Researchers Used a New Computer Vision Technique to See Around Corners
Daniel Johnson — June 20, 2019 — Tech
References: cmu.edu & techcrunch
New research from Carnegie Mellon University used a computer vision technique to see around corners, the research was in collaboration with the University of Toronto as well as University College London. The technique that was implemented uses special sources of light, along with sensors and computer vision processing -- together these can reconstruct fine details of an image. This new computer vision technology allows for far more detail to be seen than was previously available.
Despite the research advancements, some limitations persist as researchers have only had success when focused on "relatively small areas." However, this limitation could be addressed by implementing other techniques used for non-line-of-site (NLOS) research. Some of the NLOS techniques are already implemented in the automotive industry, including Tesla's Autopilot system.
Image Credit: Carnegie Mellon University
Despite the research advancements, some limitations persist as researchers have only had success when focused on "relatively small areas." However, this limitation could be addressed by implementing other techniques used for non-line-of-site (NLOS) research. Some of the NLOS techniques are already implemented in the automotive industry, including Tesla's Autopilot system.
Image Credit: Carnegie Mellon University
Trend Themes
1. Shape-detecting Computers - Disruptive innovation opportunity: Develop advanced computer vision systems that can detect and reconstruct fine details of an image, allowing for improved object recognition and spatial understanding.
2. Seeing Around Corners - Disruptive innovation opportunity: Create computer vision techniques that can perceive hidden objects or scenes by analyzing light reflections, enabling applications in surveillance, search and rescue, and autonomous navigation.
3. Non-line-of-site (NLOS) Research - Disruptive innovation opportunity: Apply non-line-of-site techniques in various industries, such as automotive, to enhance safety and improve the capabilities of autonomous systems.
Industry Implications
1. Computer Vision - Disruptive innovation opportunity: Develop advanced computer vision technologies for a wide range of industries, including healthcare, retail, and security, to enable new applications and improve efficiency.
2. Automotive - Disruptive innovation opportunity: Integrate advanced non-line-of-site (NLOS) techniques in autonomous vehicles to enhance perception, object detection, and collision avoidance capabilities.
3. Surveillance and Security - Disruptive innovation opportunity: Utilize shape-detecting computers and non-line-of-site (NLOS) techniques in security systems to enhance surveillance capabilities and detect hidden threats or objects.
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