We develop custom software that identifies objects in photos and videos.
This approach is most appropriate if the object you want to identify has specific, distinctive attributes that are easily identified.
For example, identify a part in a metal shop based on its shape and the location of holes for screws. Or identifying a PC board using the placement of components as well as their shapes and other patterns. Or identifying Lego pieces from their colors, shapes, and the number of rows and columns of embossed circles present.
Machine Learning Approach
This approach is most appropriate when the variations in the visual appearance of a particular object are diverse (even after correcting for different camera angles) and a large number of sample images is available to learn from. For example, recognition of specific faces, plants and animals where a very high number of parameters go into making a definitive identification.
The sample images used for learning need to be representative of both the object and the environment in which the object will be recognized.
This approach is based mainly on statistics. For this reason, the choice of samples used for learning will have a major impact on its accuracy.
When aspects of both approaches come into play, a hybrid approach is called for.
Our technology identifies faces and objects in video. Frame by frame, it records the x, y coordinates of its findings and displays a bounding box around the found face or object.
As an implementation of Recognition Technology, our software learns to recognize a face or object using an initial training set of sample images. As it analyzes this training set, it computes factors that are likely to make the face or object unique and uses these factors to create a learning profile of the item for future recognition.
To improve its accuracy, our solution also uses Tracking Technology and User Assistance where the user has the option to correct mistakes.
The software tracks each item it finds in the video. It uses the findings in one frame to identify faces or objects in the next and previous frames even if the object's appearance changes slightly from frame to frame. For example, when a person turns his head or smiles.
When the initial, automated detection completes, the user has the option to confirm the findings. If any errors are found, the user can correct them with an easy-to-use interface.
The results of Tracking and User Assistance allow the system to update its learning profile for future use.
By combining Recognition Technology, Tracking Technology, and User Assistance, our solutions identify and track faces and objects in video with a very high degree of accuracy over what Recognition Technology can accomplish alone.
Security and surveillance systems; medical diagnostics; quality control; inventory control; image search and object identification; matching image detection; finding similar images in video or photos; motion tracking of objects; image matching across multiple content sources; human factors; psychology; human performance measurement; neuroscience; handwriting analysis
Please contact us to talk about 3D and Computer graphics software applications tailored to your or your clients' needs.
Call (408) 980-7332 or e-mail info@ImageGraphicsVideo.com.