Cellular neural network: Difference between revisions

Revision as of 07:33, 27 December 2020

Revision as of 07:35, 27 December 2020

Line 115:

==Applications==

{{Section citations needed|date=December 2020}}

CNN researchers have diverse interests, ranging from physical, engineering, theoretical, mathematical, computational, and philosophical applications.

The philosophy, interests, and methodologies of CNN researchers are varied. Due to the potential of the CNN architecture, this platform has attracted people from a variety of backgrounds and disciplines. Some are exploring practical implementations of CNN processors, others are using CNN processors to model physical phenomena, and there are even researchers exploring theoretical mathematical, computational, and philosophical ideas through CNN processors. Some applications are engineering related, where some known, understood behavior of CNN processors is exploited to perform a specific task, and some are scientific, where CNN processors are used to explore new and different phenomenon. CNN processors are versatile platforms that are being used for a variety of applications.

CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors needed for applications like particle detection in jet engine fluids and spark-plug detection. Currently, CNN processors can achieve up to 50,000 frames per second, and for certain applications such as missile tracking, flash detection, and spark-plug diagnostics these microprocessors have outperformed a conventional supercomputer. CNN processors lend themselves to local, low-level, processor intensive operations and have been used in feature extraction, level and gain adjustments, color constancy detection, contrast enhancement, [[deconvolution]], image compression, motion estimation, image encoding, image decoding, image segmentation, orientation preference maps, pattern learning/recognition, multi-target tracking, image stabilization, resolution enhancement, image deformations and mapping, image inpainting, optical flow, contouring, [[moving object detection]], axis of symmetry detection, and [[image fusion]].