Neurotechnology, a provider of high-precision biometric and object identification technologies, today announced the availability of SentiSight 3.0, a Software Development Kit (SDK) for universal object recognition. This latest version adds shape-based recognition and offers enhanced local-feature-based recognition that is 30-40 times faster than SentiSight 2.1. It also includes enhanced tracking of fast moving objects and objects in front of complex backgrounds. The object recognition algorithms in SentiSight 3.0 enable an even broader range of recognition capabilities for applications as varied as manufacturing, artificial intelligence, searches for identifiable marks and even place recognition.
SentiSight 3.0 provides enhanced 2D and 3D object recognition quality using still or video images from most digital cameras, including Webcams. It can detect and recognize whether a particular rigid object, such as a product, logo or building, is in a scene and identify its specific location in that scene. It can also count the number of specific identified objects in a scene and can compare two photographic images to provide place recognition, based on objects within the picture.
"Today object recognition technology is being widely used in different fields of business," said Denis Kochetkov, manager of research and development for Neurotechnology. "With enhanced speed and reliability, the addition of a shape-based algorithm and improved tracking, SentiSight 3.0 SDK further extends the capabilities of object recognition and enables our customers and partners to incorporate this technology into a variety of new applications."
The new shape-based algorithm in SentiSight 3.0 is suitable for localization and recognition with objects that have distinguishable external or internal edges. The algorithm is fully tolerant of in-plane rotation, up to 15-20 degrees of out-of-plane rotation (such as from frontal to profile) and a wide range of changes in scale. It can handle occlusion of up to 50% as long as enough unique edges of the object are still visible. Multiple views can be added to the object model to provide even more reliable recognition or better out-of-plane rotation tolerance. The shape-based algorithm offers enhanced recognition at near real-time performance in many conditions.
The enhanced local-feature-based algorithm in SentiSight 3.0 offers even faster recognition for objects that have clear and stable local features, such as bank notes, brand labels on packaging, logos, etc. In addition to 30-40 times faster recognition speeds (when using a quad-core processor), the "learning" mode, where objects or images are presented to the system, now takes up to 40% less time and the model size is two times smaller. The overall quality of recognition is improved over the previous version, with a 10-30% reduction in the false rejection rate.
SentiSight's shape-based and local-feature-based algorithms can be used together to provide an even higher degree of recognition accuracy and quality when objects have both rich local features and distinguishable edges.
The new tracking algorithm in SentiSight 3.0 provides enhanced tracking of objects in front of complex backgrounds and performs automatic, reliable tracking of fast-moving objects after they have been recognized. The algorithm can track local-feature-rich as well as edge-feature-rich objects.
The SentiSight 3.0 SDK can be used for development of a wide range of applications, including:
- Recognition of documents, stamps, labels, packaging and other items for sorting, logo masking, usage monitoring and similar applications
- Object counting and inspection for assembly lines and other industrial applications
- Augmented and extended reality applications for toys, games, device and Web applications such as: smart toys for children that recognize cards, images, pictograms, etc.; recognition of places based on photographs and recognition of products such as beverages, foods and other consumer goods.
- Robotic vision for navigation and manipulation
- Law enforcement applications for identification, such as tattoo recognition
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