Daniel Pak-Kong Lun

Precise modeling of design patterns in UML

Prior research attempts to formalize the structure of object-oriented design patterns for a more precise specification of design patterns. It also allows automation support to be developed for user-defined design patterns in the future CASE tools. Targeting to a particular type of automation (e.g. verification of pattern instances), previous specification approaches over-specify pattern structures to a certain extend. Over-specification makes pattern specification ambiguous and disallows the specification language to be used for specifying compound patterns. In this paper, we present the structural properties of design patterns which reveal the true abstract nature of pattern structures. To support these properties so as to solve the over-specification problem, we propose an extension to UML 1.5 (basically UML 1.4 with Action semantics). The specialization and refining mechanism of UML provides also a smooth support for the instantiation, refinement and integration of pattern structures specified in UML. Our work makes no significant extension to the UML 1.5 meta-model but more in a UML Profile approach to ease the migration of our work to UML 2.0, which has not yet officially released by OMG during this work.

Image Analysis for Mapping Immeasurable Phenotypes in Maize [Life Sciences]

This work will allow bio-informaticians to analyze the ever-increasing gene sequence data, discover valuable knowledge in maize biology and related plant; development, and understand subtle variations among different phenotypes. Furthermore, successful measuring of visual phenotypes will advance plant research by finding the genes and/or environmental factors that cause a given visual phenotype. In what follows, the field of plant genetics is introduced (particularly quantitative trait loci and disease scoring) to the signal processing community, discuss the challenges involved, and present an image analysis system for precisely quantifying and mapping immeasurable phenotypes in maize

The discrete periodic Radon transform

In this correspondence, a discrete periodic Radon transform and its inversion are developed. The new discrete periodic Radon transform possesses many properties similar to the continuous Radon transform such as the Fourier slice theorem and the convolution property, etc. With the convolution property, a 2-D circular convolution can be decomposed into 1-D circular convolutions, hence improving the computational efficiency. Based on the proposed discrete periodic Radon transform, we further develop the inversion formula using the discrete Fourier slice theorem. It is interesting to note that the inverse transform is multiplication free. This important characteristic not only enables fast inversion but also eliminates the finite-word-length error that may be generated in performing the multiplications.

Optimizing the multiwavelet shrinkage denoising

Denoising methods based on wavelet domain thresholding or shrinkage have been found to be effective. Recent studies reveal that multivariate shrinkage on multiwavelet transform coefficients further improves the traditional wavelet methods. It is because multiwavelet transform, with appropriate initialization, provides better representation of signals so that their difference from noise can be clearly identified. We consider the multiwavelet denoising by using multivariate shrinkage function. We first suggest a simple second-order orthogonal prefilter design method for applying multiwavelet of higher multiplicities. We then study the corresponding thresholds selection using Steins unbiased risk estimator (SURE) for each resolution level provided that we know the noise structure. Simulation results show that higher multiplicity wavelets usually give better denoising results and the proposed threshold estimator suggests good indication for optimal thresholds.

Orthogonal discrete periodic Radon transform: part I: Theory and realization

The discrete periodic Radon transform (DPRT) was proposed recently. It was shown that DPRT possesses many useful properties that are similar to the conventional continuous Radon transform. Using these properties, a 2-D signal can be processed by some 1-D approaches to reduce the computational complexity. However, the non-orthogonal structure of DPRT projections introduces redundant operations that often lower the efficiency of the technique in applications. In this paper, we propose the orthogonal discrete periodic Radon transform (ODPRT) in which a new decomposition approach is introduced. All ODPRT projections are modified to be orthogonal such that redundancy is eliminated. Furthermore, we consider the efficient realization for computing ODPRT and its inverse that make the proposed ODPRT more feasible in practical applications.

