Multi-resolution System Research Articles

Lip print recognition for security systems by multi-resolution architecture

Biometric systems are forms of technology that use unique human physical characteristics to automatically identify individuals. They have sensors to pick up physical characteristics, convert them into digital patterns, and compare them with patterns stored for individual identification. However, lip print recognition has been less developed than the recognition of other human physical attributes such as the fingerprint, voice patterns, retinal blood vessel patterns, or the face. Lip print recognition by a CCD camera has the merit of being linked with other recognition systems such as the retinal/iris eye and the face. A new method using multi-resolution architecture is proposed to recognize a lip print from pattern kernels. A set of pattern kernels is a function of some local lip print masks. This function converts the information from a lip print into digital data. Recognition in the multi-resolution system is more reliable than recognition in the single-resolution system. Multi-resolution architecture allows us to reduce the false recognition rate from 15 to 4.7%. This paper shows that a lip print is sufficiently used by the measurements of biometric systems.

Multiresolutional hierarchical decision support systems

Decision support systems gain better performance and higher accuracy by the virtue of building multiresolutional (multigranular, multiscale) representation, and employing multiscale behavior generation subsystem (planning and control). The latter are equipped by devices for unsupervised learning that adjust their functioning to the results of self-identification. We demonstrate that planning and learning are joint processes. The author's intention is to emphasize that the concepts of multiresolutional representation (MR) and multiresolutional decision support (MR-DSS) probably have in common a general significance that crosses the boundaries of particular domains of applications and disciplines. The paper explores this phenomenon. The ubiquity of a principle that somehow persistently delivers benefits to many areas of knowledge and technology seems to be more important than a habit to follow the pigeonhole principle of paper presentation.

The Wavelet Element Method Part II. Realization and Additional Features in 2D and 3D

The Wavelet Element Method (WEM) provides a construction of multiresolution systems and biorthogonal wavelets on fairly general domains. These are split into subdomains that are mapped to a single reference hypercube. Tensor products of scaling functions and wavelets defined on the unit interval are used on the reference domain. By introducing appropriate matching conditions across the interelement boundaries, a globally continuous biorthogonal wavelet basis on the general domain is obtained. This construction does not uniquely define the basis functions but rather leaves some freedom for fulfilling additional features. In this paper we detail the general construction principle of the WEM to the 1D, 2D, and 3D cases. We address additional features such as symmetry, vanishing moments, and minimal support of the wavelet functions in each particular dimension. The construction is illustrated by using biorthogonal spline wavelets on the interval.

Efficient support for interactive service in multi-resolution VOD systems

Advances in high-speed networks and multimedia technologies have made it feasible to provide video-on-demand (VOD) services to users. However, it is still a challenging task to design a cost-effective VOD system that can support a large number of clients (who may have different quality of service (QoS) requirements) and, at the same time, provide different types of VCR functionalities. Although it has been recognized that VCR operations are important functionalities in providing VOD service, techniques proposed in the past for providing VCR operations may require additional system resources, such as extra disk I/O, additional buffer space, as well as network bandwidth. In this paper, we consider the design of a VOD storage server that has the following features: (1) provision of different levels of display resolutions to users who have different QoS requirements, (2) provision of different types of VCR functionalities, such as fast forward and rewind, without imposing additional demand on the system buffer space, I/O bandwidth, and network bandwidth, and (3) guarantees of the load-balancing property across all disks during normal and VCR display periods. The above-mentioned features are especially important because they simplify the design of the buffer space, I/O, and network resource allocation policies of the VOD storage system. The load-balancing property also ensures that no single disk will be the bottleneck of the system. In this paper, we propose data block placement, admission control, and I/O-scheduling algorithms, as well as determine the corresponding buffer space requirements of the proposed VOD storage system. We show that the proposed VOD system can provide VCR and multi-resolution services to the viewing clients and at the same time maintain the load-balancing property.

A simple edge-preserving filtering technique for constructing multi-resolution systems of images

We propose a simple edge-preserving filtering technique for constructing multi-resolution systems of images. The mask function of our filter is defined to be the product of a Gaussian function and the inverse function of the absolute value of the differences between neighboring pixels. The Gaussian function accounts for the spatial correlation of the pixels, while the inverse function is used to preserve the edge features in the image. In order to obtain reliable difference values, the image is pre-filtered before the construction of the filter mask. Our filtering method has been shown to have better edge-preserving and noise-removal ability than other three kinds of filters. Furthermore, since it is not an iterative process, the filtering can be processed very fast.

