SuperpixelLSC Methods

Pins the object to be allocated by cvSetData.

Allocates the specified size of memory.

Class implementing the LSC (Linear Spectral Clustering) superpixels. The function initializes a SuperpixelLSC object for the input image. It sets the parameters of superpixel algorithm, which are: region_size and ruler.It preallocate some buffers for future computing iterations over the given image.An example of LSC is illustrated in the following picture. For enhanced results it is recommended for color images to preprocess image with little gaussian blur with a small 3 x 3 kernel and additional conversion into CieLAB color space.

Releases the resources

Releases the resources

Releases managed resources

releases unmanaged resources

Enforce label connectivity. The function merge component that is too small, assigning the previously found adjacent label to this component.Calling this function may change the final number of superpixels.

Determines whether the specified object is equal to the current object.

Destructor

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Serves as the default hash function.

Returns the mask of the superpixel segmentation stored in SuperpixelLSC object. The function return the boundaries of the superpixel segmentation.

Returns the segmentation labeling of the image. Each label represents a superpixel, and each pixel is assigned to one superpixel label. The function returns an image with the labels of the superpixel segmentation.The labels are in the range [0, getNumberOfSuperpixels()].

Calculates the actual amount of superpixels on a given segmentation computed and stored in SuperpixelLSC object.

Gets the Type of the current instance.

Calculates the superpixel segmentation on a given image with the initialized parameters in the SuperpixelLSC object. This function can be called again without the need of initializing the algorithm with createSuperpixelLSC(). This save the computational cost of allocating memory for all the structures of the algorithm. The function computes the superpixels segmentation of an image with the parameters initialized with the function createSuperpixelLSC(). The algorithms starts from a grid of superpixels and then refines the boundaries by proposing updates of edges boundaries.

Creates a shallow copy of the current Object.

Notifies the allocated size of memory.

Reads algorithm parameters from a file storage

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage fs).

If this object is disposed, then ObjectDisposedException is thrown.

Returns a string that represents the current object.

Stores algorithm parameters in a file storage