General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k dataset demonstrate superior performance compared to traditional methods, achieving higher accuracy, faster processing speed, and improved boundary preservation. Novelty: The proposed model effectively combines deep learning with fusion techniques, enhancing matting quality while maintaining robustness across various environmental conditions. Implications: These findings highlight the potential of integrating fusion techniques with deep learning for image matting, offering valuable insights for future research in automated image processing applications, including augmented reality, gaming, and interactive video technologies. Highlights: Better Precision: Fusion techniques enhance fine detail preservation. Faster Processing: Lightweight U-Net improves speed and accuracy. Wide Applications: Useful for AR, gaming, and video processing. Keywords: Deep image matting, computer vision, deep learning, fusion techniques, U-Net
Experimental research was carried out to investigate the effect of fire flame (high temperature) on specimens of short columns manufactured using SCC (Self compacted concrete). To simulate the real practical fire disasters, the specimens were exposed to high
temperature flame, using furnace manufactured for this purpose. The column specimens were cooled in two ways. In the first the specimens were left in the air and suddenly cooled using water, after that the specimens were loaded to study the effect of degree of
temperature, steel reinforcement ratio and cooling rate, on the load carrying capacity of the reinforced concrete column specimens. The results will be compared with behaviour of columns without burning (control specime
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Faces of the individual in his life many stressful events, which includes expertise undesirable, and events may involve a lot of sources of tension and the risk factors and threats in all areas of life, and this would make the stressful events play a role in the genesis of many diseases physical.
The high blood pressure is one of the most Actual manifestations of mental stress in the present scale physical disorders which may frequently in men relative to women, which may be caused by spasms in the blood vessels.
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In this work, the dyes Rhodamine B and Coumarin 102 containing titanium dioxide nanoparticles were used as scattering centers to fabricate a random gain medium. The laser dye was dissolved in hexanol and methanol solvent respectively. The titanium dioxide nanoparticles were synthesized by DC reaction magnetron spraying technique. The random-gain medium was made by adding 2.5 mg of titanium dioxide nanoparticles to Rhodamine and coumarin 102 dyes by coating the glass cell with two-sided titanium dioxide with high spectral efficiency and low production cost. A narrow line optical emission was detected at 565 nm for Rhodamine B and 534 nm for coumarin 102, where it was found that rhodamine B dye has FWHM 8 nm and coumarin dye 102 has FWHM 9 nm
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Gray-Scale Image Brightness/Contrast Enhancement with Multi-Model
Histogram linear Contrast Stretching (MMHLCS) method
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