A new method for stereo matching problem in computer vision using synergetics

Müller

IEEE Trans. Geosci. Remote Sensing

2013

This paper presents a classification methodology for hyperspectral data based on synergetics theory. Pattern recognition algorithms based on synergetics have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analyzed independently. The spectrum is first projected in a space spanned by a set of user-defined prototype vectors, which belong to some classes of interest, and then attracted by a final state associated to a prototype. The spectrum can thus be classified, establishing a first attempt at performing a pixel-wise image classification using notions derived from synergetics. As typical synergetics-based systems have the drawback of a rigid training step, we introduce a new procedure which allows the selection of a training area for each class of interest, used to weight the prototype vectors through attention parameters and to produce a more accurate classification map through plurality vote of independent classifications. As each classification is in principle obtained on the basis of a single training sample per class, the proposed technique could be particularly effective in tasks where only a small training data set is available. The results presented are promising and often outperform state-of-the-art classification methodologies, both general and specific to hyperspectral data.

Section: Synergetics Theorymentioning

confidence: 99%

A Classification Algorithm for Hyperspectral Images Based on Synergetics Theory

Müller

IEEE Trans. Geosci. Remote Sensing

2013

“…Applications on image classification have been proposed, among others, in (Crounse and Chua, 1996, Hogg et al, 1998, Maeda et al, 1999. Such notions have been popular especially in the 90's, with following years witnessing an interest decrease, probably due to the rigid training step and the great dependance on scale, rotation and shift typical of such methods.…”

Section: Synergetics Theorymentioning

confidence: 99%

“…Haken shows in (Haken, 1991) that the order parameter with the highest value at time t = 0 is related to the prototype that will be chosen by the system as winning final state, while all others will eventually decay and assume a value of 0, if the attention parameters remain stable under certain limits. Based on these observations, many systems based on synergetics theory use approximations to avoid computing the full differential equations, usually by selecting the largest initial order parameter (Wang et al, 1993, ?, ?, ?, Crounse and Chua, 1996, Maeda et al, 1999. In this work we approximate then the synergetics equation 1 by its first term, which generates minima along the prototype vectors, only considered at time t = 0.…”

Section: Classification Of Hyperspectral Data Based On Synergetics Thmentioning

confidence: 99%

“…It has to be remarked that our approximation to the most similar base vector as chosen at t = 0 may now lead to misclassifications, as when the attention parameters are changed the winning vector can be different from v0. According to (Maeda et al, 1999), for the case of a two dimensional prototype space, no attention parameter should be set to more than twice the value of the other to ensure that a test vector is attracted by the final state corresponding to the prototype vector of highest potential at t = 0.…”

Section: Classification Of Hyperspectral Data Based On Synergetics Thmentioning

confidence: 99%

See 1 more Smart Citation

A Classification Algorithm for Hyperspectral Data Based on Synergetics Theory

Mueller

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

2012

ABSTRACT:This paper presents a new classification methodology for hyperspectral data based on synergetics theory, which describes the spontaneous formation of patterns and structures in a system through self-organization. We introduce a representation for hyperspectral data, in which a spectrum can be projected in a space spanned by a set of user-defined prototype vectors, which belong to some classes of interest. Each test vector is attracted by a final state associated to a prototype, and can be thus classified. As typical synergetics-based systems have the drawback of a rigid training step, we modify it to allow the selection of user-defined training areas, used to weight the prototype vectors through attention parameters and to produce a more accurate classification map through majority voting of independent classifications. Results are comparable to state of the art classification methodologies, both general and specific to hyperspectral data and, as each classification is based on a single training sample per class, the proposed technique would be particularly effective in tasks where only a small training dataset is available.

“…Pattern recognition algorithms based on synergetics theory have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images (Fuchs et al 1988, Haken 1988a, Haken 1988b, Hogg et al 1998, Kawano at al. 2006, Boebel et al 1994, Maeda et al 1999, Zhao et al 2003. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analysed independently.…”

Section: Introductionmentioning

confidence: 99%

Synergetics Framework for Hyperspectral Image Classification

Müller

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

2013

ABSTRACT:In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics -originally introduced by the physicist H. Haken -is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ecology, chemistry, cosmology, and thermodynamics up to sociology. Although this theory describes general rules for pattern formation it was linked also to pattern recognition. Pattern recognition algorithms based on synergetics theory have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analysed independently. The classification scheme based on synergetics introduces also methods for spatial regularization to get rid of "salt and pepper" classification results and for iterative parameter tuning to optimize class weights. The paper reports an experiment on a benchmark data set frequently used for method comparisons. This data set consists of a hyperspectral scene acquired by the Airborne Visible Infrared Imaging Spectrometer AVIRIS sensor of the Jet Propulsion Laboratory acquired over the Salinas Valley in CA, USA, with 15 vegetation classes. The results are compared to state-of-the-art methodologies like Support Vector Machines (SVM), Spectral Information Divergence (SID), Neural Networks, Logistic Regression, Factor Graphs or Spectral Angle Mapper (SAM). The outcomes are promising and often outperform state-of-the-art classification methodologies.