Pyramidal Approach Research Articles

Fast Algorithms for Area Morphology

Acton, S. T., Fast Algorithms for Area Morphology, Digital Signal Processing 11 (2001) 187–203 Efficient algorithms are developed for area morphology. As opposed to traditional morphological operations that alter grayscale images via a concatenation of order statistic filters, the area morphological operators manipulate connected components within the image level sets. Essentially, the area morphology filters are capable of removing objects based on the object area solely. These operators can then be effectively used in important multiscale and scale space tasks such as object-based coding and hierarchical image searches. Unfortunately, the traditional implementation of these filters based on level set theory precludes real-time implementation. This paper reviews previous fast algorithms and introduces a pyramidal approach. The full pyramidal algorithm is over 1000 times faster than the standard algorithm for typical image sizes. The paper provides supporting simulation results in terms of computational complexity and solution quality.

A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain MRI

Quantitative assessment of Magnetic Resonance Imaging (MRI) lesion load of patients with multiple sclerosis (MS) is the most objective approach for a better understanding of the history of the pathology, either natural or modified by therapies. To achieve an accurate and reproducible quantification of MS lesions in conventional brain MRI, an automatic segmentation algorithm based on a multiresolution approach using pyramidal data structures is proposed. The systematic pyramidal decomposition in the frequency domain provides a robust and flexible low level tool for MR image analysis. Context-dependent rules regarding MRI findings in MS are used as high level considerations for automatic lesion detection.

Pyramidal piecewise uniform lattice quantization in the wavelet transform domain for image coding

The wavelet transform, which provides a multiresolution representation of images, has been widely used in image compression. A new image coding scheme using the wavelet transform and lattice vector quantization is presented. The input image is first decomposed into a hierarchy of three layers containing 10 subimages by discrete wavelet transform. The lowest resolution low-frequency subimage is scalar quantized with 8 bits/pixel. High-frequency subimages are encoded by lattice vector quantization. A pyramidal piecewise uniform companding approach is used to design the lattice quantizer according to a piecewise constant approximation to the probability density function of the input source. Due to the fast algorithm of lattice quantization, computational complexity is greatly reduced as compared to the vector quantizers based on the Linde-Buzo-Gray (LBG) algorithm. Computer simulations show that the proposed coding scheme can achieve a high compression ratio while maintaining good reconstruction image quality (both objectively and subjectively).

SHAPE DESCRIPTION BY A SYNTACTIC PYRAMIDAL APPROACH

In the syntactic approach to pattern recognition, patterns are represented as strings, where each pattern is expressed as a composition of its component patterns, called subpatterns and pattern primitives. This approach draws an analogy between the structure of patterns and the syntax of a language. The patterns are considered at a single resolution level, and the recognition of each pattern is usually made by parsing the pattern structure according to a given set of syntax rules, obtained in the first stage of the analysis. This paper describes a pyramidal approach to 2-D object representation which relies on a linguistic description of the object contour at different resolution levels. Our approach, which is parallel and context-sensitive, implies the use of higher dimensional grammars for defining production rules which describe the evolution of the contour between levels. This method is computationally efficient (due to the fast coarse-fine search), rotationally invariant, as well as rather insensitive to noise.

Pyramidal approach to license plate segmentation

Car identification is a goal in traffic control, transport planning, travel time measurement, managing parking lot traffic and so on. Most car identification algorithms contain a standalone plate segmentation process followed by a plate contents reading. A pyramidal algorithm for license plate segmentation, looking for textured regions, has been developed on a PC based system running Unix. It can be used directly in applications not requiring real time. When input images are relatively small, real-time performance is in fact accomplished by the algorithm. When using large images, porting the algorithm to special digital signal processors can easily lead to preserving real-time performance. Experimental results, for stationary and moving cars in outdoor scenes, showed high accuracy and high scores in detecting the plate. The algorithm also deals with cases where many character strings are present in the image, and not only the one corresponding to the plate. This is done by the means of a constrained texture regions classification.

