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A texture extraction technique using 2D-DFT and Hamming distance | IEEE Conference Publication | IEEE Xplore
Source: ieeexplore.m7h.net
Article summary:
1. This paper presents a novel technique for texture extraction and classification using 2D-DFT transformation and a Hamming Distance based neural network.
2. The proposed feature extraction technique was implemented and tested on the Brodatz benchmark database.
3. The mean and standard deviation were calculated for each row of the sub-image (128 pixels) using the values of the pixel of each Fourier domain image.
Article analysis:
The article is generally reliable, as it provides detailed information about the proposed technique for texture extraction and classification, as well as its implementation on the Brodatz benchmark database. The article also provides an overview of related research in this field, which helps to provide context for the proposed technique. Furthermore, the article does not appear to be biased or one-sided in its reporting, as it presents both sides of the argument equally. Additionally, there are no unsupported claims or missing points of consideration in the article.
However, there are some areas where more evidence could be provided to support certain claims made in the article. For example, while it is stated that texture has been found to provide cues to scene depth and surface orientation, no evidence is provided to back up this claim. Additionally, while it is mentioned that millions of digital images are created throughout the World Wide Web, digital cameras etc., no sources are provided to back up this statement either.
In conclusion, overall this article appears to be reliable and trustworthy with regards to its content and presentation of information; however there are some areas where more evidence could be provided to support certain claims made in the article.