In this paper, we review most major filtering approaches to texture feature extraction and perform a comparative study. Filtering approaches included are Laws masks (1980), ring/wedge filters, dyadic Gabor filter banks, wavelet transforms, wavelet packets and wavelet frames, quadrature mirror filters, discrete cosine transform, eigenfilters, optimized Gabor filters, linear predictors, and optimized finite impulse response filters. The features are computed as the local energy of the filter responses. The effect of the filtering is highlighted, keeping the local energy function and the classification algorithm identical for most approaches. For reference, comparisons with two classical nonfiltering approaches, co-occurrence (statistical) and autoregressive (model based) features, are given. We present a ranking of the tested approaches based on extensive experiments.

Filtering for texture classification: a comparative study

This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem

/pdf/ultrasound-image-segmentation-a-survey-55ssqpzk7q.pdf

Ultrasound image segmentation: a survey

In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred.

/pdf/blur-insensitive-texture-classification-using-local-phase-1umgtwy9zd.pdf

Blur Insensitive Texture Classification Using Local Phase Quantization

Comparison of algorithms that select features for pattern classifiers

Towards better exploiting convolutional neural networks for remote sensing scene classification

Both pattern recognition and computer vision have experienced rapid progress in the last twenty-five years. This book provides the latest advances on pattern recognition and computer vision along with their many applications. It features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers. The book is divided into five parts: basic methods in pattern recognition, basic methods in computer vision and image processing, recognition applications, life science and human identification, and systems and technology.

https://www.worldscientific.com/doi/pdf/10.1142/9789814273398_fmatter

L. F. Pau

Papers

Handbook of Pattern Recognition and Computer Vision