Quasi-cubes

A data cube is a popular organization for summary data. A cube is simply a multidimensional structure that contains at each point an aggregate value, i.e., the result of applying an aggregate function to an underlying relation. In practical situations, cubes can require a large amount of storage. The typical approach to reducing storage cost is to materialize parts of the cube on demand. Unfortunately, this lazy evaluation can be a time-consuming operation. In this paper, we describe an approximation technique that reduces the storage cost of the cube without incurring the run time cost of lazy evaluation. The idea is to provide an incomplete description of the cube and a method of estimating the missing entries with a certain level of accuracy. The description, of course, should take a fraction of the space of the full cube and the estimation procedure should be faster than computing the data from the underlying relations. Since cubes are used to support data analysis and analysts are rarely interested in the precise values of the aggregates (but rather in trends), providing approximate answers is, in most cases, a satisfactory compromise. Alternatively, the technique can be used to implement a multiresolution system in which a tradeoff is established between the execution time of queries and the errors the user is willing to tolerate. By only going to the disk when it is necessary (to reduce the errors), the query can be executed faster. This idea can be extended to produce a system that incrementally increases the accuracy of the answer while the user is looking at it, supporting on-line aggregation.

Face recognition system using local autocorrelations and multiscale integration

In this paper we investigate the performance of a technique for face recognition based on the computation of 25 local autocorrelation coefficients. We use a large database of 11,600 frontal facial images of 116 persons, organized in training and test sets, for evaluation. Autocorrelation coefficients are computationally inexpensive, inherently shift-invariant and quite robust against changes in facial expression. We focus on the difficult problem of recognizing a large number of known human faces while rejecting other, unknown faces which lie quite close in pattern space. A multiresolution system achieves a recognition rate of 95%, while falsely accepting only 1.5% of unknown faces. It operates at a speed of about one face per second. Without rejection of unknown faces, we obtain a peak recognition rate of 99.9%. The good performance indicates that local autocorrelation coefficients have a surprisingly high information content.

Hierarchical time-extended Petri nets (H-EPNs) based error identification and recovery for multilevel systems

In this paper, hierarchical time-extended Petri nets (H-EPNs), an extended Petri net based modeling and analysis tool, are used to derive the coordination level model of hierarchically decomposable systems, viewed from a three-level hierarchical structure of organization, coordination and execution of tasks. A two-layer (vertical) coordination level framework, consisting of the dispatcher/analyzer and the H-EPN controller is presented. A detailed two sub-level (horizontal) H-EPN controller model is derived to model system operations (including system soft failures). Error classification based on the interaction between the various system coordinators is derived from the H-EPN model. The H-EPN approach preserves multi-resolutional system details as well as effective communication flows between the various subsystems. A simple example illustrates the proposed approach. The important H-EPN properties of boundedness, safeness and reversibility are verified.

Nonlinear scale-space filtering and multiresolution system

We derive and demonstrate a nonlinear scale-space filter and its application in generating a nonlinear multiresolution system. For each datum in a signal, a neighborhood of weighted data is used for clustering. The cluster center becomes the filter output. The filter is governed by a single scale parameter that dictates the spatial extent of nearby data used for clustering. This, together with the local characteristic of the signal, determines the scale parameter in the output space, which dictates the influences of these data on the output. This filter is thus adaptive and data driven. It provides a mechanism for (a) removing impulsive noise, (b) improved smoothing of nonimpulsive noise, and (c) preserving edges. Comparisons with Gaussian scale-space filtering and median filters are made using real images. Using the architecture of the Laplacian pyramid and this nonlinear filter for interpolation, we construct a nonlinear multiresolution system that has two features: (1) edges are well preserved at low resolutions, and (2) difference signals are small and spatially localized. This filter implicitly presents a new mechanism for detecting discontinuities differing from techniques based on local gradients and line processes. This work shows that scale-space filtering, nonlinear filtering, and scale-space clustering are closely related and provides a framework within which further image processing, image coding, and computer vision problems can be investigated.

