Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a neuromorphic dual-line vision sensor and signal-processing concepts for object recognition and classification. The system performs ultrahigh speed machine vision with a compact and low-cost embedded-processing architecture. The main innovation of this paper includes efficient edge extraction of moving objects by the vision sensor on pixel level and a novel concept for real-time embedded vision processing based on address-event data. The proposed system exploits the very high temporal resolution and the sparse visual-information representation of the event-based vision sensor. The 2 × 256 pixel dual line temporal-contrast vision sensor asynchronously responds to relative illumination-intensity changes and consequently extracts contours of moving objects.  shows data-volume independence from object velocity and evaluates the data quality for object velocities of up to 40 m/s (equivalent to up to 6.25 m/s on the sensor's focal plane). Subsequently, an embedded-processing concept is presented for real-time extraction of object contours and for object recognition. Finally, the influence of object velocity on high-performance embedded computer vision is discussed.
['Ahmed Nabil Belbachir', 'Michael Hofstätter', 'Martin Litzenberger', 'Peter Schön']
I am writing a research paper on a idea High-Speed Embedded-Object Analysis Using a Dual-Line Timed-Address-Event Temporal-Contrast Vision Sensor
[{"from": "human", "value": "I am writing a research paper on a idea High-Speed Embedded-Object Analysis Using a Dual-Line Timed-Address-Event Temporal-Contrast Vision Sensor"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a neuromorphic dual-line vision sensor and signal-processing concepts for object recognition and classification. The system performs ultrahigh speed machine vision with a compact and low-cost embedded-processing architecture. The main innovation of this paper includes efficient edge extraction of moving objects by the vision sensor on pixel level and a novel concept for real-time embedded vision processing based on address-event data. The proposed system exploits the very high temporal resolution and the sparse visual-information representation of the event-based vision sensor. The 2 \u00d7 256 pixel dual line temporal-contrast vision sensor asynchronously responds to relative illumination-intensity changes and consequently extracts contours of moving objects.  shows data-volume independence from object velocity and evaluates the data quality for object velocities of up to 40 m/s (equivalent to up to 6.25 m/s on the sensor's focal plane). Subsequently, an embedded-processing concept is presented for real-time extraction of object contours and for object recognition. Finally, the influence of object velocity on high-performance embedded computer vision is discussed."}]