4,363 research outputs found
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
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
Fuzzy Set Methods for Object Recognition in Space Applications
Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms
Genome-wide linkage analyses identify Hfhl1 and Hfhl3 with frequency-specific effects on the hearing spectrum of NIH Swiss mice
BACKGROUND: The mammalian cochlea receives and analyzes sound at specific places along the cochlea coil, commonly referred to as the tonotopic map. Although much is known about the cell-level molecular defects responsible for severe hearing loss, the genetics responsible for less severe and frequency-specific hearing loss remains unclear. We recently identified quantitative trait loci (QTLs) Hfhl1 and Hfhl2 that affect high-frequency hearing loss in NIH Swiss mice. Here we used 2f1-f2 distortion product otoacoustic emissions (DPOAE) measurements to refine the hearing loss phenotype. We crossed the high frequency hearing loss (HFHL) line of NIH Swiss mice to three different inbred strains and performed linkage analysis on the DPOAE data obtained from the second-generation populations. RESULTS: We identified a QTL of moderate effect on chromosome 7 that affected 2f1-f2 emissions intensities (Hfhl1), confirming the results of our previous study that used auditory brainstem response (ABR) thresholds to identify QTLs affecting HFHL. We also identified a novel significant QTL on chromosome 9 (Hfhl3) with moderate effects on 2f1-f2 emissions intensities. By partitioning the DPOAE data into frequency subsets, we determined that Hfhl1 and Hfhl3 affect hearing primarily at frequencies above 24 kHz and 35 kHz, respectively. Furthermore, we uncovered additional QTLs with small effects on isolated portions of the DPOAE spectrum. CONCLUSIONS: This study identifies QTLs with effects that are isolated to limited portions of the frequency map. Our results support the hypothesis that frequency-specific hearing loss results from variation in gene activity along the cochlear partition and suggest a strategy for creating a map of cochlear genes that influence differences in hearing sensitivity and/or vulnerability in restricted portions of the cochlea
Star formation and ISM morphology in tidally induced spiral structures
Tidal encounters are believed to be one of the key drivers of galactic spiral
structure in the Universe. Such spirals are expected to produce different
morphological and kinematic features compared to density wave and dynamic
spiral arms. In this work we present high resolution simulations of a tidal
encounter of a small mass companion with a disc galaxy. Included are the
effects of gas cooling and heating, star formation and stellar feedback. The
structure of the perturbed disc differs greatly from the isolated galaxy,
showing clear spiral features that act as sites of new star formation, and
displaying interarm spurs. The two arms of the galaxy, the bridge and tail,
appear to behave differently; with different star formation histories and
structure. Specific attention is focused on offsets between gas and stellar
spiral features which can be directly compared to observations. We find some
offsets do exist between different media, with gaseous arms appearing mostly on
the convex side of the stellar arms, though the exact locations appear highly
time dependent. These results further highlight the differences between tidal
spirals and other theories of arm structure.Comment: 17 pages, 19 colour figures, accepted for publication in MNRA
Resident Identification using Kinect Depth Image Data and Fuzzy Clustering Techniques
As a part of our passive fall risk assessment research in home environments, we present a method to identify older residents using features extracted from their gait information from a single depth camera. Depth images have been collected continuously for about eight months from several apartments at a senior housing facility. Shape descriptors such as bounding box information and image moments were extracted from silhouettes of the depth images. The features were then clustered using Possibilistic C Means for resident identification. This technology will allow researchers and health professionals to gather more information on the individual residents by filtering out data belonging to non-residents. Gait related information belonging exclusively to the older residents can then be gathered. The data can potentially help detect changes in gait patterns which can be used to analyze fall risk for elderly residents by passively observing them in their home environments
Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks
Information fusion is an essential part of numerous engineering systems and
biological functions, e.g., human cognition. Fusion occurs at many levels,
ranging from the low-level combination of signals to the high-level aggregation
of heterogeneous decision-making processes. While the last decade has witnessed
an explosion of research in deep learning, fusion in neural networks has not
observed the same revolution. Specifically, most neural fusion approaches are
ad hoc, are not understood, are distributed versus localized, and/or
explainability is low (if present at all). Herein, we prove that the fuzzy
Choquet integral (ChI), a powerful nonlinear aggregation function, can be
represented as a multi-layer network, referred to hereafter as ChIMP. We also
put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient
descent-based optimization in light of the exponential number of ChI inequality
constraints. An additional benefit of ChIMP/iChIMP is that it enables
eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP
is applied to the fusion of a set of heterogeneous architecture deep models in
remote sensing. We show an improvement in model accuracy and our previously
established XAI indices shed light on the quality of our data, model, and its
decisions.Comment: IEEE Transactions on Fuzzy System
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