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SentiSight Embedded

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SentiSight Embedded

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.

SentiSight Embedded is available as a software development kit that provides for the development of object recognition applications for the devices that are running Android OS with an 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.

OBJECT LEARNING AND RECOGNITION PROCESSES

SentiSight Embedded has two operation modes: Learning and Recognition. In learning mode, the SentiSight algorithm creates an object model by extracting object features from an image or video. In recognition mode, SentiSight finds and tracks objects with features matching those previously stored in object models.

Object Learning Process

In order to recognize an object in an image, the appearance of an object must fi rst be catalogued. In the learning phase, SentiSight algorithms extract specifi c object features from a video stream or single image and save them into an object’s model.

In many cases there is information in a video or single image beyond the features of an object you wish SentiSight to learn such as a background, other objects in the room, a hand holding the target object. For SentiSight to properly learn an object, information about the exact location of the object within the image should be provided.

SentiSight supports 2 methods of object learning:

Manual and Automatic.

Manual object learning

This is suitable for most situations. A user performs the following steps for manual object learning in the SentiSight-based application:

  1. Outline an object’s shape on an image by marking the object’s corner points to build a polygon. The image can be provided from an image file, video file or live video stream.
  2. Select the algorithm to use: blob-based, shape-based, or both.
  3. As an option, additional images of the object may be provided, repeating Step 1 for each image. The algorithm assists the user by estimating an approximate shape for the object if the image is recognized by way of previously catalogued images. Learning the object from different sides and angles results in better recognition quality.
  4. Input the learned object name (ID) into the system. Automatic object learning is suitable for lightweight, movable objects. This learning procedure is based on detecting an object through the exclusion of a static background and the object’s holder (usually a hand). A fixed camera is highly recommended for this process.

Automatic object learning

A user must perform these steps for automatic object learning in the SentiSight-based application:

  1. Select a background and position the camera.
  2. Select a holder (an object that will be used to hold and move the object to be learned). A user’s hand can be the “holder”.
  3. The “holder,” if it is not a rigid onject, should be presented to the camera in various poses and confi gurations so SentiSight can learn it.
  4. Select the algorithm to use: blob-based, shape-based, or both.
  5. After the holder has been learned, SentiSight is ready to learn the object itself. Use the holder to rotate and move the object, both closer to, and further from, the camera.
  6. Input the learned object name (ID) into the system.

The automatic method requires the use of live video or separate video / image sets of background, holder andobject. Other background elements may be learned together with the object if the object is hardly separable from the background. Too little disparity between object and background, if not learned together, may affect the ability of the algorithm to recognize an object’s unique qualities, possibly resulting in the object being misclassifi ed along with other objects having the same background.

Manual object learning should be used for objects that cannot be moved or if there is no way to provide separate media of an object’s background and/or holder. Automatic learning requires less user interaction with the system, but it is not as precise as manual learning. Manual learning is suitable, generally, for a wider range of cases.

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