Readout from iconic memory involves similar neural processes as selective spatial attention

Iconic memory and spatial attention are often considered as distinct topics, but may have functional similarities. Here we provide fMRI evidence for some common underlying neural effects. Participants judged three visual stimuli in one hemifield of a bilateral array comprising six stimuli. The relevant hemifield for partial report was indicated by an auditory cue, administered either before the visual array (pre-cues, spatial attention) or shortly after (post-cues, iconic memory). Pre- and post-cues led to similar activity modulations in lateral occipital cortex, contralateral to the cued side, indicating that readout from iconic memory can have similar neural effects to spatial attention. We also found common bilateral activation of a fronto-parietal network for post-cue and pre-cue trials. These neuroimaging data suggest that some common neural mechanisms underlie selective spatial attention and readout from iconic memory. Some differences were also found, with post-cues leading to higher activity in right middle frontal gyrus

Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models

Deep learning has shown state-of-art classification performance on datasets such as ImageNet, which contain a single object in each image. However, multi-object classification is far more challenging. We present a unified framework which leverages the strengths of multiple machine learning methods, viz deep learning, probabilistic models and kernel methods to obtain state-of-art performance on Microsoft COCO, consisting of non-iconic images. We incorporate contextual information in natural images through a conditional latent tree probabilistic model (CLTM), where the object co-occurrences are conditioned on the extracted fc7 features from pre-trained Imagenet CNN as input. We learn the CLTM tree structure using conditional pairwise probabilities for object co-occurrences, estimated through kernel methods, and we learn its node and edge potentials by training a new 3-layer neural network, which takes fc7 features as input. Object classification is carried out via inference on the learnt conditional tree model, and we obtain significant gain in precision-recall and F-measures on MS-COCO, especially for difficult object categories. Moreover, the latent variables in the CLTM capture scene information: the images with top activations for a latent node have common themes such as being a grasslands or a food scene, and on on. In addition, we show that a simple k-means clustering of the inferred latent nodes alone significantly improves scene classification performance on the MIT-Indoor dataset, without the need for any retraining, and without using scene labels during training. Thus, we present a unified framework for multi-object classification and unsupervised scene understanding

Affective iconic words benefit from additional sound–meaning integration in the left amygdala

Recent studies have shown that a similarity between sound and meaning of a word (i.e., iconicity) can help more readily access the meaning of that word, but the neural mechanisms underlying this beneficial role of iconicity in semantic processing remain largely unknown. In an fMRI study, we focused on the affective domain and examined whether affective iconic words (e.g., high arousal in both sound and meaning) activate additional brain regions that integrate emotional information from different domains (i.e., sound and meaning). In line with our hypothesis, affective iconic words, compared to their non‐iconic counterparts, elicited additional BOLD responses in the left amygdala known for its role in multimodal representation of emotions. Functional connectivity analyses revealed that the observed amygdalar activity was modulated by an interaction of iconic condition and activations in two hubs representative for processing sound (left superior temporal gyrus) and meaning (left inferior frontal gyrus) of words. These results provide a neural explanation for the facilitative role of iconicity in language processing and indicate that language users are sensitive to the interaction between sound and meaning aspect of words, suggesting the existence of iconicity as a general property of human language

Estimating Sensor Motion from Wide-Field Optical Flow on a Log-Dipolar Sensor

Log-polar image architectures, motivated by the structure of the human visual field, have long been investigated in computer vision for use in estimating motion parameters from an optical flow vector field. Practical problems with this approach have been: (i) dependence on assumed alignment of the visual and motion axes; (ii) sensitivity to occlusion form moving and stationary objects in the central visual field, where much of the numerical sensitivity is concentrated; and (iii) inaccuracy of the log-polar architecture (which is an approximation to the central 20°) for wide-field biological vision. In the present paper, we show that an algorithm based on generalization of the log-polar architecture; termed the log-dipolar sensor, provides a large improvement in performance relative to the usual log-polar sampling. Specifically, our algorithm: (i) is tolerant of large misalignmnet of the optical and motion axes; (ii) is insensitive to significant occlusion by objects of unknown motion; and (iii) represents a more correct analogy to the wide-field structure of human vision. Using the Helmholtz-Hodge decomposition to estimate the optical flow vector field on a log-dipolar sensor, we demonstrate these advantages, using synthetic optical flow maps as well as natural image sequences

Parallel stereo vision algorithm

Integrating a stereo-photogrammetric robot head into a real-time system requires software solutions that rapidly resolve the stereo correspondence problem. The stereo-matcher presented in this paper uses therefore code parallelisation and was tested on three different processors with x87 and AVX. The results show that a 5mega pixels colour image can be matched in 5,55 seconds or as monochrome in 3,3 seconds

A framework for interrogating social media images to reveal an emergent archive of war

The visual image has long been central to how war is seen, contested and legitimised, remembered and forgotten. Archives are pivotal to these ends as is their ownership and access, from state and other official repositories through to the countless photographs scattered and hidden from a collective understanding of what war looks like in individual collections and dusty attics. With the advent and rapid development of social media, however, the amateur and the professional, the illicit and the sanctioned, the personal and the official, and the past and the present, all seem to inhabit the same connected and chaotic space.However, to even begin to render intelligible the complexity, scale and volume of what war looks like in social media archives is a considerable task, given the limitations of any traditional human-based method of collection and analysis. We thus propose the production of a series of ‘snapshots’, using computer-aided extraction and identification techniques to try to offer an experimental way in to conceiving a new imaginary of war. We were particularly interested in testing to see if twentieth century wars, obviously initially captured via pre-digital means, had become more ‘settled’ over time in terms of their remediated presence today through their visual representations and connections on social media, compared with wars fought in digital media ecologies (i.e. those fought and initially represented amidst the volume and pervasiveness of social media images).To this end, we developed a framework for automatically extracting and analysing war images that appear in social media, using both the features of the images themselves, and the text and metadata associated with each image. The framework utilises a workflow comprising four core stages: (1) information retrieval, (2) data pre-processing, (3) feature extraction, and (4) machine learning. Our corpus was drawn from the social media platforms Facebook and Flickr

Generative deep fields : arbitrarily sized, random synthetic astronomical images through deep learning

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set. In typical GAN architectures these images are small, but a variant known as Spatial-GANs (SGANs) can generate arbitrarily large images, provided training images exhibit some level of periodicity. Deep extragalactic imaging surveys meet this criteria due to the cosmological tenet of isotropy. Here we train an SGAN to generate images resembling the iconic Hubble Space Telescope eXtreme Deep Field (XDF). We show that the properties of 'galaxies' in generated images have a high level of fidelity with galaxies in the real XDF in terms of abundance, morphology, magnitude distributions and colours. As a demonstration we have generated a 7.6-billion pixel 'generative deep field' spanning 1.45 degrees. The technique can be generalised to any appropriate imaging training set, offering a new purely data-driven approach for producing realistic mock surveys and synthetic data at scale, in astrophysics and beyond.Peer reviewe