Screen Content Image Segmentation Using Sparse-Smooth Decomposition

Sparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation. The proposed algorithm separates the background and foreground using a sparse-smooth decomposition technique such that the smooth and sparse components correspond to the background and foreground respectively. This algorithm is tested on several test images from HEVC test sequences and is shown to have superior performance over other methods, such as the hierarchical k-means clustering in DjVu. This segmentation algorithm can also be used for text extraction, video compression and medical image segmentation.Comment: Asilomar Conference on Signals, Systems and Computers, IEEE, 2015, (to Appear

Iris Recognition Using Scattering Transform and Textural Features

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%

Digital image colorimetry for determination of sulfonamides in water

This work aims to develop a digital image-based colorimetry for screening of sulfonamides (SAs) in water. It will be based on the determination of SAs in water, by analyzing the color response with an automatic image processing algorithm.Antimicrobial agents are considered emerging pollutants in water, because of their potential to accelerate spread of bacterial resistance genes, and due to their harmful effect to ecosystem through death or inhibition of natural microbiota. Sulfonamides (SAs) are an important antimicrobial group and it is widely used in both human and veterinary medicine. Studies have demonstrated that SAs are very mobile and highly available in soil with no bioaccumulation. Furthermore, these compounds seem to be quite resistant to biodegradation in surface water which can benefit contamination of aquatic environment. Thus, monitoring of SAs levels in water are very important to determine their aquatic risk assessment. Several methods for determination of SAs in water have been developed. Most of them are based on the coupling of high-performance liquid chromatography (LC) and mass spectrometry (MS). LC-MS is widely used due to their high sensitivity and specificity; however, this approach is very expensive and does not allow in situ analysis. Hence, development of field deployable screening methods is required. Methods based on digital image colorimetry have been broadly applied for point-of-care tests, forensic analysis and environmental monitoring. The digital image based methods are very promising as field screening techniques because they are fast, low cost, portable and easy handling methodologies