In many critical investigations of machine vision, the focus lies almost exclusively on dataset bias and on fixing datasets by introducing more and more diverse sets of images. We propose that machine vision systems are inherently biased not only because they rely on biased datasets but also because theirperceptual topology, their specific way of representing the visual world, gives rise to a new class of bias that we callperceptual bias. Concretely, we define perceptual topology as the set of those inductive (...) biases in machine vision systems that determine its capability to represent the visual world. Perceptual bias, then, describes the difference between the assumed “ways of seeing” of a machine vision system, our reasonable expectations regarding its way of representing the visual world, and its actual perceptual topology. We show how perceptual bias affects the interpretability of machine vision systems in particular, by means of a close reading of a visualization technique called “feature visualization”. We conclude that dataset bias and perceptual bias both need to be considered in the critical analysis of machine vision systems and propose to understand critical machine vision as an important transdisciplinary challenge, situated at the interface of computer science and visual studies/Bildwissenschaft. (shrink)

The project “Artificial and Artistic Vision. Computer Vision and Art History in Practical-Methodical Cooperation” is interdisciplinary by definition and also in its personnel composition and combines the humanities, engineering, and natural sciences. Together, prototypes and methodological approaches to an automatic vision that assists art history are being developed in the form of basic research.

The last few years have seen an explosion of medieval images in digital form, chiefly as a result of photo-library and manuscript digitisation projects. An entire corpus of images, even selected solely by scene or iconography, becomes an unwieldy object of study by traditional art-historical means. This is even more the case for medieval images, where authorship and dating are often cloudy and unclear, and the image itself is in many cases the first resource for scholarly inquiry.We take the digital (...) image – in particular, the digital image of the body – as our object of study in a wide-ranging computationally-augmented reading of an image-corpus; ours is made up of thousands of depictions of the ‘Annunciation’ and ‘Baptism’, selected not only for their primacy in Christian art but for their dialogical interaction. Our corpus of 6,564 ‘Annunciations’ and 883 ‘Baptisms’, whilst not necessarily representative in density, includes a wide range of stylistic, theological and historical tendencies.We computationally extract not only body images but poses, gestures and interactions. Such a range of gestures allows for a morphological treatment of bodily motifs, whose multi-dimensional, quantitative nature allows us to complicate and problematise iconographic taxonomies, populating the spaces between categories. Finally, our gestural manifolds provide a morphological pointer to dissecting the microtemporalities of the scenes, and their relative dynamics and inconsistencies. (shrink)