Exploration of Computer Vision and Pattern Recognition

Dear Colleagues,

Artificial neural networks, such as multilayer perceptual machines, feedback neural networks, convolutional neural networks, and spiking neural networks, are inspired by biological nervous systems and partially simulate them. The rapid development of deep learning offers new solutions to many problems and generates significant challenges. Especially in the field of computer vision and pattern recognition, AI has led to unprecedented disruptive changes. The challenge lies in further exploring the performance of AI, broadening the scope of AI applications, and driving technological development through advances in the field. Current applications, such as generative AI, have raised concerns about authenticity, and ethical concerns around AI applications have emerged, involving deepfakes, adversarial attacks, and the traceability of digital media. Therefore, there are still many aspects of artificial neural networks that need to be developed and many issues that need to be addressed. First, the application of AI in image processing is expanding, showing great potential in areas such as image enhancement and virtual reality, but there are still many areas where there are unresolved issues. Second, the authenticity of images poses a serious challenge, and traditional detection algorithms are often overwhelmed when working with these highly synthesized images. Finally, research on adversarial attacks brings new perspectives to the study of deep neural networks. Therefore, it is urgent to conduct further research on computer vision and pattern recognition.

In this context, this Special Issue seeks contributions on new advances in the fields of computer vision and pattern recognition to improve the application and development of neural networks; related research on neural networks’ application is also welcome.

Dr. Qian Jiang
Guest Editor

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• artificial neural networks
• bioinformatics
• deepfake detection
• image fusion
• image colorization
• image super-resolution
• adversarial attack and defence on neural networks
• pattern recognition