Improved Image Segmentation Techniques Based on Reflex Angles, Shadow Compensation, and Salient Region Detection
Date Issued
2011
Date
2011
Author(s)
Tsai, Chia-Hao
Abstract
Image segmentation is a big challenging task in image processing. Moreover, it isvery important pre-processing in image analysis and pattern recognition, and determinesthe quality of the final result of analysis. A process of partitioning an image into different non-overlapping homogeneous regions is called image segmentation, where
the homogeneous regions may be composed based on different criteria such as gray-level, color or texture. Therefore, we hope to find and propose some new segmentation algorithms for satisfying various requirements.
In the thesis, first of all, we briefly introduce the fast scanning algorithm that eachpixel is processed only once. Based on the fast scanning algorithm, we change its
distance interval and add in the edge information which can make the two original inseparable regions divide into two different regions. The simulation results show that our algorithm makes the segmentation result become be better.
From human’s point of view, the segmentation result of the existing segmentation algorithm is not good enough, or to be precise, its ability for matching physical objects
is not good. Here we combine the reflex angle operation with the method of finding representative points which a simple method. The experimental results show that the
improved method improves the existing segmentation algorithm’s the ability for matching physical objects. Moreover, the processing time of our algorithm is faster than using the morphology operation.
Due to variance in the brightness on the surfaces of the objects under consideration, shadows and highlights present a challenge to the computer vision researchers. Shadows
interfere with fundamental tasks in many image analysis and interpretation applications such as object extraction and description. Furthermore, owing to the great variation in
image measurements caused by the geometry of the object, shadows, and specularities, the segmentation of a single material reflectance is a quite challenging problem.
In a word, shadow segmentation is an important step in image analysis. We propose normalized method to solve the shadow and highlight problem based on the fast scanning algorithm. The simulation results show that our algorithm can successfully help us to segment the shadowed images. Moreover, the completeness of the segmentation results of the object and the background of the shadowed images are
better for our algorithm than the other existing algorithms.
Salient region detection is used in many applications, such as object recognition, adaptive compression, and object segmentation. We introduce a method for salient
region detection in the thesis, and use the Ridge-based Analysis of Distributions (RAD) and the fast scanning algorithm for the pre-segmentation algorithm. The RAD algorithm combines the strengths of physics with feature-based methods, and is based on the
observation that the distribution of single material reflectance.
Finally, we compare the salient object segmentation result of using the RAD and the fast scanning algorithm with the mean shift segmentation algorithm and calculate
the precision and recall by means of the ground truth image database. The experimental results show that no matter using fixed or adaptive parameters, the recall values of using the fast scanning algorithm are better than using the RAD or mean shift segmentation algorithm under the close precision value condition.
Subjects
image segmentation
image processing
image analysis
pattern recognition
edge information
reflex angle
morphology
shadow segmentation
salient region detection
object recognition
adaptive compression
object segmentation
Type
thesis
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