Industries Benefiting from Computer Vision Innovation
Computer vision can potentially support many different applications delivering life-saving functionalities for patients by assisting an increasing number of doctors to better diagnose their patients, monitor the evolution of diseases, and prescribe the right treatments. Computer vision for medical use is based on tasks such as medical imaging analysis, predictive analysis, and healthcare monitoring.
Computer vision can be utilized to perform as a virtual assistant for customer service agents, delivering effective decision support during the agent-customer interaction in order to improve the customer experience. The agent’s performance is enhanced by the computer’s ability to quickly identify devices and technical issues, as well as to provide faster resolutions. This has been proven to reduce agent training time and streamline the entire support process.
Computer vision also enables gradual automation towards full self-service with device recognition and augmentation. As an example, the customer can present a faulty device via smartphone, and the virtual assistant can recognize devices, detect motions, and interact in real time with the customer. The virtual assistant uses augmented reality to guide the customer to resolution via a step-by-step process and is also able to correct the customer in case of errors, ensuring that the resolution is successful.
Computer vision acts as a complete sensory system, one that simultaneously takes in the environment around the driver and analyzes it for potential threats, obstacles, and other relevant situations that the driver would need to react to while driving. Computer vision can be helpful to prevent car accidents on the road, providing tools to prevent pedestrian-car accidents off the road. Computer vision-powered car cameras are being developed to detect pedestrians before drivers may notice them, giving drivers real-time alerts and responses to prevent potentially deadly accidents.
With computer vision and intelligent transportation systems (ITS), drivers can get a safety net. These technologies make it possible to mitigate human error in the auto industry, assisting drivers at the wheel with tools and features that keep them from committing serious mistakes and accidents.
Computer vision algorithms take care of the image analysis. They assist the robot to identify objects and understand the environment it is in, so to navigate without stumbling on obstacles. Some of computer vision’s algorithms are application-specific, while other algorithms are common to almost all robotic applications and robots which uses computer vision.
The ability to get 3D images enables robots to distinguish visually between an item and its background, where computer vision algorithms allow them to recognize and identify complex shapes. Hardware and programming advances are facilitating the creation of more versatile mobile structures and sensor-equipped tools that can safely handle delicate and fragile items, such as electronics and food products. Computer vision is also allowing robots to cooperate safely and work alongside humans, a new concept known as cobots.
New imaging techniques have provided new application opportunities for Computer Vision in the manufacturing field. Computational imaging allows a series of images to be combined in different ways to reveal details that can’t be seen using conventional imaging techniques. As an example, polarized images can display stress patterns in materials. Other developments in machine vision technology lead to enhanced performance, integration, and automation in the manufacturing industry.
The availability of small, embedded processing boards and chips, usually based on ARM architecture, offers great potential for the development of computer vision systems present into other devices and manufacturing processes. Many of the leading image processing libraries and toolkits are being ported to these platforms, offering a wider range of vision solutions. Combining processing capabilities with low-cost cameras, including board-level cameras, means that computer vision systems could be incorporated into a wide variety of products and processes in the manufacturing field with relative small cost overheads.
Computer vision has become essential to remaining competitive in the digital retail space because it offers innovative customer experiences and automation of processes that makes the customers enjoy while reducing costs. Customers demand the convenience of features such as visual search or automated question answering and retailers benefit from the increased operational efficiency of features, such as warehouse automation or more effective product discovery.
Visual search is one of the most popular ways computer vision is utilized to benefit the customer experience and alleviate the limitations of text-based product discovery. Collaborative filtering algorithms can look at a large data set of electronic commerce activities and determine the latent features that guide to successful recommendations. Even for large catalogs without data on every product, it’s possible to use deep learning and computer vision to provide quality behavior-driven recommendations relevant to users that create opportunities to upsell.
Augmented reality (AR) combines aspects of the real world, such as backgrounds, with computer-generated content, such as a product. Customers enjoy the immersive experiences which make it easier to buy without seeing a product in person. That is why retailers should push impressive new features like AR to amaze their customers and boost online revenue. Computer vision is required to precisely position the digitized object and rotate it properly to make it appear authentic. Multiple sensory modalities may be altered beyond visual, such as auditory or haptic.
Food and Beverage
Computer vision is finding new ways to introduce quality and efficiency into the processing of food products. Adoption is growing as food manufacturers look to automation to solve their most pressing challenges.
Barcode reading was once the primary form of computer vision used in the food and beverage industry, the search for better traceability has increased the importance of optical character recognition (OCR) technology. These vision applications, used for date codes and product descriptions, combine character reading with pattern searches to identify characters, even on busy backgrounds.
Automated counting and sorting systems based on image analysis can grade fruits, vegetables, and more, according to their shape, size and maturity, increasing the sorting speed by multiple times compared to humans.
Automated visual check of a filling level and package labeling is another important application of computer vision in the food industry. Besides, a visual system can check the freshness of a packed product with the aid of a special ink changing its color with time and at a different speed depending on the temperature.
RidgeRun’s Computer Vision Hardware, Software, & Services
RidgeRun has vast experience in developing software for embedded systems, focusing on embedded Linux and GStreamer, also including Deep Learning and Computer Vision related projects which can be leveraged by the products of the customers, allowing them to reduce the time-to-market.
RidgeRun’s main projects in the Computer Vision area are listed below:
Image Stitching for NVIDIA Jetson: https://developer.ridgerun.com/wiki/index.php?title=Image_Stitching_for_NVIDIA_Jetson
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