Integrating vision modules: stereo, shading, grouping, and line labeling

It is generally agreed that individual visual cues are fallible and often ambiguous. This has generated a lot of interest in design of integrated vision systems which are expected to give a reliable performance in practical situations. The design of such systems is challenging since each vision module works under a different and possibly conflicting set of assumptions. We have proposed and implemented a multiresolution system which integrates perceptual organization (grouping), segmentation, stereo, shape from shading, and line labeling modules. We demonstrate the efficacy of our approach using images of several different realistic scenes. The output of the integrated system is shown to be insensitive to the constraints imposed by the individual modules. The numerical accuracy of the recovered depth is assessed in case of synthetically generated data. Finally, we have qualitatively evaluated our approach by reconstructing geons from the depth data obtained from the integrated system. These results indicate that integrated vision systems are likely to produce better reconstruction of the input scene than the individual modules. >

TRICLOPS: A tool for studying active vision

The design, performance, and application of The Real-time, Intelligently ControLled, Optical Positioning System (TRICLOPS) are described in this article. TRICLOPS is a multiresolution trinocular camera-pointing system which provides a center wide-angle view camera and two higher-resolution vergence cameras. It is a direct-drive system that exhibits dynamic performance comparable to the human visual system. The mechanical design and performance of various active vision systems are discussed and compared to those of TRICLOPS. The multiprocessor control system for TRICLOPS is described. The kinematics of the device are also discussed and calibration methods are given. Finally, as an example of real-time visual control, a problem in visual tracking with TRICLOPS is examined. In this example, TRICLOPS is shown to be capable of tracking a ball moving at 3 m/s, which results in rotational velocities of the vergence cameras in excess of 6 rad/s (344 deg/s).

Learning texture discrimination rules in a multiresolution system

We describe a texture analysis system in which informative discrimination rules are learned from a multiresolution representation of time textured input. The system incorporates unsupervised and supervised learning via statistical machine learning and rule-based neural networks, respectively. The textured input is represented in the frequency-orientation space via a log-Gabor pyramidal decomposition. In the unsupervised learning stage a statistical clustering scheme is used for the quantization of the feature-vector attributes. A supervised stage follows in which labeling of the textured map is achieved using a rule-based network. Simulation results for the texture classification task are given. An application of the system to real-world problems is demonstrated. >

Multiresolution systems for video coding

Multiresolution systems for video coding

Combined multiresolution source coding and modulation for digital broadcast of HDTV

A practical end-to-end all-digital multiresolution system is demonstrated that employs joint source-channel coding and modulation in order to achieve efficient broadcast of digital HDTV. The threshold effect plaguing single resolution systems is softened by a stepwise graceful degradation. This can be used to increase the coverage and robustness of the digital broadcast system. This approach is seen as an alternative to traditional single resolution digital transmission systems which are not designed for broadcast situations, and which suffer from the threshold effect. This paper highlights the benefits of using an embedded multiresolution modulation constellation over a modulation scheme that resorts to time or frequency multiplexing of the broadcast resolutions. Besides showing coding results and simulations of transmission effects, the paper discusses the trade-offs between low and high resolution coverage.

Extensions of scale-space filtering to machine-sensing systems

Major components of scale-space theory are Gaussian filtering, and the use of zero-crossing thresholders and Laplacian operators. Properties of scale-space filtering may be useful for data analysis in multiresolution machine-sensing systems. However, these systems typically violate the Gaussian filter assumption, and often the data analyses used (e.g. trend analysis and classification) are not consistent with zero-crossing thresholders and Laplacian operators. The authors extend the results of scale-space theory to include these more general conditions. In particular, it is shown that relaxing the requirement of linear scaling allows filters to have non-Gaussian spatial characteristics, and that relaxing of the scale requirements (s to 0) of the impulse response allows the use of scale-space filters with limited frequency support (i.e. bandlimited filters). Bandlimited scale-space filters represent an important extension of scale-space analysis for machine sensing. >

Radio spectrographs for observing the planet Jupiter

A variety of techniques and instruments have been developed at the University of Florida to study the decametric radio emissions from the planet Jupiter. Several types of radio spectrograph are being utilized, each offering a unique advantage in speed, resolution or frequency coverage. The individual instruments, as well as their integration into a multiresolution system, are discussed. Representative data are presented.