By Rick S. Blum, Zheng Liu
Taking one other lesson from nature, the most recent advances in snapshot processing know-how search to mix photograph info from numerous varied varieties of sensors so as to receive a extra actual view of the scene: greatly just like we depend on our 5 senses. Multi-Sensor snapshot Fusion and Its functions is the 1st textual content devoted to the speculation and perform of the registration and fusion of photograph information, masking such methods as statistical equipment, color-related suggestions, model-based equipment, and visible details show strategies.
After a assessment of state of the art snapshot fusion recommendations, the booklet presents an summary of fusion algorithms and fusion functionality overview. the subsequent chapters discover fresh growth and useful purposes of the proposed options to fixing difficulties in such components as clinical prognosis, surveillance and biometric structures, distant sensing, nondestructive assessment, blurred photograph recovery, and photo caliber review. well-known leaders from and academia give a contribution the chapters, reflecting the most recent examine developments and offering precious algorithms to assist implementation.
Supplying a 28-page full-color insert, Multi-Sensor photograph Fusion and Its functions essentially demonstrates the advantages and probabilities of this progressive improvement. It offers an effective wisdom base for using those state of the art concepts to new demanding situations and developing destiny advances.
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Extra info for Multi-Sensor Image Fusion and Its Applications (Signal Processing and Communications)
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One of the most critical problems is that there is still a lack of reliable and efﬁcient methods to validate and evaluate different fusion schemes. In image fusion, it is very difﬁcult to access the truth, making evaluations often subjective. Dealing with unregistered images further complicates these problems. Most fusion schemes usually assume that the source images to be fused are perfectly registered. In practice, this requirement may be difﬁcult to achieve. Thus we believe, that, after the consideration of the fusion algorithms themselves, the two most important issues for image fusion are how to judge performance, and how to register the images.