SentiSight Embedded SDK

Object recognition for moble computer vision applications

SentiSight Embedded is designed for developers who want to use computer vision-based object recognition in their applications for smartphones, tablets and other mobile devices. Through manual or fully automatic object learning it enables searching for learned objects in images or videos from built-in cameras with PC-like accuracy.

Available as a software development kit that provides for the development of object recognition applications for the devices that are running Android OS.

Features and Capabilities

  • Innovative algorithm, that is tolerant to appearance, object scale, rotation and pose.
  • PC-level accuracy of object detection and processing with mobile devices.
  • Smatphone built-in cameras are suitable for obtaining object images.
  • Compatability and interoperability with PC-side SentiSight-based products.
  • Reasonable prices, flexible licensing and free customer support.

SentiSight is designed to be as universal as possible and is able to perform fully automatic or manual object learning. SentiSight Embedded technology can be used for a wide range of tasks, including:

  • recognition of documents, stamps, labels, packaging and other items for sorting, usage monitoring and similar applications;
  • object counting and inspection;
  • augmented and extended reality applications for toys, games, devices and Web applications such as: smart toys for children that recognize cards, images, pictograms, etc.; recognition of places based on photographs; recognition of products such as beverages, foods and other consumer goods;
  • law enforcement applications for identification, such as tattoo recognition.

The SentiSight Embedded 1.3 technology has these capabilities for visual-based object learning and recognition on mobile and embedded devices:

  • Accurate object detection. The SentiSight algorithm is able to find out:
    • whether a particular object is present in a scene;
    • where the object is located within the scene;
    • how many instances of the object occur in the scene.
  • Two algorithms for object recognition. Depending on the object type, one of these algorithms (or both) may be used for successful recognition:
    • The blob-based algorithm uses small details of an object as distinctive features that are extracted into an object model and are used later to recognize the object. This algorithm offers performance but is not suitable for solid-colored, reflecting or transparent (glass, etc.) objects.
    • Shape recognition
      SentiSight shape recognition screenshots thumbnail Click to zoom
      The shape-based algorithm is useful for objects which do not have any distinctive details but have stable external edges (boundaries) and/or internal edges. This algorithm performs at slower speeds but allows for the recognition of most objects not identified by the blob-based algorithm.
  • Color usage mode. The blob-based and shape-based algorithms may be configured to detect object colors and use this information for improving recognition accuracy. This mode enables SentiSight-based applications to distinguish similar objects that only differ in color.
  • Object's image quality determination. A quality threshold can be used during object learning to ensure that only the best quality object model will be stored into database.
  • Simultaneous multiple object recognition. The SentiSight Embedded is able to detect and recognize several 2D and 3D objects simultaneously.
  • Object evaluation. The algorithm makes estimates based on the region an object occupies in a scene, providing additional information about the size, orientation and scale of the recognized object.
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