Computer Vision System Toolbox™ provides pretrained object detectors and the functionality to train a custom detector. The cascade object detector uses the Viola-Jones algorithm to detect people's faces, noses, eyes, mouth, or upper body. The people detector detects people in an input image using the histogram of oriented gradients (HOG) features and a trained support vector machine (SVM) classifier.
You can customize the cascade object detector using the
|Detect objects using aggregate channel features|
|Detect people using aggregate channel features|
|Detect objects using the Viola-Jones algorithm|
|Foreground detection using Gaussian mixture models|
|Detect upright people using HOG features|
|Properties of connected regions|
|Detect BRISK features and return BRISKPoints object|
|Detect corners using FAST algorithm and return cornerPoints object|
|Detect corners using Harris–Stephens algorithm and return cornerPoints object|
|Detect KAZE features|
|Detect corners using minimum eigenvalue algorithm and return cornerPoints object|
|Detect MSER features and return MSERRegions object|
|Detect SURF features and return SURFPoints object|
|Extract interest point descriptors|
|Find matching features|
|Evaluate miss rate metric for object detection|
|Evaluate precision metric for object detection|
|Convert rectangle to corner points list|
|Compute bounding box overlap ratio|
|Compute bounding box precision and recall against ground truth|
|Select strongest bounding boxes from overlapping clusters|
|Select strongest multiclass bounding boxes from overlapping clusters|
Interactively label rectangular ROIs for object detection, pixels for semantic segmentation, and scenes for image classification.
Choose functions that return and accept points objects for several types of features
Specify pixel Indices, spatial coordinates, and 3-D coordinate systems
Learn the benefits and applications of local feature detection and extraction
Train a custom classifier
Retrieve images from a collection of images similar to a query image using a content-based image retrieval (CBIR) system.
Use the Computer Vision Toolbox™ functions for image category classification by creating a bag of visual words.