Multi-object Detection
碩士 === 元智大學 === 資訊管理學系 === 96 === This study describes a machine learning approach for visual object detection which is capable of processing images rapidly and achieving high detection rates。 The proposed detector computes the Haar-Like Features at the first stage. In the second stage, a learning...
| Main Authors: | , |
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/90344974336354831204 |
| Summary: | 碩士 === 元智大學 === 資訊管理學系 === 96 === This study describes a machine learning approach for visual object detection which is capable of processing images rapidly and achieving high detection rates。
The proposed detector computes the Haar-Like Features at the first stage. In the second stage, a learning algorithm called Gentle Adaboost is used which selects a small number of critical visual features and yields extremely efficient classifiers.
The final stage of the object detection process is to combine those complex classifiers into something called “Cascade” which allows background regions of the image to be quickly discarded while spending most computation efforts on regions with candidate objects.
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