Noisy texture classification: A higher-order statistics approach

Murino, Vittorio; Ottonello, C.; Pagnan, S.

doi:10.1016/s0031-3203(97)00055-1

Cited by 31 publications

(12 citation statements)

References 16 publications

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“…(b) Textured segmentation using artificial neural network [29,30]. (c) Statistical texture segmentation [31,32]. (d) Texture segmentation using filters [33,34].…”

Section: A Review Of Image Segmentation Techniquesmentioning

confidence: 99%

Simulating autonomous agents in augmented reality

Gelenbe¹,

Hussain²,

Kaptan³

2005

Journal of Systems and Software

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In many critical applications such as airport operations (for capacity planning), military simulations (for tactical training and planning), and medical simulations (for the planning of medical treatment and surgical operations), it is very useful to conduct simulations within physically accurate and visually realistic settings that are represented by real video imaging sequences. Furthermore, it is important that the simulated entities conduct autonomous actions which are realistic and which follow plans of action or intelligent behavior in reaction to current situations. We describe the research we have conducted to incorporate synthetic objects in a visually realistic manner in video sequences representing a real scene. We also discuss how the synthetic objects can be designed to conduct intelligent behavior within an augmented reality setting. The paper discusses both the computer vision aspects that we have addressed and solved, and the issues related to the insertion of intelligent autonomous objects within an augmented reality simulation.

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“…(b) Textured segmentation using artificial neural network [29,30]. (c) Statistical texture segmentation [31,32]. (d) Texture segmentation using filters [33,34].…”

Section: A Review Of Image Segmentation Techniquesmentioning

confidence: 99%

Simulating autonomous agents in augmented reality

Gelenbe¹,

Hussain²,

Kaptan³

2005

Journal of Systems and Software

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“…Good surveys can be found in [5][6][7][8][9][10][11]. The conjecture presented in [12], where second-order probability distributions [6,13] are enough for human discrimination of two texture patterns, has motivated the use of statistical approaches. This conjecture showed not to hold strictly particularities when textures present some structure [14].…”

Section: Introductionmentioning

confidence: 99%

Color texture analysis based on fractal descriptors

Backes

Casanova

Bruno

2012

Pattern Recognition

156

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“…This pixel distribution depends on the characteristics of the material, such as reflectance and homogeneity, which constitutes the surface of object. Given the importance of the texture, many approaches have been developed along the years: second-order statistics [3,4], spectral analysis [5][6][7][8][9][10][11], wavelet packets [12,13] and fractals [14,15].…”

Section: Introductionmentioning

confidence: 99%

Plant Leaf Identification Using Color and Multi-scale Fractal Dimension

Backes

Bruno

2010

Lecture Notes in Computer Science

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The most extracted measures from plants are traditionally performed manually. This and the great biodiversity of species makes the taxonomical classification of plants a very complex and time-consuming task. In order to contribute to the plant species characterization, a novel approach based on color texture is proposed. Each color channel in a leaf texture is modeled as a surface, so the complexity in each channel, as well as in the interaction among these three color channels, can be measured using the Multi-scale fractal dimension. Results show the potential of this approach, which overcomes traditional color texture analysis methods, such as Chromaticity Moments and Gabor EEE descriptors.

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Noisy texture classification: A higher-order statistics approach

Cited by 31 publications

References 16 publications

Simulating autonomous agents in augmented reality

Simulating autonomous agents in augmented reality

Color texture analysis based on fractal descriptors

Plant Leaf Identification Using Color and Multi-scale Fractal Dimension

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