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Matching of image spatial-temporal features is a fundamental task in image processing module used to comprehend two or more images taken, for example, at different times, from different sensors, or from different aspects. Virtually all the intelligent image processing and understanding products require image comprehension, or closely related operations as intermediate steps. Examples of systems in which image spatial-temporal matching is a significant component include automated image registration (AIR), automated target recognition (ATR), change detection (CD), multiple sensor data fusion, 3D scene reconstruction, vision-based autonomous navigation, image database classification and retrieval; internet image search engine; product tracking, consumer games, and recognizing images taken by customers of billboards, posters, building signs, logos, etc. The main challenge posted by the vision based systems is the developed vision algorithms have to be reliable, robust and capable to handle the realistic conditions in our real-world.
Sentinel AVE’s image matching engine is a truly solution that can live up to the market’s expectations. Using a combination of the inherent knowledge of image features with several key technologies, our image matching engine can efficiently detect, match and recognize natural-occurring image features in natural and complex environments. Inspired by recent achievements from biological vision study, our approach extracts highly-distinctive image features as a basic primitive to represent image information and perform spatial-temporal matching. These unique feature representations are compact and show superior advantages in terms of distinctiveness, robustness to occlusions, invariance to scale, and tolerance of geometric distortions. Furthermore, our fast search engine dramatically improves on previous approaches over hundred-times faster for matching and recognition of objects from a large library of database.
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Core Technologies
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 Urban3D
AVE
RIMR
Product Details
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