Efficient blind image restoration using discrete periodic Radon transform

Restoring an image from its convolution with an unknown blur function is a well-known ill-posed problem in image processing. Many approaches have been proposed to solve the problem and they have shown to have good performance in identifying the blur function and restoring the original image. However, in actual implementation, various problems incurred due to the large data size and long computational time of these approaches are undesirable even with the current computing machines. In this paper, an efficient algorithm is proposed for blind image restoration based on the discrete periodic Radon transform (DPRT). With DPRT, the original two-dimensional blind image restoration problem is converted into one-dimensional ones, which greatly reduces the memory size and computational time required. Experimental results show that the resulting approach is faster in almost an order of magnitude as compared with the traditional approach, while the quality of the restored image is similar.

Content-based scalable H.263 video coding for road traffic monitoring

For sending video data through very low bit-rate mobile channels, video codec with high compression rate is the pre-requisite. Although the H.263 video codec is recommended as one of the candidates due to its simplicity and efficiency, it is generally believed that its compression efficiency can be further improved if the content-based scalable video coding technique can be applied. In this paper, we propose a modified H.263 encoder which supports real-time content-based scalable video coding. The proposed technique is applied to real-time video surveillance systems for road traffic monitoring. For the proposed approach, the moving objects, i.e. cars, are first extracted from the steady background. Their activities are then further classified as fast or slow by assessing the regularity of their motion. The information is then passed to a modified H.263 encoder to reduce the temporal and spatial redundancies in the video. As compared with the conventional H.263 encoder using for the same application, the proposed system has a 20% increase in compression rate with negligible visual distortion. The proposed system fully complies with the ITU H.263 standard hence the encoded bit stream is completely comprehensible to the conventional H.263 decoder.

Orthogonal discrete periodic Radon transform: part II: applications

In this paper, we study the properties and possible applications of the newly proposed orthogonal discrete periodic Radon transform (ODPRT). Similar to its previous version, the new ODPRT also possesses the useful properties such as the discrete Fourier slice theorem and the circular convolution property. They enable us to convert a 2-D application into some 1-D ones such that the computational complexity is greatly reduced. Two examples of using ODPRT in the realization of 2-D circular convolution and blind image resolution are illustrated. With the fast ODPRT algorithm, efficient realization of 2-D circular convolution is achieved. For the realization of blind image restoration, we convert the 2-D problem into some 1-D ones that reduces the computation time and memory requirement. Besides, ODPRT adds more constraints to the restoration problem in the transform domain that makes the restoration solution better. Significant improvement is obtained in each case when comparing with the traditional approaches in terms of quality and computation complexity. They illustrate the potentially widespread applications of the proposed technique.

On prime factor mapping for the discrete Hartley transform

The authors propose a new prime factor mapping scheme, which requires no extra arithmetic operations for the realization of prime factor mapping, for the computation of the discrete Hartley transform (DHT). It is achieved by embedding all the extra arithmetic operations into the subsequent short-length computations, with the computational complexities of these embedded short lengths remaining unchanged. Consequently, the present approach significantly eliminates the burden which is introduced by the extra arithmetic operations. With this mapping scheme, it is further demonstrated that a prime-factor-mapped DHT would have superb performance compared with other fast DHT algorithms. >

On the efficient computation of 2-d image moments using the discrete radon transform

Abstract In this paper, a fast algorithm for the computation of two-dimensional image moments is proposed. In our approach, a new discrete Radon transform (DRT) is used for the major part of the algorithm. The new DRT preserves an important property of the continuous Radon transform that the regular or geometric moments can be directly obtained from the projection data. With this property, the computation of two-dimensional (2-D) image moments can be decomposed to become a number of one-dimensional (1-D) ones, hence it reduces greatly the computational complexity. The new DRT algorithm can be applied with a recursive approach such that the number of multiplication required is further reduced. However, the number of addition will then be increased. It suits to the situation where the effort for realizing multiplication is much greater than addition. Comparisons of the present approaches with some known methods show that the proposed algorithms significantly reduce the complexity and computation time.