Facial Expression Recognition Using Multiresolution Analysis

Facial expression recognition from images or videos attracts interest of research community owing to its applications in human-computer interaction and intelligent transportation systems. The expressions cause non-rigid motions of the face-muscles thereby changing the orientations of facial curves. Wavelets and Gabor wavelets have been used effectively for recognition of these oriented features. Although wavelets are the most popular multiresolution method, they have limited orientation-selectivity/directionality. Gabor wavelets are highly directional but they are not multiresolution methods in the true sense of the term. Proposed work is an effort to apply directional multiresolution representations like curvelets and contourlets to explore the multiresolution space in multiple ways for extracting effective facial features. Extensive comparisons between different multiresolution transforms and state of the art methods are provided to demonstrate the promise of the work. The problem of drowsiness detection, a special case of expression recognition, is also addressed using a proposed feature extraction method

Investigating Potential Combinations of Visual Features towards Improvement of Full-Reference and No-Reference Image Quality Assessment

Objective assessment of image quality is the process of automatic assignment of a scalar score to an image such that the rating or score corresponds to the score provided by the Human Visual System (HVS). Despite extensive studies since the last two decades, it remains a challenging problem in image processing due to the presence of different types of distortions and limited knowledge of the HVS. Existing approaches for assessing the perceptual quality of images have relied on a number of methodologies that directly apply known properties of the HVS, construct hypotheses considering the HVS as a blackbox and use hybrid approaches that apply both of the techniques. All of these methodologies have relied on different types of visual features for Image Quality Assessment (IQA). In this dissertation, we have studied the problem of different types of IQA from the feature extraction point of view and showed that effective combinations of simple visual features can be used to develop IQA approaches having competitive performance with the state-of-the-art. Our work is divided into four parts each having the final goal to bring about performance improvement in the areas of Full-Reference (FR) and No-Reference (NR)-IQA. We have gradually moved from FR to NR-IQA in the works presented in this dissertation. First, we propose improvements in two existing FR-IQA techniques by introducing changes in the features used. Next, we propose a new FR-IQA technique by extracting image saliency as global features and combining them with the local features of gradient and variance to improve the performance. For NR-IQA, we propose a novel technique for sharpness detection in natural images using simple features. The performance of this method provides improvement over the existing methods. After working with the specific purpose NR-IQA, we propose a general purpose technique using suitable features such that no training with pristine or distorted images or subjective quality scores is required. This technique, despite having no reliance on training, provides competitive performance with the state-of-the-art techniques. The main contribution of the dissertation lies in identification and analysis of effective features and their combinations for improving three different sub-areas of IQA

A scoping review of natural language processing of radiology reports in breast cancer

Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing