A Review of Recent Texture Segmentation and Feature Extraction Techniques

Reed, T.R.; Buf, J. M. H. du

doi:10.1006/ciun.1993.1024

Cited by 513 publications

(144 citation statements)

References 0 publications

Supporting

Mentioning

135

Contrasting

Unclassified

Order By: Relevance

“…We note that there are numerous image representation schemes in the computer vision literature. [24][25][26][27][28][29][30] Our motivation for choosing the SIFT-VLAD system (as opposed to, for example, convolutional neural network-based texture representations 31 ) is its strong rotation and scale invariance. Unlike photographic scenes or portraits, which are almost always oriented with gravity pointing down, powder micrographs do not have a natural orientation.…”

Section: Computer Visionmentioning

confidence: 99%

Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

DeCost

Jain

Rollett

et al. 2016

JOM

110

View full text Add to dashboard Cite

By applying computer vision and machine learning methods, we develop a system to characterize powder feedstock materials for metal additive manufacturing (AM). Feature detection and description algorithms are applied to create a microstructural scale image representation that can be used to cluster, compare, and analyze powder micrographs. When applied to eight commercial feedstock powders, the system classifies powder images into the correct material systems with greater than 95% accuracy. The system also identifies both representative and atypical powder images. These results suggest the possibility of measuring variations in powders as a function of processing history, relating microstructural features of powders to properties relevant to their performance in AM processes, and defining objective material standards based on visual images. A significant advantage of the computer vision approach is that it is autonomous, objective, and repeatable.

show abstract

Section: Computer Visionmentioning

confidence: 99%

Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

DeCost

Jain

Rollett

et al. 2016

JOM

110

View full text Add to dashboard Cite

show abstract

“…After preprocessing, if we take an image by preprocessing as a texture image, then many methods of texture analysis can be used to extract the target features [6,13]. We generally choose textures which are in the center of textures when we extract the features of textures, it is because the area includes a lot of detailed information of textures, i.e., these are the rotation direction of the texture, length, breadth, height, width, depth, area coverage, distance between textures, etc.…”

Section: Feature Extraction Of Target Imagementioning

confidence: 99%

Fuzzy automata system with application to target recognition based on image processing

Pang

Han

2011

Computers & Mathematics with Applications

View full text Add to dashboard Cite

a b s t r a c tIn order to get better image processing and target recognition, this paper presents a fuzzy automata system to target recognition. The system first performs image processing, and then accomplishes the target recognition. The system consists of four parts: image preprocessing, feature extraction, target matching and experiment. Compared with existing approaches, this paper uses both global features and local features of the target image, and carries out target recognition by using a fuzzy automata system. Simulation results show that the correct recognition rate based on the fuzzy automata system for target recognition is higher at 94.59%, an improvement on an average of 29.24%, compared to other existing approaches. Finally, some directions for future research are described.

show abstract

“…Texture analysis has been a very active research field over the last four decades, and an exhaustive study of this field is of course beyond the scope of this paper. Some surveys and comparative studies of existing methods can be found in Haralick (1979), Tuceryan and Jain (1993), Reed and du Buf (1993), Randen and Husoy (1999), Zhang and Tan (2002), the last one being devoted to invariant texture analysis. In what follows, we first focus on classical approaches and the type of global invariances they allow.…”

Section: Previous and Related Workmentioning

confidence: 99%

Shape-based Invariant Texture Indexing

2009

View full text Add to dashboard Cite

This paper introduces a new texture analysis scheme, which is invariant to local geometric and radiometric changes. The proposed methodology relies on the topographic map of images, obtained from the connected components of level sets. This morphological tool, providing a multi-scale and contrast-invariant representation of images, is shown to be well suited to texture analysis. We first make use of invariant moments to extract geometrical information from the topographic map. This yields features that are invariant to local similarities or local affine transformations. These features are invariant to any local contrast change. We then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases. The obtained experimental results outperform the current state of the art in locally invariant texture analysis.

show abstract

A Review of Recent Texture Segmentation and Feature Extraction Techniques

Cited by 513 publications

References 0 publications

Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

Fuzzy automata system with application to target recognition based on image processing

Shape-based Invariant Texture Indexing

Contact Info

Product

Resources

About