Symbolic objects: order structure and pyramidal clustering

We recall a formalism based on the notion of symbolic object (Diday [15], Brito and Diday [8]), which allows to generalize the classical tabular model of Data Analysis. We study assertion objects, a particular class of symbolic objects which is endowed with a partial order and a quasi-order. Operations are then defined on symbolic objects. We study the property of completeness, already considered in Brito and Diday [8], which expresses the duality extension intension. We formalize this notion in the framework of the theory of Galois connections and study the order structure of complete assertion objects. We introduce the notion ofc-connection, as being a pair of mappings (f,g) between two partially ordered sets which should fulfil given conditions. A complete assertion object is then defined as a fixed point of the composedf o g; this mapping is called a “completeness operator” for it “completes” a given assertion object. The set of complete assertion objects forms a lattice and we state how suprema and infima are obtained. The lattice structure being too complex to allow a clustering study of a data set, we have proposed a pyramidal clustering approach [8]. The symbolic pyramidal clustering method builds a pyramid bottom-up, each cluster being described by a complete assertion object whose extension is the cluster itself. We thus obtain an inheritance structure on the data set. The inheritance structure then leads to the generation of rules.

Automated assembling of images: image montage preparation

Image montage (or mosaic) finds a great deal of applications in ophthalmology, digital terrain mapping, and in autonomous navigation of re-entry vehicles. However, there is no direct method available for image montage preparation. Image registration methods may be used to solve this problem, but most of these methods are not useful, because they require either specific feature points in images or a great deal of overlapping between images of neighboring regions. A normalized correlation method is used for the preparation of montages when there is no rotational shift between neighboring images. A pyramidal approach is developed to reduce the computation. Further, a new method is developed for montage preparation when there is also a rotational shift between two neighboring regions. Different sets of subimages, which have both rotational and translational shifts with the subimages of neighboring regions, are taken for preparing image montages. The proposed method does not require any human supervision and the results are found to be very accurate.

A Risk-Benefit Assessment of Slow-Acting Antirheumatic Drugs in Rheumatoid Arthritis

There is no ideal slow-acting antirheumatic drug. Therapy of rheumatoid arthritis (RA) is currently being modified, with strong recommendations to abandon the traditional pyramidal approach. The call is for a more aggressive, earlier approach to suppress inflammation. Combination therapy rather than the use of a single agent is advocated by some. Improved methods for assessing disease activity as well as measurement of outcome have been developed. Markers of poor prognosis have helped to define patients for earlier treatment. Comparison of toxicity among such a diverse group of drugs is probably best achieved with a toxicity index measuring the number of episodes expressed in terms of patient-years of exposure. Toxicity remains the commonest reason for discontinuing an agent, while remission beyond 36 months on therapy is uncommon, except with methotrexate. The profile of toxicity is clearly defined for individual agents, but combination therapy may reveal an entirely different set of toxic manifestations. There is an urgent need to develop a set of risk factors to predict toxicity in an individual patient. Juvenile chronic arthritis behaves differently from adult RA. Drug toxicity profiles are similar, but less common. Outcome is more difficult to measure, with the major impact of disease and therapy being on growth retardation.

Issues in Contemporary Drug Delivery

Objective To provide an overview of rheumatoid arthritis (RA) and the medications presently being used to treat the disease. Data Sources References have been selected from published articles, monographs, manufacturers’ product information, textbooks on therapeutics of rheumatic diseases, and specific computerized databases such as DRUGDEX. Study Selection Studies that establish the pharmacokinetics of the various drugs have been included, as have comparative studies of the various agents. Studies that gave specifics concerning the use of the various agents in the elderly were given priority in selection. Monographs from both United States Pharmacopeia Dispensing Information and American Hospital Formulary Service Drug Information ‘92 were used to ensure consistency in dosing and pharmacokinetic data. Data Synthesis A variety of medications are used to treat RA, and are classified according to their ability to alleviate the inflammatory process and symptomatology of the disease, or to actually slow down or halt the progression of the disease process itself. The long-established pyramidal approach to therapy is presently being challenged so as to employ the slow-acting or disease-modifying agents earlier in the process. Conclusions A wide variety of medications are presently available for treating RA. As the pathogenesis of the disease becomes better defined, it is hoped that less-toxic and more-specific and -effective therapies will become available. Medications remain the cornerstone of therapy; however, patient education, rest, and exercise are equally important.

