@Namespace(value="cv::saliency") @Properties(inherit=opencv_saliency.class) public class StaticSaliency extends Saliency
Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter| Constructor and Description |
|---|
StaticSaliency(Pointer p)
Pointer cast constructor.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
computeBinaryMap(GpuMat _saliencyMap,
GpuMat _binaryMap) |
boolean |
computeBinaryMap(Mat _saliencyMap,
Mat _binaryMap)
\brief This function perform a binary map of given saliency map.
|
boolean |
computeBinaryMap(UMat _saliencyMap,
UMat _binaryMap) |
computeSaliency, computeSaliency, computeSaliencyclear, empty, getDefaultName, position, read, save, save, write, write, writeaddress, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zeropublic StaticSaliency(Pointer p)
Pointer.Pointer(Pointer).@Cast(value="bool") public boolean computeBinaryMap(@ByVal Mat _saliencyMap, @ByVal Mat _binaryMap)
In a first step, to improve the definition of interest areas and facilitate identification of targets, a segmentation by clustering is performed, using *K-means algorithm*. Then, to gain a binary representation of clustered saliency map, since values of the map can vary according to the characteristics of frame under analysis, it is not convenient to use a fixed threshold. So, Otsu's algorithm* is used, which assumes that the image to be thresholded contains two classes of pixels or bi-modal histograms (e.g. foreground and back-ground pixels); later on, the algorithm calculates the optimal threshold separating those two classes, so that their intra-class variance is minimal.
_saliencyMap - the saliency map obtained through one of the specialized algorithms_binaryMap - the binary map@Cast(value="bool") public boolean computeBinaryMap(@ByVal UMat _saliencyMap, @ByVal UMat _binaryMap)
Copyright © 2020. All rights reserved.