Cipia an AI computer vision in-cabin automotive solutions provider announced that the company had secured a design win with an additional Chinese OEM together with Tier 1 HiRain. Cipia's Driver Sense Driver Monitoring System (DMS) will be incorporated into a pickup truck model that will be sold in South America, Australia and New Zealand. The model is expected to start production in 2023. With this announcement, Cipia has now been awarded 29 design wins across 6 car manufacturers. Yehuda Holtzman, CEO of Cipia, said "The selection of Cipia's DMS by a new OEM and Tier 1 demonstrates again the reliability and quality of our technology, and our ability to deliver safer and better driving experiences. We are continuing to push and expand our collaborations with new OEMs, focusing on the US, European and Chinese markets."

The MINSU(Mobile Inventory and Scanning Unit) algorithm uses the computational vision analysis method to record the residual quantity/fullness of the cabinet. To do so, it goes through a five-step method: object detection, foreground subtraction, K-means clustering, percentage estimation, and counting. The input image goes through the object detection method to analyze the specific position of the cabinets in terms of coordinates. After doing so, it goes through the foreground subtraction method to make the image more focus-able to the cabinet itself by removing the background (some manual work may have to be done such as selecting the parts that were not grab cut by the algorithm). In the K-means clustering method, the multi-colored image turns into a 3 colored monotonous image for quicker and more accurate analysis. At last, the image goes through percentage estimation and counting. In these two methods, the proportion that the material inside the cabinet is found in percentage which then is used to approximate the number of materials inside. Had this project been successful, the residual quantity management could solve the problem addressed earlier in the introduction.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Until recently, there was no visceral sense that the largest challenge we face is fixing the planet. Responding to environmental problems was for too long viewed by big companies as a marketing strategy to target consumers who were more environmentally conscious than others. Today, the tides are, literally, changing, and sustainability is now mission critical for businesses as new wisdom has emerged that illustrates how being'green' is a catalyst for innovation and market opportunity. Climate tech companies can now leverage advances in visual data collection, computer vision and AI to bolster their bottom line by focusing on enhancing sustainable practices.

Inspecting five million vehicle welds every day requires the ability to check a weld's quality every 17 milliseconds--an impossible challenge for a human. This type of quality control task is just one of many where the combined technologies of computer vision and AI excel. Cameras, microphones, and a wide array of sophisticated sensors used in IoT solutions are increasingly tying together the physical and digital worlds. Using devices with the analytical capabilities of AI, solutions can quickly scan medical images for potentially concerning anomalies, listen to machinery noises for maintenance problems, or provide more thorough remote monitoring in a variety of environments. Intel and Microsoft Azure are working together to help enterprises deploy intelligent IoT technologies and services, including AI's deep learning abilities, computer vision, and audio or speech capabilities.

What is Edge AI? What are some applications of this technology? Edge Computing runs processes locally on the device itself, instead of running them in the cloud. This reduced computing time allows data to be processed much faster, removes the security risk of transferring the data to a cloud-based server, and reduces the cost of data transfer, as well as the risks of bandwidth outages disrupting performance. Computer vision and AI at the edge are becoming instrumental in powering everything from factory assembly lines and retail inventory management, to hospital urgent care medical imaging equipment like X-ray and CAT scans. Drones, security cameras, robots, facial recognition on cell phones, self-driving vehicles, and more all utilize this technology as well.

Food demand and sustainability to feed the growing population are explained clearly. The technological innovations including 4.0 industry revolution strengthen the agricultural sector. The usage of computer vision and artificial intelligence in the field of agriculture and food industry is deeply elaborated. Emerging technologies such as computer vision and Artificial Intelligence (AI) are estimated to leverage the accessibility of big data for active training and yielding operational real time smart machines and predictable models. This phenomenon of applying vision and learning methods for the improvement of food industry is termed as computer vision and AI driven food industry.

Computer vision is yet another technology that was made possible in the wake of the recent AI revolution. Thanks to modern machine learning algorithms, you can now train a computer to distinguish between objects in images and videos. This opens a world of stunning opportunities for business across industries. Below, we explore five examples of how AI and computer vision are helping to revamp the shopping experience, both online and offline. Likely inspired by Amazon Go, AiFi offers a technology that can turn your brick-and-mortar venue into a line-free store.

Take an isolated tribe in the Amazon rainforest. These people have had no contact with civilization, and have never seen anyone who looked different from them. Imagine -- completely cut-off from the 21st century. This is a photograph of the low-flying helicopter that due to a diversion because of a storm, took the National Geographic photographer Ricardo Stuckert out there, above the tribe. What did the helicopter look like to them? In fact, they shot arrows at the helicopter!

In a bid to boost the gaming, VFX industry, the Software Technology Park of India (STPI) has set up IMAGE, a centre of excellence (CoE) for gaming, VFX, computer vision and AI in Hyderabad on Monday. This is also the first Incubation centre for companies focussed on IP in Gaming, VFX, Artificial Intelligence, Computer Vision set up by STPI. Spread over 10,000 Square feet, the IMAGE CoE, which is inside the existing STPI facility targets to incubate up to 25-30 startups per year for the next five years. It will be investing a total of Rs 19.68 crore over a period of 5 years for the CoE in addition to existing infrastructure in the STPI facility. STPI-IMAGE also signed MoU with five partners TVAGA (Telangana VFX, Animation and Gaming Association), Hyderabad Angels, HYSEA, IIIT- Hyderabad, and TiE Hyderabad with an objective to nurture and promote start-ups and budding entrepreneurs by offering resources like mentoring, technology support, infrastructure like Plug & Play Incubation, Mocap, CVAI labs and funding.

DeepQuanty Artificial Intelligence Labs applies computer vision and artificial intelligence to solve problems in various sectors including BFSI, retail, e-commerce, and healthcare. All the three of them have other successful businesses in the analytics and technology domains. Their products extract data from images using computer vision and process the data using machine learning techniques for better end-use. "Our mission is to drastically improve customer experience (CX) while improving cost efficiencies," says Mahadevan Jayaram, Co-Founder, DeepQuanty. Currently focusing on the BFSI sector, DeepQuanty later aims to leverage the patent-pending technologies in other domains such as healthcare, retail and such.