The objectdetection using the Artificialintelligence.pptx

It is the information about how object detection is work and how he perform the detection.

1. Marathwada Shikshan Prasarak Mandal’s Shri Shivaji Institute of Engineering and Management Studies, Parbhani

2. Content 

3. What is Object Detection? Detecting a specified object class within a image. Object detection has many applications in computer based vision. Object tracking, object recognition, scene surveillance. The focus of this project was to implement object detection, and to detect objects of the class face.

4. How Is It Done? A standard pattern recognition problem. Feature extraction. Something that can be representative of a face. Feature evaluation. Does this something really represent a face. A bit of a black art Classification. Given a sample and its features, what is it?

5. Strong focuses on statistics. Statistical models of images. Schneiderman-Kanade A lot of work with Neural networks. Generally slow systems. Rowley- Balauja Feature and Template methods seem to be the most common. Common Techniques

6. Features of Good Techniques Quick to compute. Classification of a face does not require a lot of offline processing. Accurate. Most good implementations can provide accuracy above the 90 percentile. Capitalization on invariance. Features are invariant. Scale, luminance, rotation.

7. Features Four basic types. They are easy to calculate. The white areas are subtracted from the black ones. A special representation of the sample called the integral image makes feature extraction faster.

8. Feature Extraction Features are extracted from sub windows of an sample image. The base size for a sub window is 24 by 24 pixels. In a 24 pixel by 24 pixel sub window there are 180,000 possible features to be calculated. What is the end result of feature extraction? A lot of data! This is called over fitting and the amount of data must be reduced. Overfitting can be compensated to an extent by logical elimination.