In the thesis, attention is focussed on theory and application of the multi-scaletransform and the wavelet transform. Most attention will be given to the analysis of well logs. A well log is a sequence of measurements that is obtained from a bore hole. The well logs are a record of the...
Therefore, the Gabor wavelets have been used in image processing for feature extraction and texture analysis. The wavelet transform is a very powerful tool for texture discrimination [24]. It is a linear operation that decomposes a signal into components that appear at different scales. This ...
while prediction accuracy needs to be high. This fast expansion in medical image modalities and data collection leads to generation of so called “Big Data” which is time-consuming to be analyzed by medical experts.
. These coefficients are then fused, and the fused results are inverse-transformed to generate the final fused image. Transformation methods used for input images include wavelet transform, contourlet transform, shearlet transform, and more5,6,7. Each of these transformations has its drawbacks, leadi...
To address the fuzzy segmentation boundaries, missing details, small target losses and low efficiency of traditional segmentation methods in ancient mural image segmentation scenarios, this paper proposes a mural segmentation model based on multiscale feature fusion and a dual attention-augmented segmentatio...
The estimated discrepancy of two feature classes very much depends on considered scale levels; then, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden markov tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree ...
EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32, 1084–1093 (2007). Google Scholar Polat, K. & Gunes, S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl...
Extracting buildings from very high resolution (VHR) images has attracted much attention but is still challenging due to their large varieties in appearance and scale. Convolutional neural networks (CNNs) have shown effective and superior performance in automatically learning high-level and discriminative...
EEG classification; emotion recognition; multiscale feature; cross attention MSC: 92B201. Introduction Affective computing is an umbrella term for human emotion, sentiment, and emotion recognition. As emotion affects human daily behaviors and cognitive activities, emotion recognition plays a crucial role...
Abstract. Early childhood marks a pivotal period in the maturation of executive function, the cognitive ability to consciously regulate actions and thought