AIMC Topic: Form Perception

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Visual prototypes in the ventral stream are attuned to complexity and gaze behavior.

Nature communications
Early theories of efficient coding suggested the visual system could compress the world by learning to represent features where information was concentrated, such as contours. This view was validated by the discovery that neurons in posterior visual ...

sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot.

Sensors (Basel, Switzerland)
Staircase cleaning is a crucial and time-consuming task for maintenance of multistory apartments and commercial buildings. There are many commercially available autonomous cleaning robots in the market for building maintenance, but few of them are de...

Joint representation of color and form in convolutional neural networks: A stimulus-rich network perspective.

PloS one
To interact with real-world objects, any effective visual system must jointly code the unique features defining each object. Despite decades of neuroscience research, we still lack a firm grasp on how the primate brain binds visual features. Here we ...

EEG-based trial-by-trial texture classification during active touch.

Scientific reports
Trial-by-trial texture classification analysis and identifying salient texture related EEG features during active touch that are minimally influenced by movement type and frequency conditions are the main contributions of this work. A total of twelve...

Early Emergence of Solid Shape Coding in Natural and Deep Network Vision.

Current biology : CB
Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in V4 has been studied and conceived...

Local features and global shape information in object classification by deep convolutional neural networks.

Vision research
Deep convolutional neural networks (DCNNs) show impressive similarities to the human visual system. Recent research, however, suggests that DCNNs have limitations in recognizing objects by their shape. We tested the hypothesis that DCNNs are sensitiv...

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...

Crowding reveals fundamental differences in local vs. global processing in humans and machines.

Vision research
Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not necessarily imply human-like computations. Previous studies have sugge...

Learning efficient haptic shape exploration with a rigid tactile sensor array.

PloS one
Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown objects or to recognize familiar objects. Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabiliti...