An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex.

By J. Jones, L. Palmer. 1987

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “They extract simple features related to those observed in early visual pre-processing stages of biological systems (e.g., De Valois, Albrecht, & Thorell, 1982; Jones & Palmer, 1987).”
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream (2014, background): “Furthermore, while the RFs in early visual area V1 have been characterized in terms of preferred orientation, location, and spatial frequency (Jones and Palmer, 1987), exactly what stimulus features are represented in downstream areas is less clear (Cox, 2014).”
A Biologically Inspired System for Action Recognition (2007, methodology): “Space-time-oriented S1 units: Most studies focus on the spatial structure of receptive fields: V1 simple cells’ receptive field profiles were modeled by twodimensional Gabors or Gaussian-derivative functions in [28, 29].”
Evidence and a Computational Explanation of Cultural Differences in Facial Expression Recognition (2010, background): “They act as edge detectors and have been shown to accurately model the receptive fields of simple cells in primary visual cortex (Jones & Palmer, 1987).”
An efficient automated parameter tuning framework for spiking neural networks (2014, methodology): “The synaptic connections between the On(Off)Buffer neurons and the Exc neurons in Figure 6B formed receptive ﬁelds that resembled Gabor ﬁlters, which have been used extensively to model V1 simple cell responses (Jones and Palmer, 1987).”
Facial expression recognition using radial encoding of local Gabor features and classifier synthesis (2012, methodology): “In order to model the spatial orientation properties of simple cells in the primary cortex, a set of two-dimensional Gabor filters have been proposed [22].”
Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex (2012, methodology): “The receptive ﬁelds of simple cells consist of excitatory and inhibitory regions (Hubel & Wiesel, 1962), and their arrangement can be well described by Gabor ﬁlters (Jones & Palmer, 1987).”
The Operating Point of the Cortex: Neurons as Large Deviation Detectors (2007, methodology): “A simplified model of this transformation is one in which the instantaneous spike rate is obtained by passing a generator potential through a static nonlinearity (Fig.”
Population coding under normalization (2009, background): “Sharp tuning is achieved as the number of effective subregions increases or if their individual aspect ratio is increased (Jones & Palmer, 1987; Ringach, 2002).”
Robust Object Recognition with Cortex-Like Mechanisms (2007, methodology): “For the most part, the use of visual neuroscience in computer vision has been limited to early vision for deriving stereo algorithms (e.g., [1]) and to justify the use of DoG (derivative-of-Gaussian) filters and more recently of Gabor filters [2], [3].”
Why is Real-World Visual Object Recognition Hard? (2008, background): “The responses of these cells to visual stimuli are welldescribed by a spatial linear filter, resembling a Gabor wavelet [29– 31], with a nonlinear output function (threshold and saturation) and some local normalization (roughly analogous to ‘‘contrast gain control’’).”
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, methodology): “Following the process of selecting the proposals from the whole image, a simple feature extraction is conducted for each proposal or the whole image to extract the low-level descriptors that are invariant to shift, rotation, and scaling, to some extent, such as SIFT [98], Gabor [99], and the histogr”