The aim of this work is to take advantage of image complexity in the design of macro-level neural networks for medical image segmentation. To demonstrate the efficacy and wide applicability of image complexity
However, if the focus is on minimizing the number of parameters and model complexity, the TP-Unet+AE model can be utilized to achieve a smaller parameter count and reduced complexity. Nevertheless, in the context of medical imaging, where accurate segmentation is of utmost importance, prioritizing...
However, the efficiency is achieved at the expense of complexity to perform mathematical evaluation. Based on the requirements for efficient interpolation and compression with less complexity, Bilinear interpolation is selected in this research for image resampling. The region of interest specified by the...
This study suggested that the problem of camera attribution by using JPEG compression should focus on the image complexity factors in addition with image quality factor to improve the detection performance. Table 20. Comparison of source camera brand/model attribution frameworks by using JPEG ...
To make high-resolution image synthesis with diffusion models more efficient, the proposed method involves using an autoencoding model to separate the compressive and generative phases. This reduces computational complexity by operating in a lower-dimensional latent space, leverages the inductive bias of...
In this work, an image captioning method is proposed that uses discrete wavelet decomposition along with convolutional neural network (WCNN) for extracting the spectral information in addition to the spatial and semantic features of the image. An attempt is made to enhance the visual modelling of ...
Linear filtration is nothing but a frequency filtering, whereby certain frequencies are left unchanged, and the other are suppressed. The operation of filtration can be performed both in the frequency as well as in spatial domain. While in terms of computational complexity, performing the filtering ...
The increased research in data fusion in several domains is motivated by the multiplication of sources of knowledge and data, and of techniques for their acquisition. The huge volume of data to be processed and the complexity of the problems that are addressed induce a real need for the develop...
Compared to segmentation of [48] which suffers from network complexity and ignores the correlation between the three sequential segmentation tasks, it decomposes brain tumor segmentation into three different but related tasks. Each task has an independent convolutional layer, one classification layer, one...
4.1.1 Phased-array ultrasonic transducers and convolutional neural networks Together with the increasing complexity of UT cases, the corresponding NDT systems such as PAUT also evolved, resulting in rich image-like UT data (see chapter 2.1). Based on the success of DNNs for image classification, ...