Digital Manipulation of Human Faces: Effects on Emotional Perception and Brain Activity

The study of human face-processing has granted insight into key adaptions across various social and biological functions. However, there is an overall lack of consistency regarding digital alteration styles of human-face stimuli. In order to investigate this, two independent studies were conducted examining unique effects of image construction and presentation. In the first study, three primary forms of stimuli presentation styles (color, black and white, cutout) were used across iterations of non-thatcherized/thatcherized and non-inverted/inverted presentations. Outcome measures included subjective reactions measured via ratings of perceived “grotesqueness,” and objective outcomes of N170 event-related potentials (ERPs) measured via encephalography. Results of subjective measures indicated that thatcherized images were associated with an increased level of grotesque perception, regardless of overall condition variant and inversion status. A significantly larger N170 component was found in response to cutout-style images of human faces, thatcherized images, and inverted images. Results suggest that cutout image morphology may be considered a well-suited image presentation style when examining ERPs and facial processing of otherwise unaltered human faces. Moreover, less emphasis can be placed on decision making regarding main condition morphology of human face stimuli as it relates to negatively valent reactions. The second study explored commonalities between thatcherized and uncanny images. The purpose of the study was to explore commonalities between these two styles of digital manipulation and establish a link between previously disparate areas of human-face processing research. Subjective reactions to stimuli were measured via participant ratings of “off-putting.” ERP data were gathered in order to explore if any unique effects emerged via N170 and N400 presentations. Two main “morph continuums” of stimuli, provided by Eduard Zell (see Zell et al., 2015), with uncanny features were utilized. A novel approach of thatcherizing images along these continuums was used. thatcherized images across both continuums were regarded as more off-putting than non-thatcherized images, indicating a robust subjective effect of thatcherization that was relatively unimpacted by additional manipulation of key featural components. Conversely, results from brain activity indicated no significant differences of N170 between level of shape stylization and their thatcherized counterparts. Unique effects between continuums and exploratory N400 results are discussed

Image Data Augmentation Approaches: A Comprehensive Survey and Future directions

Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation. Among these, data augmentation, which aims to enlarge the dataset size by including sample diversity, has been a hot topic in recent times. In this article, we focus on advanced data augmentation techniques. we provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation. For results reproducibility, we compiled available codes of all data augmentation techniques. Finally, we discuss the challenges and difficulties, and possible future direction for the research community. We believe, this survey provides several benefits i) readers will understand the data augmentation working mechanism to fix overfitting problems ii) results will save the searching time of the researcher for comparison purposes. iii) Codes of the mentioned data augmentation techniques are available at https://github.com/kmr2017/Advanced-Data-augmentation-codes iv) Future work will spark interest in research community.Comment: We need to make a lot changes to make its quality bette

Segmentation Versus Object Representation - Are They Separable?

Relation between shape representation and segmentation is discussed to make an argument that they cannot be handled separately. Parameters that influence the selection of a particular shape representation scheme are identified and a control structure is proposed that employs shape models of different types and granularities in a coarse to fine strategy. The necessity of using different shape models is demonstrated by comparing object boundaries of volumetric models with actual occluding boundaries of objects in range images