S. Rathika

Bio: S. Rathika is an academic researcher from Annamalai University. The author has contributed to research in topics: Alpha compositing. The author has co-authored 1 publications.

Performance analysis of data hiding in thermal image using alpha blending technique

Abstract: Hiding data in an image become a very successful technique to communicate with end users, blinding the interpreter’s intention. Various algorithms have been analyzed on the basis of masking the content within the coordinates of the images or in the color of the images used. Few advanced developed complex algorithms to hide the content in the values of the colors to ensure or guarantee robustness of the data hidden. In this research, a simple technique called alpha blending is used to hide the data in an image and its impact is analyzed for different alpha values. The unavoidable impact of noise on the transmitted carrier image was formulated and studied by many researchers using various available algorithms. In this regard: the same is analyzed using alpha blending in thermal images.

Effect of different imaging modalities on the performance of a CNN: An experimental study on damage segmentation in infrared, visible, and fused images of concrete structures

Abstract: This study investigates the semantic segmentation of common concrete defects when using different imaging modalities. One pre-trained Convolutional Neural Network (CNN) model was trained via transfer learning and tested to detect concrete defect indications, such as cracks, spalling, and potential subsurface defects. We compared the model's performance using datasets of visible, thermal, and fused images. In addition, the impact of using different image enhancement techniques, such as histogram equalization and resolution improvement, was investigated. The data was collected from four different concrete structures using four infrared cameras with distinct sensitivities and resolutions, with imaging campaigns conducted during autumn, summer, and winter. Although specific defects can be detected in monomodal images, the results demonstrated that a larger number of defect classes could be detected using fused images with the same viewpoint and resolution as the single-sensor image without significant loss of information. In addition, the output of one hypothesis test showed that the image enhancement techniques provided no significant improvement in the CNN performance for this case of study, even though they resulted in enhanced images with higher information content (entropy) than the original images.