Search results for "pattern recognition"
showing 10 items of 2301 documents
Nonlinear radial-harmonic correlation using binary decomposition for scale-invariant pattern recognition
2003
We introduce a new scale-invariant pattern-recognition method that uses nonlinear correlation. We applied several common linear correlations to images decomposed into disjoint binary images, which is very discriminant even when the target is embedded in strong noise. We combine our sliced orthogonal nonlinear generalized correlation method and the radial-harmonic expansion in order to achieve scale-invariant pattern recognition. The information from a radial harmonic for each binary slice of the reference object is combined with binary slices of the target. The method avoids the time-consuming process of finding expansion centers for the radial harmonics. The stability of the correlation pe…
Fuzzy temporal random sets with an application to cell biology
2007
Total Internal Reflection Fluorescence Microscopy (TIRFM) greatly facilitates to imaging the first steps of endocytosis, a process whereby cells traffic cargo from the cell surface to endosomes. Using TIRFM, fluorescent-tagged endocytic proteins are observed as overlapped areas forming random clumps of different sizes, shapes and durations. A common procedure to segment these objects consists of thresholding the original gray-level images to produce binary sequences in which a pixel is covered or not by a given fluorescent-tagged protein. This binary logic is not appropriate because it leaves a free tuning parameter to be set by the user which can influence on the conclusions of the statist…
Parallel distance transforms on pyramid machines: Theory and implementation
1990
Abstract A distance transform of a binary image is an array each of whose elements gives the distance from the corresponding pixel to the closest ‘1’ in the binary image. Distance transforms have uses in image matching and shape analysis, among other applications. We present a parallel algorithm for weighted distance transforms that runs particularly efficiently on hierarchical cellular-logic machines, a subclass of the architectures known as pyramid machines. The algorithm computes the 3–4 distance transform; however it can be readily adapted to the city-block (‘Manhattan’) and chessboard distance measures. The algorithm runs in O(M) time, for an M × M image. Since it avoids using arithmet…
Improved rotation invariant pattern recognition using circular harmonics of binary gray level slices
2000
We introduce a new rotation invariant pattern recognition method based on nonlinear correlation. The images are decomposed into disjoint binary slices and then correlated using the common linear correlation. This operation is very discriminant even when the target is embedded in strong noise. We extend our sliced orthogonal nonlinear generalized correlation method to rotation invariant pattern recognition by combining the information of a circular harmonic (CH) of each binary slice of the reference object with binary slices of the target. In addition to improved discrimination capability, the method avoids the time-consuming process of finding proper centers for the CHs. Results are present…
Psychophysical response to electrocutaneous stimulation.
1984
A method is presented to determine a reliable stimulus-sensation relationship particularly suitable for electrocutaneous stimulation. An experimental intensity-discrimination curve was obtained through simple psychophysical comparison tasks, and sensory response was inferred from integration of a JND's density function. The psychophysical response resembles a power law, although departures cannot be described in terms of a unique exponent. An estimate of binary information capacity per electrode is also given as a feature of a stimulation procedure that preserves a low value of the size-intensity product.
Nonlinear data description with Principal Polynomial Analysis
2012
Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Performance of PCA is however hampered when data exhibits nonlinear feature relations. In this work, we propose a new framework for manifold learning based on the use of a sequence of Principal Polynomials that capture the eventually nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) is shown to generalize PCA. Unlike recently proposed nonlinear methods (e.g. spectral/kernel methods and projection pursuit techniques, neural networks), PPA features are easily interpretable and the method leads to a fully invertible transform, which is a desirable property…
Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap
2015
This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, so…
A principled approach to network-based classification and data representation
2013
Measures of similarity are fundamental in pattern recognition and data mining. Typically the Euclidean metric is used in this context, weighting all variables equally and therefore assuming equal relevance, which is very rare in real applications. In contrast, given an estimate of a conditional density function, the Fisher information calculated in primary data space implicitly measures the relevance of variables in a principled way by reference to auxiliary data such as class labels. This paper proposes a framework that uses a distance metric based on Fisher information to construct similarity networks that achieve a more informative and principled representation of data. The framework ena…
Spectral clustering with the probabilistic cluster kernel
2015
Abstract This letter introduces a probabilistic cluster kernel for data clustering. The proposed kernel is computed with the composition of dot products between the posterior probabilities obtained via GMM clustering. The kernel is directly learned from the data, is parameter-free, and captures the data manifold structure at different scales. The projections in the kernel space induced by this kernel are useful for general feature extraction purposes and are here exploited in spectral clustering with the canonical k-means. The kernel structure, informative content and optimality are studied. Analysis and performance are illustrated in several real datasets.
Fuzzy sigmoid kernel for support vector classifiers
2004
This Letter proposes the use of the fuzzy sigmoid function presented in (IEEE Trans. Neural Networks 14(6) (2003) 1576) as non-positive semi-definite kernel in the support vector machines framework. The fuzzy sigmoid kernel allows lower computational cost, and higher rate of positive eigenvalues of the kernel matrix, which alleviates current limitations of the sigmoid kernel.