8863189
On Texture Classification Using Fractal Dimension
The fractal dimension has been studied as a feature for texture analysis. It has been found that the fractal dimension is not an effective image texture measure but little is known about the reasons for the fractal dimension failing to be effective for texture analysis. This paper investigates into the underlying causes why the fractal dimension is not an effective image texture feature. Four mathematical properties have been identified which are responsible for the fractal dimension's ineffectiveness. The experimental results show that while the fractal dimension itself is hardly an effective feature for texture classification, it can considerably enhance other feature sets.
26820236
3-D IFS fractals as real-time graphics model
Abstract Fractals have recently been attracting increasing research interest. Unlike conventional geometrical figures, fractals have unlimited details—a potentially useful feature for artistic images. One way to generate fractals is to use Iterated Function Systems (IFS). There have been significant research efforts on 2-D IFS. Programs for rendering 2-D IFS fractals are readily available. 3-D IFS appear to be a natural extension but remain an area to be investigated. This paper explores the use of 3-D IFS for creating fascinating fractal scenes. An efficient coloring, lighting and mist effects scheme is proposed. A program using the proposed scheme has been developed that can render delicate scenes consisting of multiple IFS fractals in real-time on the PC.
205904713
Fuzzy aggregated connectedness for image segmentation
Fuzzy methodology has been successfully applied to image analysis and pattern recognition since the theory of fuzzy set was 0rst introduced by Zadeh [1]. It is believed that the fuzzy set theory models the inherent imprecision in images. Fuzzy geometrical approaches focus on the spatial measures and have attracted considerable research interest in handling the spatial uncertainty in images [2]. Fuzzy connectedness is an important topological property of images. The classical de0nition was given by Rosenfeld [3]. A modi0cation to the traditional concept of fuzzy connectedness was further proposed [4]. Based on the notion of fuzzy connectedness of image elements, fuzzy objects for n-dimensional digital spaces was exploited for image segmentation [5]. Fuzzy connectedness is a natural extension to the crisp connectedness and re:ects the imprecise topological structures of images. The algorithms in the literature usually compute the fuzzy connectedness with regard to the best path between a pair of points. This approach su;ers from high sensitivity to noise. Noise in corrupted images may misguide the detection of the best path and further in:uence the determination of fuzzy connectedness. We believe that this has mainly been due to the binarization in determining the best path. The information of a path is either fully preserved or discarded and this contradicts the concept of fuzzy sets. In this paper,
8311095
Classifying image texture with statistical landscape features
This paper proposes to use three-dimensional information derived from the graph of an image function for texture description. The graph of an image function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous images, the entire Brodatz texture set, as well as the VisTex texture collection.
16801553
Mini-Max Initialization for Function Approximation
No abstract available.
10274461
Pattern Classification Using Rectified Nearest Feature Line Segment
This paper proposes a new classification method termed Rectified Nearest Feature Line Segment (RNFLS). It overcomes the drawbacks of the original Nearest Feature Line (NFL) classifier and possesses a novel property that centralizes the probability density of the initial sample distribution, which significantly enhances the classification ability. Another remarkable merit is that RNFLS is applicable to complex problems such as two-spirals, which the original NFL cannot deal with properly. Experimental comparisons with NFL, NN(Nearest Neighbor), k-NN and NNL (Nearest Neighbor Line) using artificial and real-world datasets demonstrate that RNFLS offers the best performance.
8484723
Effective test metrics for test strategy evolution
How to measure a test process is a required competence for an effective software test manager. Effective test process measurement is essential for designing, evaluating and managing a cost-effective test strategy. Although some existing research has proposed many metrics for these purposes, most of those metrics are ignored or used individually. This paper summarizes a test process measurement pilot project of the IBM Electronic Commerce Development (ECD) test teams, and analyzes the effectiveness of using a "metric set" of complementary metrics for cost, time, and quality to measure the quality of test process based on the results of our project. Details are given for a "fictitious" project in order to explain the detailed considerations that go into adopting a cost-effective metric strategy. Further discussions about implementation problems, test wisdom learned in the project, and cost-effectiveness of the measurement activities and metrics are also presented. We conclude with suggestions for extensions of the work into the area of real-time, in-phase, in-process metrics.
