Feature extraction & image processing for computer vision

By Mark S. Nixon, A. Aguado. 2012

403 citations recorded by the source corpus.

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A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework (2021, methodology): “If we consider an image, I of m×m pixels, its 2D-DFT can be expressed as [41]:”
A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing (2020, background): “environ ents [10], and airborne syste s [11,12], in this paper, e provide a co prehensive study of the ost r pres ntative forest fire d tection syst s, focusing on those that use optical re ot sensing, as el as digital i age processing [13] nd classification t chniques [14].”
Deep into the Brain: Artificial Intelligence in Stroke Imaging (2017, methodology): “, signal intensity gradient, and skewness, which are not discernible by humans.(3,7) Figure 1 demonstrates an example of automated segmentation of infarct lesions using supervised machine learning techniques.”
DOCToR: The Role of Deep Features in Content-Based Mammographic Image Retrieval (2018, methodology): “Feature Extractor Category #Features BIC Histogram [15] Color 512 Edge Histogram [16] Color 150 Normalized Histogram [17] Color 256 Haralick [18] Texture 24 Rotation Invariant LBP [19] Texture 108 Texture Spectrum [20] Texture 8 Daubechies [21] Shape 16 Haar [21] Shape 16 Zernike [22] Shape 36”