Sensor band selection for multispectral imaging via average normalized information

Wang, Hongzhi; Angelopoulou, Elli

doi:10.1007/s11554-006-0014-9

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…In the future, a fusion at the image and feature levels should be investigated based on the iris images that are simultaneously acquired, and this has attracted the attention of some researchers [26][27][28][29][30][31]. More effective enhancement techniques for image quality may be explored to improve the performance of multispectral iris recognition in large datasets [32].…”

Section: Discussionmentioning

confidence: 99%

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Optimal wavelength band clustering for multispectral iris recognition

et al. 2012

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This work explores the possibility of clustering spectral wavelengths based on the maximum dissimilarity of iris textures. The eventual goal is to determine how many bands of spectral wavelengths will be enough for iris multispectral fusion and to find these bands that will provide higher performance of iris multispectral recognition. A multispectral acquisition system was first designed for imaging the iris at narrow spectral bands in the range of 420 to 940 nm. Next, a set of 60 human iris images that correspond to the right and left eyes of 30 different subjects were acquired for an analysis. Finally, we determined that 3 clusters were enough to represent the 10 feature bands of spectral wavelengths using the agglomerative clustering based on two-dimensional principal component analysis. The experimental results suggest (1) the number, center, and composition of clusters of spectral wavelengths and (2) the higher performance of iris multispectral recognition based on a three wavelengths-bands fusion.

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Section: Discussionmentioning

confidence: 99%

“…To find the optimal combination for a given number of wavelength clusters, many algorithms could be used, such as divergence [20], mutual information [21], and entropy [22]. Here, an exhaustive search was implemented as it decreased the possibility of missing any optimal combinations.…”

Section: Performance Analysismentioning

confidence: 99%

Optimal wavelength band clustering for multispectral iris recognition

et al. 2012

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“…Most of the methods usually involve two separate tasks: (a) selecting the bands that can indicate the particular material well, feature bands selection; and (b) removing the feature bands contributing redundant information, redundancy reduction [41]. Recently, Information theory has also been used in feature bands selection [42,43], it consists of analyzing the amount of information in a subset of features (bands), measuring the degree of independence between image bands as a relevance criterion.…”

Section: Introductionmentioning

confidence: 99%

Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images

Khelifi

Adel

Bourennane

2012

EURASIP J. Adv. Signal Process.

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Various approaches have been proposed in the literature for texture characterization of images. Some of them are based on statistical properties, others on fractal measures and some more on multi-resolution analysis. Basically, these approaches have been applied on mono-band images. However, most of them have been extended by including the additional information between spectral bands to deal with multi-band texture images. In this article, we investigate the problem of texture characterization for multi-band images. Therefore, we aim to add spectral information to classical texture analysis methods that only treat gray-level spatial variations. To achieve this goal, we propose a spatial and spectral gray level dependence method (SSGLDM) in order to extend the concept of gray level co-occurrence matrix (GLCM) by assuming the presence of texture joint information between spectral bands. Thus, we propose new multi-dimensional functions for estimating the second-order joint conditional probability density of spectral vectors. Theses functions can be represented in structure form which can help us to compute the occurrences while keeping the corresponding components of spectral vectors. In addition, new texture features measurements related to (SSGLDM) which define the multi-spectral image properties are proposed. Extensive experiments have been carried out on 624 textured multi-spectral images for use in prostate cancer diagnosis and quantitative results showed the efficiency of this method compared to the GLCM. The results indicate a significant improvement in terms of global accuracy rate. Thus, the proposed approach can provide clinically useful information for discriminating pathological tissue from healthy tissue.

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“…To reduce the amount of unneeded data, various approaches have been proposed for transmitting data in selected bands only for further processing after the data acquisition. Algorithms for automated band selection having been proposed based on minimizing reconstruction error using PCA or ICA [1], or minimizing classification error using Fisher Discriminant Analysis or Mutual Information [2]. Another approach is to acquire data only at spectral bands informative to the task at hand.…”

Section: Introductionmentioning

confidence: 99%