Multiresolution Rotation-Invariant Texture Classification Using Feature Extraction in the Frequency Domain and Vector Quantization

Lillo, Antonella Di; Motta, Giovanni; Storer, James A.

doi:10.1109/dcc.2008.108

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…Therefore, they often produces better features that leads to a higher accuracy despite being more complex and slower. Some other transforms that were used includes the curvelet transform [10,18,[47][48][49] and the Wavelets based Dynamic Texture classification using Gumble Distribution [11] as well as a few other transforms that were less popularly used. E.g.…”

Section: Wavelets Methodsmentioning

confidence: 99%

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A Review of Recent Texture Classification: Methods.

2013

IOSR-JCE

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Section: Wavelets Methodsmentioning

confidence: 99%

“…Other classifiers are also used for texture classification but has yet to be popular in the recent years, e, g. the Bayes classifier [59,71], Learning Vector Quantization (LVQ) [47] and Hidden Markov Model (HMM) [72].…”

Section: Other Classifiersmentioning

confidence: 99%

A Review of Recent Texture Classification: Methods.

2013

IOSR-JCE

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“…One major difference between how FPFT is used on textures [3] and how it's used here in the RBRC algorithm is that in the case of textured images, features were extracted on a pixel-bypixel basis. In the case of a shape, the FPFT is computed once for the whole input image.…”

Section: The Rbrc Algorithmmentioning

confidence: 99%

“…In a previous work [3], we have introduced FPFT, an algorithm to perform texture classification and segmentation by extracting features that are invariant to certain geometric transformations. FPFT was tested by using benchmarks from the Outex database [11], a reference database containing a large collection of problems composed of synthetic and natural textured images presenting various naturally occurring transformations, such as rotation, scaling, and translation.…”

Section: Introductionmentioning

confidence: 99%

Shape recognition, with applications to a passive assistant

Lillo

Motta

Thomas

et al. 2010

Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments

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We address the problem of retrieving an object description, in the form of a silhouette, from a database of shapes, in a way that is robust with respect to translation, rotation, scale change, and distortion. Algorithms to solve this problem can be a key step in passive assistant software that receives information from available sensors and databases, and provides useful information about the local environment to a visually disabled user (where the assistant input may be in the form of audio). Experiments show significant gains in retrieval accuracy over previous work.

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“…One major difference between how FPFT is used on textures (Di Lillo, Motta, and Storer [16]) and how it is used here in the RBRC algorithm is that in the case of textured images, features were extracted on a pixel-by-pixel basis. In the case of a shape, the FPFT is computed once for the whole input image.…”

Section: The Rbrc Algorithmmentioning

confidence: 99%

Compression-Based Tools for Navigation with an Image Database

et al. 2012

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Abstract:We present tools that can be used within a larger system referred to as a passive assistant. The system receives information from a mobile device, as well as information from an image database such as Google Street View, and employs image processing to provide useful information about a local urban environment to a user who is visually impaired. The first stage acquires and computes accurate location information, the second stage performs texture and color analysis of a scene, and the third stage provides specific object recognition and navigation information. These second and third stages rely on compression-based tools (dimensionality reduction, vector quantization, and coding) that are enhanced by knowledge of (approximate) location of objects.

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Multiresolution Rotation-Invariant Texture Classification Using Feature Extraction in the Frequency Domain and Vector Quantization

Cited by 9 publications

References 17 publications

A Review of Recent Texture Classification: Methods.

A Review of Recent Texture Classification: Methods.

Shape recognition, with applications to a passive assistant

Compression-Based Tools for Navigation with an Image Database

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