Demos (May 28 and 29)
The following institutions will demonstrate their products and technologies. All the titles are tentative.
Honda R&D Co., Ltd.
Personal Car Automated Driving System using Fusion Technology Combined with AI and Model-base Control
Three methods for depth estimation on smartphones
Portrait mode is a popular feature in many recent models of smartphones; it allows users to take DSLR-like photos with shallow depth of field easily.
The basic algorithm of portrait mode consists of three steps. Firstly estimating depth, secondly segmenting the image into foreground and background, and finally blurring pixels that are part of the background. Morpho implements three methods for depth estimation. One is the classical method that uses a stereo camera. It estimates disparity from two rectified images taken by each camera, and converts the estimated disparity into depth. The other two are CNN-based methods that directly estimate depth from a single image. Generally, CNN-based methods are computationally intensive, making them slow on mobile devices. However, we have realized real-time processing on smartphones by modifying the model architecture and using our inference engine named "SoftNeuro".
We explain the above three methods with smartphone demonstrations during our demo sessions. Please visit our booth if you are interested in them.
Research at CyberAgent AI Lab
CyberAgent AI Lab is an R&D team of CyberAgent. We focus on developing cutting-edge technologies for internet media, digital advertising, and gaming business. Our research topics include but not limited to Machine Learning, Computer Vision, Computer Graphics, Interaction Design, and Computational Economics.
Founded in 1998, CyberAgent is a Japanese company providing various internet services. We have the largest market share in the Japanese digital advertising business as of 2019, and actively seeking a future business innovation through cutting edge technologies. Digital ads have a huge market in worldwide, and we aim at bringing state of the art image recognition technologies to revolutionize the digital ads production processes.
Come visit our booth to see our research projects and talk to our research scientists. We look forward to seeing you in the conference!
Panasonic Research and Development on Artificial Intelligence
Design and development of artificial vision for robot learning in the agricultural sector, to estimate crop volume. (Case study Avocados, Michoacán, Mexico)
Nowadays, the demand for avocado has increased significantly worldwide.
The price of the crop is established prior to harvesting, often ignoring the amount of fruit per production period. Producers partially counteract this uncertainty by carrying out manual counting, which consumes a lot of time to provide the estimated number.
As an alternative method, an artificial vision system based on convolutional neural networks has been developed to be installed in a robot, which has the purpose of identifying the fruit while it is in the tree and that is adaptable to the different geographical conditions of the growing region.
This will allow the service of counting and monitoring of the avocados before being harvested, with a level of certainty that favors the sale of the production in advance, to facilitate its commercialization.
External Link: http://www.antalinnovacion.com/
IoT with Google Vision Kit (tentative)
Electrical and Mechanical Services Department, Hong Kong S.A.R. Government
Smart Fever Screening System for Health Clearance
Thermal imaging systems are in place at boundary control points of many Asian countries which assist the quarantine authorities to perform fever screening on travellers. While it is an effective measure for identifying febrile subjects and has been adopted for screening large quantity travellers since the outbreak of SARS in 2003, existing fever screening by thermal imaging systems involve considerable manual input for operation which the detection outcome could be affected by human error and fatigue. In addition, operators inevitably have close contact with travellers and are more prone to infection. The consequences are compromised fever screening accuracy and lessened health clearance efficiency.
The Smart Fever Screening System uniquely integrates thermal imaging, computer vision and pattern recognition for reducing manual effort and effectively identifying useful thermal information related to fever. The automatic fever detection and tracking function not only assists to enhance health clearance efficiency at boundary control points but also lowers the risk of operators contracting infectious diseases. Other than boundary control points, the Smart Fever Screening System can also be applied in hospitals, clinics, elderly centres and schools. A patent for the design of the system has already been filed.