Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel method is presented to detect defects in random colour textures which requires only a very few normal samples for unsupervised training.  decorrelate the colour image by generating three eigenchannels in each of which the surface texture image is divided into overlapping patches of various sizes. Then, a mixture model and EM is applied to reduce groupings of patches to a small number of textural exemplars, or texems. Localised defect detection is achieved by comparing the learned texems to patches in the unseen image eigenchannels.
['Xianghua Xie', 'Majid Mirmehdi']
I am writing a research paper on a idea Localising surface defects in random colour textures using multiscale texem analysis in image eigenchannels
[{"from": "human", "value": "I am writing a research paper on a idea Localising surface defects in random colour textures using multiscale texem analysis in image eigenchannels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel method is presented to detect defects in random colour textures which requires only a very few normal samples for unsupervised training.  decorrelate the colour image by generating three eigenchannels in each of which the surface texture image is divided into overlapping patches of various sizes. Then, a mixture model and EM is applied to reduce groupings of patches to a small number of textural exemplars, or texems. Localised defect detection is achieved by comparing the learned texems to patches in the unseen image eigenchannels."}]