AIMC Topic: Pattern Recognition, Visual

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Crowding in humans is unlike that in convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
Object recognition is a primary function of the human visual system. It has recently been claimed that the highly successful ability to recognise objects in a set of emergent computer vision systems-Deep Convolutional Neural Networks (DCNNs)-can form...

CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks.

Sensors (Basel, Switzerland)
Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs...

Efficient inverse graphics in biological face processing.

Science advances
Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or "analysis-by-synthesis", presents a possible solution, but its...

Decoding dynamic affective responses to naturalistic videos with shared neural patterns.

NeuroImage
This study explored the feasibility of using shared neural patterns from brief affective episodes (viewing affective pictures) to decode extended, dynamic affective sequences in a naturalistic experience (watching movie-trailers). Twenty-eight partic...

Separability and geometry of object manifolds in deep neural networks.

Nature communications
Stimuli are represented in the brain by the collective population responses of sensory neurons, and an object presented under varying conditions gives rise to a collection of neural population responses called an 'object manifold'. Changes in the obj...

Can machine learning account for human visual object shape similarity judgments?

Vision research
We describe and analyze the performance of metric learning systems, including deep neural networks (DNNs), on a new dataset of human visual object shape similarity judgments of naturalistic, part-based objects known as "Fribbles". In contrast to prev...

Object parsing in the left lateral occipitotemporal cortex: Whole shape, part shape, and graspability.

Neuropsychologia
Small and manipulable objects (tools) preferentially evoke a network of brain regions relative to other objects, including the lateral occipitotemporal cortex (LOTC), which is assumed to process tool shape information. Given the correlation between v...

Body Patches in Inferior Temporal Cortex Encode Categories with Different Temporal Dynamics.

Journal of cognitive neuroscience
An unresolved question in cognitive neuroscience is how representations of object categories at different levels (basic and superordinate) develop during the course of the neural response within an area. To address this, we decoded categories of diff...

Figure-Ground Organization in Natural Scenes: Performance of a Recurrent Neural Model Compared with Neurons of Area V2.

eNeuro
A crucial step in understanding visual input is its organization into meaningful components, in particular object contours and partially occluded background structures. This requires that all contours are assigned to either the foreground or the back...

Human visual cortical gamma reflects natural image structure.

NeuroImage
Many studies have reported visual cortical gamma-band activity related to stimulus processing and cognition. Most respective studies used artificial stimuli, and the few studies that used natural stimuli disagree. Electrocorticographic (ECoG) recordi...