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1. PCA alone. 2. Gabor with PCA. Gabor with PCA and ICA. rate is given. The successfulness of the proposed method is compared with some popular face recognition schemes like Gabor wavelet based classification 8 the PCA method 29 the LDA method 30 and the eigen faces method 3. C 2010 Published by Elsevier Ltd Open access under CC BY-NC-ND license. Keywords Human face recognition Principal Component Analysis Gabor Wavelet transform ICA LDA. It is observed from the results of PCA with Gabor...
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How to fill out gabor feature based classification
How to fill out gabor feature based classification
01
Step 1: Preprocess the input image by converting it to gray-scale.
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Step 2: Apply Gabor filters to the gray-scale image. These filters will extract Gabor features at different orientations and scales.
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Step 3: Calculate the mean and standard deviation of the filtered responses for each Gabor filter.
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Step 4: Concatenate the mean and standard deviation values of all the Gabor filters to form the feature vector.
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Step 5: Use the feature vector as input to a classifier, such as SVM, for classification.
Who needs gabor feature based classification?
01
Researchers and practitioners in the field of computer vision and image processing who are interested in texture-based classification.
02
Individuals or organizations working on tasks like facial recognition, texture segmentation, object detection, and image retrieval where texture information plays a crucial role.
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Researchers developing systems for medical imaging analysis, where Gabor features can be used to differentiate between different tissue types or detect abnormalities in images.
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