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:param image: image or set of input images. According to InputArray proxy and to the needs of different algorithms (currently plugged), the param image may be Mat or vector<Mat>
:param saliencyMap: saliency map. According to OutputArray proxy and to the results given by different algorithms (currently plugged), the saliency map may be a Mat or vector<Vec4i> (BING results).
:param image: image or set of input images. According to InputArray proxy and to the needs of different algorithms (currently plugged), the param image may be *Mat* or *vector<Mat>*
:param saliencyMap: saliency map. According to OutputArray proxy and to the results given by different algorithms (currently plugged), the saliency map may be a *Mat* or *vector<Vec4i>* (BING results).
:param image: image or set of input images. According to InputArray proxy and to the needs of different algorithms (currently plugged), the param image may be Mat or vector<Mat>
:param saliencyMap: saliency map. According to OutputArray proxy and to the results given by different algorithms (currently plugged), the saliency map may be a Mat or vector<Vec4i> (BING results).
Algorithms belonging to this category, are particularly focused to detect salient objects over time (hence also over frame), then there is a temporal component sealing cosider that allows to detect "moving" objects as salient, meaning therefore also the more general sense of detection the changes in the scene.
Objectness is usually represented as a value which reflects how likely an image window covers an object of any category. Algorithms belonging to this category, avoid making decisions early on, by proposing a small number of category-independent proposals, that are expected to cover all objects in an image. Being able to perceive objects before identifying them is closely related to bottom up visual attention (saliency)
Presently, the Binarized normed gradients algorithm [BING]_ has been implemented.
.. [BING] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014.
ObjectnessBING
--------------
.. ocv:class:: ObjectnessBING
Implementation of BING from :ocv:class:`Objectness`::
class CV_EXPORTS_W ObjectnessBING : public Objectness
Return the list of the rectangles' objectness value, in the same order as the *vector<Vec4i> objectnessBoundingBox* returned by the algorithm (in computeSaliencyImpl function).
The bigger value these scores are, it is more likely to be an object window.
:param image: input image. According to the needs of this specialized algorithm, the param image is a single *Mat*
:param saliencyMap: objectness Bounding Box vector. According to the result given by this specialized algorithm, the objectnessBoundingBox is a *vector<Vec4i>*.
Each bounding box is represented by a *Vec4i* for (minX, minY, maxX, maxY).
Many computer vision applications may benefit from understanding where humans focus given a scene. Other than cognitively understanding the way human perceive images and scenes, finding salient regions and objects in the images helps various tasks such as speeding up object detection, object recognition, object tracking and content-aware image editing.
About the saliency, there is a rich literature but the development is very fragmented. The principal purpose of this API is to give a
unique interface, a unique framework for use and plug sever saliency algorithms, also with very different nature and methodology, but they share the same purpose, organizing algorithms into three main categories:
**Static Saliency**: algorithms belonging to this category, exploit different image features that allow to detect salient objects in a non dynamic scenarios.
**Motion Saliency**: algorithms belonging to this category, are particularly focused to detect salient objects over time (hence also over frame), then there is a temporal component sealing cosider that allows to detect "moving" objects as salient, meaning therefore also the more general sense of detection the changes in the scene.
**Objectness**: Objectness is usually represented as a value which reflects how likely an image window covers an object of any category. Algorithms belonging to this category, avoid making decisions early on, by proposing a small number of category-independent proposals, that are expected to cover all objects in an image. Being able to perceive objects before identifying them is closely related to bottom up visual attention (saliency).
UML design:
-----------
**Saliency diagram**
.. image:: pics/saliency.png
:width: 80%
:alt: Saliency diagram
:align: center
To see how API works, try tracker demo: https://github.com/fpuja/opencv_contrib/blob/saliencyModuleDevelop/modules/saliency/samples/computeSaliency.cpp
.. note:: This Tracking API has been designed with PlantUML. If you modify this API please change UML files under modules/tracking/misc/
The following reference was used in the API
.. [SR] Hou, Xiaodi, and Liqing Zhang. "Saliency detection: A spectral residual approach." Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 2007.
.. [BING] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014.
This function perform a binary map of given saliency map. This is obtained in this way:
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@@ -32,10 +32,10 @@ by clustering is performed, using *K-means algorithm*. Then, to gain a binary re
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
Base classes which give a general interface for each specialized type of saliency algorithm and provide utility methods for each algorithm in its class.
StaticSaliency
--------------
.. ocv:class:: TrackerSampler
StaticSaliency class::
class CV_EXPORTS_W StaticSaliency : public virtual Saliency
This function perform a binary map of given saliency map. This is obtained in this way:
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
@@ -11,7 +11,7 @@ Presently, the Spectral Residual approach [SR]_ has been implemented.
SpectralResidual
------------------------------
----------------
Starting from the principle of natural image statistics, this method simulate the behavior of pre-attentive visual search. The algorithm analyze the log spectrum of each image and obtain the spectral residual. Then transform the spectral residual to spatial domain to obtain the saliency map, which suggests the positions of proto-objects.
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@@ -30,7 +30,7 @@ Implementation of SpectralResidual from :ocv:class:`StaticSaliency`::
Algorithms belonging to this category, exploit different image features that allow to detect salient objects in a non dynamic scenarios.
Presently, the Spectral Residual approach [SR]_ has been implemented.
.. [SR] Hou, Xiaodi, and Liqing Zhang. "Saliency detection: A spectral residual approach." Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 2007.
StaticSaliencySpectralResidual
------------------------------
Starting from the principle of natural image statistics, this method simulate the behavior of pre-attentive visual search. The algorithm analyze the log spectrum of each image and obtain the spectral residual. Then transform the spectral residual to spatial domain to obtain the saliency map, which suggests the positions of proto-objects.
.. ocv:class:: StaticSaliencySpectralResidual
Implementation of SpectralResidual from :ocv:class:`StaticSaliency`::
class CV_EXPORTS_W StaticSaliencySpectralResidual : public StaticSaliency
