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by Max Maxfield Do you recall my earlier column When Genetic Algorithms Meet Artificial Intelligence? This reflected my discovery that the chaps and chapesses at Algolux are using an evolutionary algorithm approach in their Atlas Camera Optimization Suite. The idea here is that, when it comes to creating a new camera system, each of the components lens assembly, sensor, and image signal processor (ISP) has numerous parameters (variables). This means that a massive and convoluted parameter space controls the image quality for each camera configuration. Traditional human-based camera system tuning can involve weeks of lab tuning combined with months of field and subjective tuning. The sad part of all of this is that there’s no guarantee of results when it comes to computer vision applications employing artificial intelligence (AI) and machine learning (ML). The problem is that tuning a camera system for a computer vision application is a completely different

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