@Namespace(value="cv::saliency") public static class opencv_saliency.StaticSaliency extends opencv_saliency.Saliency
| Constructor and Description |
|---|
StaticSaliency(org.bytedeco.javacpp.Pointer p)
Pointer cast constructor.
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| Modifier and Type | Method and Description |
|---|---|
boolean |
computeBinaryMap(opencv_core.GpuMat _saliencyMap,
opencv_core.GpuMat _binaryMap) |
boolean |
computeBinaryMap(opencv_core.Mat _saliencyMap,
opencv_core.Mat _binaryMap)
\brief This function perform a binary map of given saliency map.
|
boolean |
computeBinaryMap(opencv_core.UMat _saliencyMap,
opencv_core.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, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zeropublic StaticSaliency(org.bytedeco.javacpp.Pointer p)
Pointer.Pointer(Pointer).@Cast(value="bool")
public boolean computeBinaryMap(@ByVal
opencv_core.Mat _saliencyMap,
@ByVal
opencv_core.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
opencv_core.UMat _saliencyMap,
@ByVal
opencv_core.UMat _binaryMap)
@Cast(value="bool")
public boolean computeBinaryMap(@ByVal
opencv_core.GpuMat _saliencyMap,
@ByVal
opencv_core.GpuMat _binaryMap)
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