METHOD FOR SEGMENTING A SOURCE IMAGE

• US 20140247986A1
• Filed: 11/14/2012
• Published: 09/04/2014
• Est. Priority Date: 11/14/2011
• Status: Abandoned Application

• Current Assignee: Université du Sud Toulon-Var
• Original Assignee: Université du Sud Toulon-Var
• Inventors: Bouchara, Frdric, Lelore, Thibault
• Application Number:
  US13/261,857
• Publication Number:
  US 20140247986A1
• Time in Patent Office: Days
• US Class Current: 382/173
• CPC Class Codes: G06F 18/2415 based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 3/4053 based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects

Abstract

The present invention concerns a method for segmenting a source image containing an object on a background. The invention produces a possibly binary representation according to which each pixel is associated with an attribute indicating that the pixel describes the object or the background. The segmentation of the image is based on a sub-process to estimate the respective probabilities of describing the object rather than the background of pixels of interest alone—i.e. those located in the proximity of transitions between pixel regions describing the object and those describing the background. A representation RV is associated with said image to indicate said probabilities.

Claims

1. Method to classify the pixels of a source image describing an object on a background, said method being carried out by a processing unit of an image segmentation system, said processing unit cooperating with storage means, said method comprising a step to produce and store in said storage means a likelihood representation of said source image associating a likelihood attribute with each pixel of the source image, the value of which corresponds to a probability that said pixel describes the object rather than the background, said step comprising:

a step to initialise the respective values of the probability attributes to a predetermined value indicating that the pixel is undetermined;

a step to detect a transition between first and second pixel regions respectively describing the object and the background, according to a given sensitivity parameter and producing then storing in the storage means a transition representation of the source image associating with each pixel of the source image a transition attribute indicating whether the pixel corresponds to a detected transition or not;

a step to estimate the respective probabilities to describe the object rather than the background of pixels for which the respective distance separating them respectively from a pixel corresponding to a detected transition is less than or equal to a predetermined value and to replace the likelihood attribute values respectively associated with them by said estimates.

2. Method according to claim 1, according to which the likelihood attribute value corresponding to a probability that a pixel describes the object rather than the background is a real number between 0 and 1 and the predetermined value of a likelihood attribute indicating an undetermined pixel is equal to 0.5.

3. Method to classify the pixels of a source image describing an object on a background, said method being performed by a processing unit of an image segmentation system, said processing unit cooperating with storage means, said method comprising a step to produce and store in storage means a consolidated likelihood representation of said source image associating a likelihood attribute with each pixel of the source image, indicating a probability that the pixel describes the object rather than the background, said step consisting in:

a step to perform a first instance of a method according to claim 1 for which the sensitivity parameter is chosen in order to prevent any false detection of transitions, said implementation producing a first likelihood representation of the source image;

a step to perform a second instance of a method according to claim 1 for which the sensitivity parameter is chosen in order to prevent any transitions going undetected, said implementation producing a second likelihood representation of the source image;

a step to produce and store the consolidated likelihood representation of said source image assigning the respective likelihood attribute values of the first likelihood representation to each likelihood attribute of said consolidated representation then replacing the likelihood attribute values of the consolidated representation with the likelihood attribute values of the second likelihood representation if, and only if, said values are strictly higher than a determined threshold.

4. Method to classify the pixels of a source image, said method being carried out by a processing unit of an image segmentation system, said processing unit cooperating with storage means storing first and second source images having been captured by capture means displaced a non-zero displacement distance between the two captures, said method comprising a step to produce and store in the storage means a consolidated likelihood representation of the first source image associating a likelihood attribute with each pixel of said image indicating a probability that the pixel describes the object rather than the background, said step consisting in:

a step to perform a first instance of a method according to claim 1 to produce a likelihood representation of the first source image;

a step to perform a second instance of a method according to claim 1 to produce a likelihood representation of the second source image;

a step to estimate the displacement distance and to determine the correspondence between the pixels of the two images;

a step to produce and store the consolidated likelihood representation assigning the respective likelihood attribute values of the first likelihood representation to each likelihood attribute of said consolidated representation, then replacing the likelihood attribute values of the consolidated representation by a linear combination of the likelihood attribute values corresponding to the first and second likelihood representations.

5. Method according to claim 4, according to which it comprises a prior step to increase the resolution of the two images by interpolation and to respectively replace the source images by the interpolated images.

6. Method according to claim 1 comprising a step to produce a filtered likelihood representation, said step comprising:

a step to interpret the likelihood representation and to identify all directly adjacent pixel pairs, the first of which describes the object and the second being undetermined;

a step to initialise a transition representation of the source image in which only the values of transition attributes respectively associated with pixels of the source image respectively adjoining said pixel pair, as well as those of the values of the transition attributes associated with said pixel pair, indicate that the pixels correspond to a detected transition;

a step to estimate the respective probabilities to describe the object rather than the background of pixels respectively associated with transition attributes indicating that said pixels correspond to a detected transition and for which the distance—

respectively separating them from one of the pixels for which the value of the transition attribute indicates that said pixel corresponds to a detected transition—

is less than or equal to a predetermined value and to replace the likelihood attributes respectively associated with them by said estimates.

7. Method to segment a source image describing an object on a background, said method being carried out by a processing unit of an image segmentation system, said processing unit cooperating with storage means, said method comprising a step to produce a binary representation of the source image associating an attribute with each pixel of said source image the value of which is a predetermined value associated with the background or a predetermined value associated with the object, said method comprising:

a step to classify the pixels of the source image according to a method according to claim 1 to obtain a likelihood representation of the source image associating a likelihood attribute with each pixel of the source image the value of which corresponds to the probability that said pixel describes the object and not the background;

a step to characterise a region of respectively connected pixels associated with likelihood attributes indicating that they are undetermined, replacing the values of said likelihood attributes by the average of the likelihood attribute values respectively associated with the boundary pixels for which the respective likelihood attribute values are different from the value indicating indeterminacy.

8. Method according to claim 7 comprising a step thresholding the likelihood representation obtained to produce the binary representation assigning to its attributes predetermined values respectively associated with the background or with the object when the values of the corresponding likelihood attributes are strictly less than the background threshold or greater than the object threshold.

9. Method according to claim 8 according to which, the predetermined values respectively associated with the background and the object may be 0 and 1.

10. Method according to claim 8 according to which, the background threshold and the object threshold are respectively set at 0.5.

11. Computer program comprising a plurality of instructions operable by a processing unit of a segmentation system, said program being intended to be stored in storage means cooperating with said processing unit, wherein said instructions trigger the performance of a method according to claim 1 when executed or interpreted by the processing unit.

12. Non-volatile memory means wherein it holds the instructions of a computer program according to claim 11.