UCLA Released a Powerful Image Detection Algorithm For Everybody’s Use
A UCLA Engineering group made an algorithm public that process images at high speeds and see them in ways that human eyes cannot. The code could be used to detect a face, a fingerprint or an iris recognition for security purposes also, can be used in autonomous cars’ navigation systems or for inspecting industrial products.
A UCLA Engineering group made an algorithm public that process images at high speeds and see them in ways that human eyes cannot. The code could be used to detect a face, a fingerprint or an iris recognition for security purposes also, can be used in autonomous cars’ navigation systems or for inspecting industrial products. The algorithm developed by a group of UCLA engineers which led by Bahram Jalali. Algorithm performs a mathematical operation which can able to identifies the edges and detects and extract every feature of the target. Also, can enhance images and recognize objects texture.
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The algorithm is available for download from two open source website; Github and Matlab File Exchange. The reason for making the algorithm as open source, make the other programs work on the algorithm and make this system improve. The algorithm helped the team to find a new system which has been used for ultrafast imaging and detecting cancers cells in the blood (The Phase Stretch Transform algorithm ). The algorithm even can detect the images which can not able to scanned, for example, it can detect LED lamp’s internal structure, this would be helpful to see distant stars that not normally be visible in astronomical images.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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