Fuzzy set methods for object recognition in space applications

Progress on the following tasks is reported: (1) fuzzy set-based decision making methodologies; (2) feature calculation; (3) clustering for curve and surface fitting; and (4) acquisition of images. The general structure for networks based on fuzzy set connectives which are being used for information fusion and decision making in space applications is described. The structure and training techniques for such networks consisting of generalized means and gamma-operators are described. The use of other hybrid operators in multicriteria decision making is currently being examined. Numerous classical features on image regions such as gray level statistics, edge and curve primitives, texture measures from cooccurrance matrix, and size and shape parameters were implemented. Several fractal geometric features which may have a considerable impact on characterizing cluttered background, such as clouds, dense star patterns, or some planetary surfaces, were used. A new approach to a fuzzy C-shell algorithm is addressed. NASA personnel are in the process of acquiring suitable simulation data and hopefully videotaped actual shuttle imagery. Photographs have been digitized to use in the algorithms. Also, a model of the shuttle was assembled and a mechanism to orient this model in 3-D to digitize for experiments on pose estimation is being constructed

Possibility expectation and its decision making algorithm

The fuzzy integral has been shown to be an effective tool for the aggregation of evidence in decision making. Of primary importance in the development of a fuzzy integral pattern recognition algorithm is the choice (construction) of the measure which embodies the importance of subsets of sources of evidence. Sugeno fuzzy measures have received the most attention due to the recursive nature of the fabrication of the measure on nested sequences of subsets. Possibility measures exhibit an even simpler generation capability, but usually require that one of the sources of information possess complete credibility. In real applications, such normalization may not be possible, or even desirable. In this report, both the theory and a decision making algorithm for a variation of the fuzzy integral are presented. This integral is based on a possibility measure where it is not required that the measure of the universe be unity. A training algorithm for the possibility densities in a pattern recognition application is also presented with the results demonstrated on the shuttle-earth-space training and testing images

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise

Chemiluminescent Tags for Tracking Insect Movement in Darkness: Application to Moth Photo-Orientation

The flight tracks of Manduca sexta (Lepidoptera: Sphingidae) flying toward a 5 watt incandescent light bulb were recorded under low light conditions with the aid of a camera-mounted photomultiplier and a glowing marker technique. Small felt pads bearing a chemiluminescent (glowi ma­erial, Cyalume®, were affixed to the abdomens of free-flying moths. insects orienting to a dim incandescent bulb were easily visible to the naked eye and were clearly captured on videotape. On their initial approach to the light source, M. sexta were found to orient at a mean angle of -0.220 ± 2.70 (mean ± SEM). The speed of the initial approach flight (OA ± 0.03 m/s) was significantly faster than the speed immediately after passing the light (0.29 ± 0.02 m/s; t =6.4, PM. sexta initially fly approximately at a light source and only after passing it, do they engage in circular flight around the source. M. sexta flight to lights does not entirely match any paths predicted by several light orientation mechanisms, including the commonly invoked light compass theory

Openness and industrial response in a Wal-Mart world : a case study of Mexican soaps, detergents, and surfactant producers

This paper uses a case study approach to explore the effects of NAFTA and GATT membership on innovation and trade in the Mexican soaps, detergents, and surfactants (SDS) industry. Several basic findings emerge. First, the most fundamental effect of the NAFTA and the GATT on the SDS industry was to help induce Wal-Mart to enter Mexico. Once there, Walmex fundamentally changed the retail sector, forcing SDS firms to cut their profit margins and innovate. Those unable to respond to this new environment tended to lose market share and, in some cases, disappear altogether. Second, partly in response to Walmex, many Mexican producers logged impressive efficiency gains during the previous decade. These gains came both from labor-shedding and from innovation, which in turn was fueled by innovative input suppliers and by multinationals bringing new products and processes from their headquarters to Mexico. Finally, although Mexican detergent exports captured an increasing share of the U.S. detergent market over the past decade, Mexican sales in the U.S. were inhibited by a combination of excessive shipping delays at the border and artificially high input prices (due to Mexican protection of domestic caustic soda suppliers). They were also held back by the major re-tooling costs that Mexican producers would have had to incur to establish brand recognition among non-Latin consumers and to comply with zero phosphate laws in many regions of the U.S.Markets and Market Access,Transport Economics Policy&Planning,Access to Markets,Economic Theory&Research,Water and Industry

Quantitative analysis of properties and spatial relations of fuzzy image regions

Properties of objects and spatial relations between objects play an important role in rule-based approaches for high-level vision. The partial presence or absence of such properties and relationships can supply both positive and negative evidence for region labeling hypotheses. Similarly, fuzzy labeling of a region can generate new hypotheses pertaining to the properties of the region, its relation to the neighboring regions, and finally, the labels of the neighboring regions. In this paper, we present a unified methodology to characterize properties and spatial relationships of object regions in a digital image. The proposed methods can be used to arrive at more meaningful decisions about the contents of the scene

Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy