( e brain disorder that involves distorti...)Mimicking how the brain recognizes street scenes

"The versatility of the biological model turns computer vision from a trick into something really useful," said co-author Stanley Bileschi, a post-doctoral researcher in the Poggio lab. He and co-author Lior Wolf, a former post-doctoral associate who is now on the faculty of the Computer Science Department at Tel-Aviv University, are working with the MIT entrepreneur office, the Deshpande Center in the Sloan School. This center helps MIT students and professors bridge the gap between an intriguing idea or technology and a commercially viable concept.

Recognizing Scenes

The IEEE paper describes how the team "showed" the model randomly selected images so that it could "learn" to identify commonly occurring features in real-word objects, such as trees, cars, and people. In so-called supervised training sessions, the model used those features to label by category the varied examples of objects found in digital photographs of street scenes: buildings, cars, motorcycles, airplanes, faces, pedestrians, roads, skies, trees, and leaves. The photographs derive from a Street Scene Database compiled by Bileschi.

Compared to traditional computer-vision systems, the biological model was surprisingly versatile. Traditional systems are engineered for specific object classes. For instance, systems engineered to detect faces or recognize textures are poor at detecting cars. In the biological model, the same algorithm can learn to detect widely different types of objects.

To test the model, the team presented full street scenes consisting of previously unseen examples from the Street Scene Database. The model scanned the scene and, based on its supervised training, recognized the objects in the scene. The upshot is that the model learned from examples, which, according to Po
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Source:McGovern Institute for Brain Research