Recognizing objects in images

By | 13.07.2017

For a long time “PAWLIN Technologies” company develops algorithms and software for automatic selection of objects in images using a number of computer vision and pattern recognition methods for russian and foreign customers.

Goal

Automate the process of manual selection of specified classes in the image to provide the customers products with a new functionality or to increase the productivity of operators involved in manual image processing.

Input data

The customer provides a representative sample of images containing and not containing the specified objects, or formulates different description of specified objects, for example, providing single standards. Optionally, the customer provides a priori information about the desired objects — the degree of variability of their shape, subclasses, possible lighting conditions, camera angles, etc. It is possible to manually create a training sample by company specialists using open sources or customer’s materials. In some cases, it is possible to generate a training sample using an imitation model of objects themselves or to increase their number by artificial changes in their shape.

Output

As a result, the algorithm and its software implementation, that ensures the task of detecting a given class of objects in images, are developed. In order to achieve certain processing speed requirements, can be applied acceleration of computations on graphic or multi-core processors. A software implementation for single-board computers (embedded) is possible.

Examples of developed detectors:

  • search for reference points of a specialized calibration board, ensuring unique determination of its orientation;
  • peoples faces detector;
  • search for logos (trade marks) in video stream frames and photo images;
  • search for similar frames (for pirated video);
  • search for strange inclusions in frozen food products based on analysis of X-ray images (flaw detection);
  • human head and shoulders detector;
  • car detector (in aerial photos);
  • road detector (in aerial photos);
  • rectangular objects detector (in aerial photos);
  • ships detector (in aerial photos);
  • animal (moose) detector (in aerial photos);
  • flags and cards detector (in robotics);
  • digital signature detector;
  • cloud detector;
  • forgotten objects detector.

For specialists

Caffe; CNN; MLP; MSER; SIFT; SOM; Connected Components; GPU; OpenCL; OpenMP; OpenCV

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