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If we want to decolonize the history of art, argues Kristopher Kersey, we must rethink our approach to the historical record. This means dispensing with Eurocentric binaries—divisions between Western and non-Western, modern and premodern—and making a commitment to artworks that challenge the perspectives we build upon them. In Facing Images, the question takes elegant and intriguing form: If the aesthetic hallmarks of “modernity” can be found in twelfth-century art, what does it really mean to be “modern”? Kersey’s answer to this question models a new historiography. Facing Images begins by tracing the turbulent discourse surrounding the emergence of Japanese art history as a modern field. In lieu of examining canonical works from the twelfth century, Kersey foregrounds the elusive and the enigmatic in artworks little known and understudied outside Japan; the manuscripts he selects defy traditional art-historical narratives by exhibiting decidedly modern techniques, including montage, self-reference, reuse, noise, dissonance, and chronological disarray. Kersey weaves these medieval case studies together with insights from a wide range of interdisciplinary scholarship, using a methodology that will prove important for historians: Facing Images produces a history of non-Western art in which diverse and anachronic works are brought responsibly and equitably into dialogue with the present, without being subsumed under Eurocentric formalisms or false universals. A timely intervention in the history of medieval Japanese art, art historiography, and the history of global modernism, Facing Images redefines the relationship of the “premodern” non-West to “modern” art. It will be of particular interest to scholars of medieval Japanese art and of modernism.
This work draws upon material from the visual arts, poetry, fiction, drama, and pop-culture to help lead the reader to a heightened awareness of the universal nature of the issues that face the dying and those who care for them. The author argues.
For several years Harriet Walter has been collecting images of older women whose faces and lives have inspired and moved her. Some of these faces are well-known, some are facing the glasre of public scrutiny for the first time".
Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
In everyday life we identify faces regularly and seemingly with great ease. One might assume this to be a straightforward and highly accurate task. However, we are poor at identifying the faces of unfamiliar people, who we have never met before, despite the fact that many important everyday tasks depend on this. Forensic face matching requires the comparison of two face photographs, of a person who is not known to the observer. This seemingly simple task is critical for a wide range of security tasks, such as person identification at airports and borders, passport issuance and renewal, and criminal identification in police investigations. Despite its ubiquity, face matching is highly prone to error, even under conditions that are designed to maximally facilitate this task. For this reason, face matching has been studied extensively in Psychology, with the bulk of the research conducted since 2010. 'Forensic face Matching' provides readers with a wide-ranging, detailed, and critical overview of facial comparison and face matching, providing insights into its application, efficacy, and limitations in occupational settings, and of current scientific knowledge of this task.
"This book provides related theoretical background to understand the overall configuration and challenging problem of automated face analysis systems"--Provided by publisher.
This book presents the state-of-the-art in face detection and analysis. It outlines new research directions, including in particular psychology-based facial dynamics recognition, aimed at various applications such as behavior analysis, deception detection, and diagnosis of various psychological disorders. Topics of interest include face and facial landmark detection, face recognition, facial expression and emotion analysis, facial dynamics analysis, face classification, identification, and clustering, and gaze direction and head pose estimation, as well as applications of face analysis.
This book constitutes the thoroughly refereed post-conference proceedings of four international workshops held in the framework of the 9th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2019, in Sydney, NSW, Australia, in November 2019: Vision-Tech: Workshop on Challenges, Technology, and Solutions in the Areas of Computer Vision; Workshop on Passive and Active Electro‐Optical Sensors for Aerial and Space Imaging; Workshop on Deep Learning and Image Processing Techniques for Medical Images; and Workshop on Deep Learning for Video and Image Analysis. The 16 revised full papers presented were carefully selected from 26 submissions. The papers cover the full range of state-of-the-art research in image and video technology with topics ranging from well-established areas to novel current trends.
Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g. , recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gen erate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis.