Contribution to the image segmentation methods and machinevision tracking: of multiple objects in imagesequences,Used

Contribution to the image segmentation methods and machinevision tracking: of multiple objects in imagesequences,Used

In Stock
SKU: DADAX384431489X
Brand: LAP Lambert Academic Publishing
Condition: New
$106.33
Quantity
Add to wishlist
Add to compare
Sold by Ergodebooks, an authorized reseller.

Processing time: 1-3 days

US Orders Ships in: 3-5 days

International Orders Ships in: 8-12 days

Return Policy: 15-days return on defective items

Payment Option
Payment Methods

Help

If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.

Customer service

All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com

Sale & Press

If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com

In this research work, several visionbased algorithms have been proposed for automatic detection, classification and tracking of multiple mobile objects as well as for automatic detection of stationary obstacles appearing in a sequence of images taken with an overhead camera. An adaptive background subtraction technique that models each pixel as a mixture of Gaussians has been developed for motion activity detection of dynamic objects, mobile robots and human beings moving in the scene. The computational cost and the memory requirement have been considerably reduced. The classification of dynamic objects as rigid or nonrigid has been based on the homogeneity analysis of the motion field. The rigiditybased classification of dynamic objects is aimed to avoid collisions of interfering humans with mobile robots, whereas identifying stationary obstacles is aimed to prevent collisions of dynamic objects with such obstacles. The identification of stationary obstacles has been based on separating the textured background of the scene. Good results have been obtained over the imageframe sequence and the method can run in realtime on a Pentium class computer.

⚠️ WARNING (California Proposition 65):

This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

For more information, please visit www.P65Warnings.ca.gov.

Recently Viewed