a decision stage can enhance the performance of a conventional word recognizer.
16135620
Constructing fair-exchange protocols for E-commerce via distributed computation of RSA signatures
Applications such as e-commerce payment protocols, electronic contract signing, and certified e-mail delivery require that fair exchange be assured. A fair-exchange protocol allows two parties to exchange items in a fair way so that either each party gets the other's item, or neither party does. We describe a novel method of constructing very efficient fair-exchange protocols by distributing the computation of RSA signatures. Specifically, we employ multisignatures based on the RSA-signature scheme. To date, the vast majority of fair-exchange protocols require the use of zero-knowledge proofs, which is the most computationally intensive part of the exchange protocol. Using the intrinsic features of our multisignature model, we construct protocols that require no zero-knowledge proofs in the exchange protocol. Use of zero-knowledge proofs is needed only in the protocol setup phase--this is a one-time cost. Furthermore, our scheme uses multisignatures that are compatible with the underlying standard (single-signer) signature scheme, which makes it possible to readily integrate the fair-exchange feature with existing e-commerce systems.
5761929
Dual-beam structured-light scanning for 3-D object modeling
In this paper we present our Dual-Beam Structured-Light Scanner (DSLS), a scanning system that generates range maps much richer than those obtained from a conventional structured-light scanning system. Range maps produced by DSLS require fewer registrations for 3-D modeling. We show that the DSLS system more easily satisfies what are often difficult-to-satisfy conditions for determining the 3-D coordinates of an arbitrary object point. Two specific advantages of DSLS over conventional structured-light scanning are: (1) A single scan by the DSLS system is capable of generating range data on more surfaces than possible with the conventional approach using the same number of camera images. And (2) since the data collected by DSLS is more free of self-occlusions, the object needs be examined from a smaller number of viewpoints.
9807251
Originator Control in Usage Control
Originator Control is an access control policy that requires recipients to gain originator’s approval for redissemination of disseminated digital object. Originator control policies are one of the generic and key concerns of usage control. Usage control is an emerging concept which encompasses traditional access control and digital rights management solutions. However, current commercial Digital Rights Management (DRM) solutions lack enforcement of access control policies such as role-based access control (RBAC), mandatory access control (MAC), discretionary access control (DAC) and originator control because their control of access to digital object is mainly based on payment. In this paper, we attempt to combine originator control policies and usage control. Then we show how this can extend traditional originator control solutions to enforce access control policies even outside of a local control environment where a central control authority is not available. License and ticket concepts are proposed and used for originator control in usage control. Also, we define seven different solution approaches to deal with various dissemination situations. In addition, we discuss some published DRM solutions and relate these to our solution approaches.
11119047
Component-abnormality detection and immunization for survivable systems in large distributed environments
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.