15862425
A Fixpoint Theory for Non-monotonic Parallelism
This paper studies parallel recursions. The trace specification language used in this paper incorporates sequentiality, nondeterminism, reactiveness (including infinite traces), conjunctive parallelism and general recursion. The language is the minimum of its kind and thus provides a context in which we can study parallel recursions in general. In order to use Tarski's theorem to determine the fixpoints of recursions, we need to identify a well-founded partial order. A theorem of this paper shows that no appropriate order exists. Tarski's theorem alone is not enough to determine the fixpoints of parallel recursions. Instead of using Tarski's theorem directly, we reason about the fixpoints of terminating and nonterminating behaviours separately. Such reasoning is supported by the laws of a new composition called partition. We propose a fixpoint technique called the partitioned fixpoint, which is the least fixpoint of the nonterminating behaviours after the terminating behaviours reach their greatest fixpoint. The surprising result is that although a recursion may not be monotonic with regard to the lexical order, it must have the partitioned fixpoint, which equals the least lexical-order fixpoint. Since the partitioned fixpoint is well defined in any complete lattice, the results are applicable to various semantic models. Major existing fixpoint techniques simply become special cases of the partitioned fixpoint. For example, an Egli-Milner-monotonic recursion has its least Egli-Milner fixpoint, which can be shown to be the same as the partitioned fixpoint. The new technique is more general than the least Egli-Milner fixpoint in that the partitioned fixpoint can be determined even when a recursion is not Egli-Milner monotonic. Examples of non-monotonic recursions with fair-interleaving parallelism are studied. Their partitioned fixpoints are shown to be consistent with our intuitions.
365943
Modeling and Storing Scientific Protocols
We propose an abstract model for scientific protocols, where several atomic operators are proposed for protocol composition We distinguish two different layers associated with scientific protocols: design and implementation, and discuss the mapping between them We illustrate our approach with a representative example and describe ProtocolDB, a scientific protocol repository currently in development Our approach benefits scientists by allowing the archiving of scientific protocols with the collected data sets to constitute a scientific portfolio for the laboratory to query, compare and revise protocols.
1935912
A Novel Memory Structure for Embedded Systems: Flexible Sequential and Random Access Memory
The on-chip memory performance of embedded systems directly affects the system designers' decision about how to allocate expensive silicon area. A novel memory architecture, flexible sequential and random access memory (FSRAM), is investigated for embedded systems. To realize sequential accesses, small “links” are added to each row in the RAM array to point to the next row to be prefetched. The potential cache pollution is ameliorated by a small sequential access buffer (SAB). To evaluate the architecture-level performance of FSRAM, we ran the Mediabench benchmark programs on a modified version of the SimpleScalar simulator. Our results show that the FSRAM improves the performance of a baseline processor with a 16KB data cache up to 55%, with an average of 9%; furthermore, the FSRAM reduces 53.1% of the data cache miss count on average due to its prefetching effect. We also designed RTL and SPICE models of the FSRAM, which show that the FSRAM significantly improves memory access time, while reducing power consumption, with negligible area overhead.
17883721
Classification of self-consumable highlights for soccer video summaries
An effective scheme for soccer summarization is significant to improve the usage of this massively growing video data. The paper presents an extension to our recent work which proposed a framework to integrate highlights into play-breaks to construct more complete soccer summaries. The current focus is to demonstrate the benefits of detecting some specific audio-visual features during play-break sequences in order to classify highlights contained within them. The main purpose is to generate summaries which are self-consumable individually. To support this framework, the algorithms for shot classification and detection of near-goal and slow-motion replay scenes is described. The results of our experiment using 5 soccer videos (20 minutes each) show the performance and reliability of our framework