@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 |
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StaticSaliency(Algorithm pointer)
Downcast constructor.
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StaticSaliency(Pointer p)
Pointer cast constructor.
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StaticSaliency(Saliency pointer)
Downcast constructor.
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Modifier and Type | Method and Description |
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Saliency |
asSaliency() |
static Saliency |
asSaliency(StaticSaliency pointer) |
boolean |
computeBinaryMap(GpuMat _saliencyMap,
GpuMat _binaryMap) |
boolean |
computeBinaryMap(Mat _saliencyMap,
Mat _binaryMap)
\brief This function perform a binary map of given saliency map.
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boolean |
computeBinaryMap(UMat _saliencyMap,
UMat _binaryMap) |
asAlgorithm, asAlgorithm, computeSaliency, computeSaliency, computeSaliency
clear, empty, getDefaultName, getPointer, position, read, save, save, write, write, write
address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, getDirectBufferAddress, getPointer, getPointer, getPointer, hashCode, interruptDeallocatorThread, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetAddress, offsetof, offsetof, parseBytes, physicalBytes, physicalBytesInaccurate, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, sizeof, toString, totalBytes, totalCount, totalPhysicalBytes, withDeallocator, zero
public StaticSaliency(Pointer p)
Pointer(Pointer)
.public StaticSaliency(Saliency pointer)
public StaticSaliency(Algorithm pointer)
public Saliency asSaliency()
@Namespace @Name(value="static_cast<cv::saliency::Saliency*>") public static Saliency asSaliency(StaticSaliency 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)
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