Networks For Image Analysis Research Articles

Toward Interpreting the Thermally Activated β Dynamics in Metallic Glass Using the Structural Constraint Neural Network.

It is crucial to unravel the structural factors influencing the dynamics of the amorphous solids. Deep learning aids in navigating these complexities, while transparency issues persist. Drawing inspiration from the successful application of prototype neural networks in image analysis, this study introduces a novel machine learning approach to address interpretability challenges in glassy research. Distinguishing from traditional machine learning models, the proposed neural network tries to learn distant structural motifs for solid-like atoms and liquid-like atoms. Such learned structural motifs constrain the underlying structural space and thus can serve as a breakthrough in explaining how structural differences impact dynamics. We further used the proposed model to explore the correlation between the local structure and activation energy in the CuZr alloys. Building upon this interpretable model, we demonstrated significant structural differences among atoms with different activation energies. Our interpretable model is a data-driven solution that provides a pathway to reveal the origin of structural heterogeneity in amorphous alloys.

On the use of artificial neural networks in topology optimisation

The question of how methods from the field of artificial intelligence can help improve the conventional frameworks for topology optimisation has received increasing attention over the last few years. Motivated by the capabilities of neural networks in image analysis, different model-variations aimed at obtaining iteration-free topology optimisation have been proposed with varying success. Other works focused on speed-up through replacing expensive optimisers and state solvers, or reducing the design-space have been attempted, but have not yet received the same attention. The portfolio of articles presenting different applications has as such become extensive, but few real breakthroughs have yet been celebrated. An overall trend in the literature is the strong faith in the "magic" of artificial intelligence and thus misunderstandings about the capabilities of such methods. The aim of this article is therefore to present a critical review of the current state of research in this field. To this end, an overview of the different model-applications is presented, and efforts are made to identify reasons for the overall lack of convincing success. A thorough analysis identifies and differentiates between problematic and promising aspects of existing models. The resulting findings are used to detail recommendations believed to encourage avenues of potential scientific progress for further research within the field.

Deriving cues from human cognition for the modelling of shack boundaries in aerial imagery


 
 
 Organic studies inspire cues for modelling logic in image processing and become a basis for the development of novel remote-sensing algorithms. Examples of applications of such paradigms include the growing application of techniques such as object-oriented analysis and neural networks in image analysis for which the logic was drawn from studying various components of organic systems in the human body. Here we document a key investigation based on a set of cognitive tests conducted using aerial imagery captured over Cape Town (South Africa). These tests were conducted to later draw parallels with a feature extraction algorithm for shack settlements. We found that the visual variables of ‘pattern’ and ‘shape’ display the most significant cognitive guide for shack boundary extraction. Although the focus here was on digital imagery, learning points can be selected for application in other scientific fields as well.
 
 
 
 
 Significance: 
 
 
 
 Provides an interest point for several image-processing and computer vision sciences.
 Contributes to geospatial studies and helps improve mapping and imaging initiatives.
 Directed towards solving the challenges that urban governors face in slum management.
 
 
 

Artificial Neural Networks for Image Analysis and Computer Vision

Artificial Neural Networks for Image Analysis and Computer Vision

Application of artificial neural networks for image analysis of organ culture preserved donor corneas: A pilot study.

Application of artificial neural networks for image analysis of organ culture preserved donor corneas: A pilot study.

4216 Application of artificial neural networks for image analysis of organ culture preserved donor corneas: A pilot study

4216 Application of artificial neural networks for image analysis of organ culture preserved donor corneas: A pilot study

Artificial neural networks for decision support in clinical medicine.

Connectionist models such as neural networks are alternatives to linear, parametric statistical methods. Neural networks are computer-based pattern recognition methods with loose similarities with the nervous system. Individual variables of the network, usually called 'neurones', can receive inhibitory and excitatory inputs from other neurones. The networks can define relationships among input data that are not apparent when using other approaches, and they can use these relationships to improve accuracy. Thus, neural nets have substantial power to recognize patterns even in complex datasets. Neural network methodology has outperformed classical statistical methods in cases where the input variables are interrelated. Because clinical measurements usually derive from multiple interrelated systems it is evident that neural networks might be more accurate than classical methods in multivariate analysis of clinical data. This paper reviews the use of neural networks in medical decision support. A short introduction to the basics of neural networks is given, and some practical issues in applying the networks are highlighted. The current use of neural networks in image analysis, signal processing and laboratory medicine is reviewed. It is concluded that neural networks have an important role in image analysis and in signal processing. However, further studies are needed to determine the value of neural networks in the analysis of laboratory data.