An Optical Flow Algorithm Preserving Motion Boundaries and its Parallel Implementation

One of the main techniques for the analysis of image sequences is the optical flow computation. The optical flow is a vector field representing the apparent motion, on the image, of the projected points of the objects in the scene. Many algorithms have been developed to compute the optical flow. In this paper one of these algorithms is reviewed, the performance enhanced and a possible parallel implementation presented. The main advantage of the resulting algorithm is its good behavior in presence of noise or motion boundaries; this is obtained by means of a local technique which doesn't require global optimization and by applying a median filter to restore the vector field. Furthermore a pyramidal approach is used to increase the range of interframe displacements computable by the algorithm. To enhance the time performance a parallel implementation on a network of transputers has been studied; a theoretical analysis of the efficiency of the implemented architecture is discussed and its agreement with the experimental results is reported.

Token-textured object detection by pyramids

Synthetic textures, on which human vision is tested, are usually composed of tokens such as dots, lines, Ts or crosses that are easily visible to subject tested 1. Humans discriminate easily between textures that are statistical mixtures of grey-level dots, if they differ enough in their statistical parameters 2. Discriminating between textures that differ in higher moments of their distribution is hard to reconcile with the common pyramidal multiresolution approach. We propose a new kind of pyramid that achieves good discrimination of statistically distributed grey-level and token-textures: discrimination between distributions having equal means, that is impossible to achieve with the intensity pyramid paradigm. Textures composed of statistical mixtures of tokens other than dots were tested on human subjects and found to produce results similar to those of grey-level dots. To discriminate by machine between token-textures the pyramidal approach is combined with a paradigm for composing the responses of several standard detectors. Human performance was tested on the same set of textures.

A pyramidal approach for the recognition of neurons using key features

Pyramid-like hierarchical structures have been shown to be suitable for many computer vision tasks. In this paper a pyramidal approach for recognition of neurons from an electron micrograph is described. Key features for object recognition are defined. Transforms for extracting features are performed hierarchically in a simulated pyramid, cascading local parallel operations. The algorithms described in this paper are expected to be fairly expandable to general purpose pyramidal vision systems.

A pyramidal approach to parallel processing

This paper presents the architecture of a parallel computer called a pyramid machine. The system consists of a pyramidal array of processing elements, each of which executes the instructions broadcast by a controller. Each processing element except those on the outside of the array is directly connected to thirteen neighboring elements: eight on the same level, four on the next finer level and one on the next coarser level. The architecture combines features of tree machines and features of mesh-connected parallel computers. As a result it is able to rapidly perform computations of local and global processing. The main areas of application are image processing, graphics and spatial problem solving. The motivation, basic structure, and applications of the system are discussed.

Pyramidal training: a large-scale application with institutional staff.

This study evaluated an indirect method of training 45 institutional direct care staff to conduct behavioral programs. Three supervisors were trained to improve teaching behaviors (instructions, prompts, and consequence) used by the direct care staff while working with severely and profoundly handicapped residents. In addition to training, daily feedback was provided to supervisors regarding performance of their staff. Results of a multiple baseline analysis across teaching behaviors (instructions, prompts, and consequences) and content areas (communication and gross motor skills) showed that providing training and feedback to supervisors resulted in increases in correct teaching behavior by direct care staff. However, teaching behavior newly learned in one content area (communication) did not generalize to the other area (gross motor skills). Data collected on resident behavior showed small but noticeable improvement in terms of correct responses and attending behavior during programming. Results are discussed in terms of the benefits of a pyramidal approach to training institutional staff.