Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: The purpose of this research is to compare the original
intra-oral digital dental radiograph images with images that are
enhanced using a combination of image processing algorithms. Intraoral
digital dental radiograph images are often noisy, blur edges and
low in contrast. A combination of sharpening and enhancement
method are used to overcome these problems. Three types of
proposed compound algorithms used are Sharp Adaptive Histogram
Equalization (SAHE), Sharp Median Adaptive Histogram
Equalization (SMAHE) and Sharp Contrast adaptive histogram
equalization (SCLAHE). This paper presents an initial study of the
perception of six dentists on the details of abnormal pathologies and
improvement of image quality in ten intra-oral radiographs. The
research focus on the detection of only three types of pathology
which is periapical radiolucency, widen periodontal ligament space
and loss of lamina dura. The overall result shows that SCLAHE-s
slightly improve the appearance of dental abnormalities- over the
original image and also outperform the other two proposed
compound algorithms.