Active Visual Segmentation
ABSTRACT:
Attention is an integral part of the human visual system and has been widely studied in the visual attention literature. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, we propose a method to segment the object of interest by finding the “optimal” closed contour around the fixation point in the polar space, avoiding the perennial problem of scale in the Cartesian space. The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the “optimal” closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues (edges). In fact, we propose a segmentation refinement process based on such a feedback process. Finally, our experiments show the promise of the proposed method as an automatic segmentation framework for a general purpose visual system.

EXISTING SYSTEM:

Existing there is no fixation point lies inside a particular region of arbitrary shape and size, Only outer boundary segmentation is done .which can either be an entire object or a part of it. Formulate old problem segmentation in a different way and show that existing computational mechanisms in the state-of-the-art computer vision are sufficient to lead us to promising automatic solutions.

PROPOSED SYSTEM:

The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the “optimal” closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues (edges). In fact, we propose a segmentation refinement process based on such a feedback process. Finally, our experiments show the promise of the proposed method as an automatic segmentation framework for a general purpose visual system.

MODULES:

1.     FIXATION-BASED SEGMENTATION
2.     FIXATED REGION
3.     POLAR SPACE METHOD.
4.     MULTIPLE FIXATION-BASED SEGMENTATION


MODULE DESCRIPTION:

FIXATION-BASED SEGMENTATION:
A segmentation framework that takes as its input a fixation (a point location) in the scene and outputs the region containing that fixation the fixated region is segmented in terms of the area enclosed by the “optimal” closed boundary around the fixation using the probabilistic boundary edge map of the scene (or image). The probabilistic boundary edge map, which is generated by using all available visual cues, contains the probability of an edge pixel being at an object (or depth) boundary. The separation of the cue handling from the actual segmentation step is an important contribution of our work because it makes segmentation of a region independent of types of visual cues that are used to generate the probabilistic boundary edge map.

FIXATED REGION:
Fixated region is equivalent to finding the “optimal” closed contour around the fixation point. This closed contour should be a connected set of boundary edge pixels (or fragments) in the edge map. However, the edge map contains both types of edges, namely boundary (or depth) and internal (or texture/intensity) edges. In order to trace the boundary edge fragments in the edge map to form the closed contour enclosing the fixation point, it is important to be able to differentiate between the boundary edges from the non-boundary (e.g., texture and internal) edges.

POLAR SPACE METHOD:

The optimal contour around the red fixation point on the .disc The gradient edge map of the disc, has two concentric circles. The big circle is the actual boundary of the disc, whereas the small circle is just the internal edge on the disc. The edge map correctly assigns the boundary contour intensity as 0.78 and the internal contour 0.39.

MULTIPLE FIXATION-BASED SEGMENTATION:

We have described segmentation for a given fixation. Our objective now is to refine that segmentation by making additional fixations inside the initial segmentation to reveal any thin structures not found in the initial segmentation. Detecting these thin structures can be expensive and complicated if we choose to fixate at every location inside the region. We are going to instead fixate at only a few “salient” locations and incrementally refine the initial segmentations as the new details are revealed.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENTS:

•         System                           :         Pentium IV 2.4 GHz.
•         Hard Disk                      :         40 GB.
•         Floppy Drive                 :         1.44 Mb.
•         Monitor                          :         15 VGA Colour.
•         Mouse                            :         Logitech.
•         Ram                               :         256 Mb.


SOFTWARE REQUIREMENTS:

•         Operating system                     : -      Windows XP Professional.
•         Front End                       : -      Visual Studio.Net 2005
•         Coding Language           : -      Visual C# .Net.

REFERENCE:
Ajay K. Mishra, Yiannis Aloimonos, Loong-Fah Cheong, and Ashraf A. Kassim, Member, IEEE, “Active Visual Segmentation”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 34, NO. 4, APRIL 2012.