O. Juina 1, SC Hu2 and T.Lin3
1Department of Mechanical and Automation Engineering, National Taipei University of Technology, Taipei, 10608 Taiwan,R.O.C
2,3Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology,
Taipei, 10608 Taiwan, R.O.C
In the field of clean room systems, the need to have high standards of cleaning and environmental
control has generated the creation of new equipment which can solve the different problems of
monitoring in particle filtration. The system proposed below has been developed based on new
technologies like the evolution of camera sensors, the use of a beam laser to visualize particles, and the
link between programming algorithms with free platforms.
The first system used consisted of a Canon 650D camera with a 17-55mm lens, a Ld Pumped All-SolidState Green Laser. Tests were performed inside a controlled environment where the external light insulation was removed, a Transparent FOUP (Front Opening Unified Pod) where the sample of white
marble dust was introduced to see its dispersion among the particles. At that moment, the photos were
taken at different angles of incidence such as against the laser and 60 degrees with reference to the line
of action of the laser. Furthermore, other tests were carried out in an external environment, where
photographs of particles from the human body were taken.
In order to implement a comparison of results, we used a Second System composed by a Camera HighResolution CMOS Sensor with Global Shutter lt225. The Ld Pumped All-Solid-State Green Laser, using the same parameters as the first system. Cases were repeated with the transparent FOUP and large
particles from the body. Simulating the normal movements inside a clean room.
The third stage, is the image processing using OpenCV libraries, in this case, EmguCV which processes
images, the fundamental principle of the image processing is the reading of each pixel, the intensities of
each pixel and in the case of the processing of black and white images, each pixel receives values from 0
to 255, with 0 being the value for black and 255 for white. The program algorithm responds to these
values and will separate the high-intensity values from the low-intensity values. In this case, the green
color will become an important value, which by means of mathematical filters, will generate a clearer
image of where the particles are.
The main features of the images taken by the camera Canon 650D are its resolution, which at short
distances allows displaying small particles in the order of 12um but, its limitation is its speed of capture.
On the other hand, the processed results of the photographs taken by the CMOS camera showed greater
accuracy in frame per second, but a large amount of storage memory is required due to the capture
Particle Visualization System, FOUP (Front Opening Unified Pod), CMOS Sensor, OpenCV.
Blind Image Quality Assessment Using Singular Value Decomposition Based Dominant Eigenvectors For Feature Selection
Besma Sadou, Atidel Lahoulou and Toufik Bouden, University of Jijel, Algeria
In this paper, a new no-reference image quality assessment (NR-IQA) metric for grey images is proposed using LIVE II image database. The features used are extracted from three well-known NR-IQA objective metrics based on natural scene statistical attributes from three different domains. These metrics may contain redundant, noisy or less informative features which affect the quality score prediction. In order to overcome this drawback, the first step of our work consists in selecting the most relevant image quality features by using Singular Value Decomposition (SVD) based dominant eigenvectors. The second step is performed by employing Relevance Vector Machine (RVM) to learn the mapping between the previously selected features and human opinion scores. Simulations demonstrate that the proposed metric performs very well in terms of correlation and monotonicity.
Natural Scene Statistics (NSS), Singular Value Decomposition (SVD), dominant eigenvectors, Relevance Vector Machine (RVM).