A novel image inpainting framework based on multilevel image pyramids

Image inpainting is the art of manipulating an image so that it is visually unrecognizable way. A considerable amount of research has been done in this area over the last few years. However, the state of art techniques does suffer from computational complexities and plausible results. This paper proposes a multi-level image pyramid-based image inpainting algorithm. The image inpainting algorithm starts with the coarsest level of the image pyramid and overpainting information is transferred to the subsequent levels until the bottom level gets inpainted. The search strategy used in the algorithm is based on hashing the coherent information in an image which makes the search fast and accurate. Also, the search space is constrained based on the propagated information thereby reducing the complexity of the algorithm. Compared to other inpainting methods; the proposed algorithm inpaints the target region with better plausibility and human vision conformation. Experimental results show that the proposed algorithm achieves better results as compared to other inpainting techniques

Early detection of dyslexia based on EEG with novel predictor extraction and selection

Abstract Dyslexia is a learning disorder caused by difficulties in the brain’s processing of letters and words. This study used EEG recordings to detect dyslexia at a young age. EEG recordings of 53 individuals, including 29 dyslexic and 24 normal individuals, were collected while they were engaged in two distinct mental activities known as the N-Back task and the Oddball task. Predictors were extracted using several methods and reduced using Principal Component Analysis (PCA). A relief-based strategy was applied to select predictors, and Support Vector Machine (SVM) classifier was used to achieve an average accuracy of 79.3% for dyslexia detection, which is better than the performance of its predecessors. The results indicate that EEG recordings and machine learning methods could be useful for identifying dyslexia in children

A novel and efficient Wavelet Scattering Transform approach for primitive-stage dyslexia-detection using electroencephalogram signals

Dyslexia is a neurological disorder affecting reading and writing abilities. Early intervention is important for affected individuals’ social and academic development. The accuracy and objectivity limitations of traditional dyslexia detection systems based on behavioral symptoms and standard tests can pose challenges to the early detection of the condition. In response, an electroencephalogram (EEG) based detection method has been proposed to aid medical professionals in addressing these limitations. A comparison is made between the Wavelet Scattering Transform (WST) approach and three other approaches, namely Spectral Statistical Features (SSF), Connectivity Features with Autoencoders (CFA), and Hybrid Features (HF), using two datasets. These two datasets were chosen for various reasons, including the fact that they were collected during different tasks and from different countries. Another significant factor is that the age range of the participants was 7 and 12 years old, marking the beginning of their educational journey. This age range is ideal for detecting dyslexia in its primitive stages, making these datasets a perfect fit for this research. The performance evaluation of the approaches involved utilizing Support Vector Machine (SVM) classifiers with three non-linear kernels and k-fold cross-validation implementation. The findings suggest that the other three approaches could not achieve more than 80% accuracy, and their accuracy results were inconsistent with each dataset. In contrast, the WST approach achieved a high accuracy rate, with an average accuracy of 96.96% and 97.12% for dataset 1 and dataset 2, respectively, using the Radial Basis Function (RBF) kernel. The accuracy of WST features is further improved to 98.72% and 98.67% through the Majority Voting method. These findings demonstrate the effectiveness and generalization of the WST approach