Download as ODP, PPTX
Uploaded byBeatrice van Eden
Machine learning group - Practical examples
The document summarizes examples of machine learning applications presented by a machine learning group. It discusses number plate recognition using SVM and neural networks for plate detection and recognition. It also discusses face recognition using Eigenfaces or Fisherfaces methods including face detection, preprocessing, training an algorithm, and recognition. Code examples are provided for both number plate recognition and face recognition.
More Related Content
Similar to Machine learning group - Practical examples
Machine learning group - Practical examples
- 1. Machine learning group Practical examples Scope We have covered some basic theory in our machine learning group but only a few of us have applied any of the tools. The next few weeks we are presenting some practical examples.
- 2. 2 Content Practicalexamples 1. Numberplate recognition - Method - Code 2. Face recognition - Method - Code Material from the book - Mastering OpenCV with practical computer vision examples, was used for this presentation.
- 3. 3 Examples 1.Numberplate recognition Method SVM and Neural Networks 1. Plate detection - Segmentation - Feature extraction - Classification - Results 2. Plate recognition - Segmentation - Feature extraction - Classification - Results
- 4.
- 5. 5 1. Platedetection Examples - Segmentation - Feature extraction - Classification - Results
- 6. Sobel filter Thresholdoperation Close morphologic operation Mask of one filled area Detected plates after the 6 Possible detected plates marked in red (features images) SVM classifier Examples
- 7. 7 2. Platerecognition Examples - Segmentation (OCR segmentation) - Feature extraction - Classification - Results
- 8.
- 9. 9 1. Numberplaterecognition Code Examples
- 10. 10 Examples 2.Face recognition Method Eigenfaces or Fisherfaces 1. Face detection 2. Face preprocessing 3. Training a machine-learning algorithm from collected faces 4. Face recognition
- 11. 11 Examples 2000– Slow and Unreliable face detection 2001 – Viola and Jones invented Haar-based cascade classifiers for object Detection. Big improvement especialy for real time application. 2002 – Improved buy Lienhard and Maydt. 2006 – LBP features by Ahonen Hadrd (Faster than Haar based features) LBP similar to Haar but uses histograms of pixel intensity comparisons.
- 12. 12 Examples Violaand Jones 1. Grey scale image – intensity values from 0 to 255. 2. The feature value is calculated as the sum of the pixel intensities in the light rectangle(s) minus the sum of the pixels in the dark rectangle(s) 3. These adjacent blocks are known as ’features’.The value of the feature is then used in a filter to determine if that feature is present in the original image 4. To make summing the intensity of the pixels in a given rectangle less computationally expensive and improve the speed, the integral image gets calculated (finding the area under a curve by adding together small rectangular areas) at every point. 5. Learning. When there is no correlation between the feature identify-ing a face and it not being one, and vice versa it would be rejected.
- 13. Type of cascadeclassifier XML filename – OpenCV v2.4 Face detector (default) haarcascade_frontalface_default.xml Face detector (fast Haar) haarcascade_frontalface_alt2.xml Face detector (fast LBP) lbpcascade_frontalface.xml Profile (side-looking) face detector haarcascade_profileface.xml Eye detector (separate for left and right) haarcascade_lefteye_2splits.xml Mouth detector haarcascade_mcs_mouth.xml Nose detector haarcascade_mcs_nose.xml Whole person detector haarcascade_fullbody.xml 13 Examples
- 14. 14 Examples Geometrical transformation and cropping Separate histogram equalization for left and right sides Smoothing bilateral filter Elliptical mask
- 15. 15 Examples EigenFaces Fisher Faces
- 16. 16 Examples 2.Face recognition Code
- 17. Thank you Name(email@csir.co.za)
Editor's Notes
- #18 <number>