6770113
Image magnification method using joint diffusion
In this paper a new algorithm for image magnification is presented. Because linear magnification/interpolation techniques diminish the contrast and produce sawtooth effects, in recent years, many nonlinear interpolation methods, especially nonlinear diffusion based approaches, have been proposed to solve these problems. Two recently proposed techniques for interpolation by diffusion, forward and backward diffusion (FAB) and level-set reconstruction (LSR), cannot enhance the contrast and smooth edges simultaneously. In this article, a novel Partial Differential Equations (PDE) based approach is presented. The contributions of the paper include: firstly, a unified form of diffusion joining FAB and LSR is constructed to have all of their virtues; secondly, to eliminate artifacts of the joint diffusion, soft constraint takes the place of hard constraint presented by LSR; thirdly, the determination of joint coefficients, criterion for stopping time and color image processing are also discussed. The results demonstrate that the method is visually and quantitatively better than Bicubic, FAB and LSR.
62682777
Texture classification through directional empirical mode decomposition
This work presents a method for texture classification through directional empirical mode decomposition (DEMD). Although there have been many filtering based techniques proposed for texture retrieval, problems of non-adaptivity and redundancy are still hard to solve simultaneously. As a technique being introduced into signal processing, empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process. To apply EMD to texture classification, we propose a new method of extending 1-D EMD to 2-D case called DEMD. The approach adaptively decomposes images into local narrow band ingredients-intrinsic mode functions (IMFs) and extracts their features including frequency and envelopes. To improve its classification ability the fractal dimensions of the IMFs are also considered. Decomposition of several directions is computed for rotation invariance. Experiments for textures in Brodatz set and USC database indicate the effectiveness of our technique.
16274304
Face detection and tracking in video using dynamic programming
Face detection and tracking are important in video content analysis since the most important objects in most video are human beings. This paper proposes a new approach for combined face detection and tracking in video. The face detection algorithm is a fast template matching procedure using iterative dynamic programming (DP). Although the face detection algorithm is designed for frontal face, the same mechanism can also be applied to track non-frontal faces with online adapted face models. Due to the essence of template matching, the algorithm is capable of comparing the similarity among different faces, which makes it suitable for tracking the same face that occur at disjointed temporal locations in video. While the proposed face detection method provides comparable accuracy as the neural network based approach, it is much faster.
206638919
Stepwise Development of Fault-Tolerant Reactive Systems
Consider a reactive system of the form S1 ∥ S2. Given the specification of one component, say S1, methods are available to design S2 in a stepwise manner such that the composed system S1 ∥ S2 satisfies a given specification. In this paper, we use a similar paradigm to construct a fault-tolerant program. Given a program S1 that may suffer from hardware faults or software design faults, we show how a stepwise method can be used to design a program S2 so that S1 ∥ S2 meets a given specification. The main idea is demonstrated by solving a resource allocation problem.
9219349
Flexible Secure Multicasting in Active Networks
In this paper we describe an alternative, flexible approach to multicast security in active networks. Traditional schemes for securing multicast communication have key management and scalability problems for many typical applications. In addition, traditional mechanisms are not capable of expressing flexible, situational security policies for multicast sessions and participants. Our scheme exploits the computational power of active networks to provide dynamic, flexible security for multicast applications. One of the main advantages of our scheme is the low communication and key distribution overhead associated with multicast group management. Our approach is based on the Seraphim security architecture implementation, which uses active capabilities for access control. Seraphim is an extensible, reconfigurable security architecture that is flexible and accommodates a wide variety of security policies and mechanisms. It also provides applications and users the ability to create dynamically and enforce highly customized and situational policies. Using these policies we have developed several secure multicast applications that demonstrate the flexible nature and low overheads associated with our architecture.
17610722
A Relational Model for Object-Oriented Designs
This paper develops a mathematical characterisation of object-oriented concepts by defining an observation-oriented semantics for an object-oriented language (OOL) with a rich variety of features including subtypes, visibility, inheritance, dynamic binding and polymorphism. The language is expressive enough for the specification of object-oriented designs and programs. We also propose a calculus based on this model to support both structural and behavioural refinement of object-oriented designs. We take the approach of the development of the design calculus based on the standard predicate logic in Hoare and He’s Unifying Theories of Programming (UTP). We also consider object reference in terms of object identity as values and mutually dependent methods.
4908568
Insignificant shadow detection for video segmentation
To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then a Canny edge map is generated. After that, the shadow region is detected and removed through multiframe integration, edge matching, and region growing. Finally, a post processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for video segmentation in indoor environment. The experimental results demonstrate its good performance.