Influence of the Direction of Elemental Luminance Gradients on the Responses of V4 Cells to Textured Surfaces

Hanazawa, Akitoshi; Komatsu, Hidehiko

doi:10.1523/jneurosci.21-12-04490.2001

Cited by 67 publications

(55 citation statements)

References 25 publications

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“…Previous physiological studies have shown that neurons in V4 respond to various visual attributes (33) including shape (27,34), color (35,36), disparity (37,38), and texture (6,22). In particular, many studies have demonstrated that V4 neurons show I n t o le r a n t T o le r a n t Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Their algorithm is particularly inspiring because PS statistics use filters and computations that share biological properties. It was recently shown, for example, that a version of their synthesis algorithm can generate perceptually indistinguishable visual images (visual metamers) (21) and that naturalistic textures incorporating these summary statistics strongly activate neurons in V2, compared with noise images lacking these features (7).Given the previous successes in the parametric description of textures, it is tempting to expect that promoting this framework will reveal a full picture of texture representation in the subsequent stages in the ventral pathway, such as area V4, where many neurons show selectivity for textures (6,22). However, such an attempt has been hampered by a fundamental difficulty of data sampling due to the high dimensionality of natural texture space.…”

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confidence: 99%

“…Given the previous successes in the parametric description of textures, it is tempting to expect that promoting this framework will reveal a full picture of texture representation in the subsequent stages in the ventral pathway, such as area V4, where many neurons show selectivity for textures (6,22). However, such an attempt has been hampered by a fundamental difficulty of data sampling due to the high dimensionality of natural texture space.…”

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confidence: 99%

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Image statistics underlying natural texture selectivity of neurons in macaque V4

Okazawa

Tajima

Komatsu

2014

Proc. Natl. Acad. Sci. U.S.A.

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146

171

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Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures. We systematically explored neural selectivity in a high-dimensional texture space by combining texture synthesis and efficient-sampling techniques. This yielded parameterized models for individual texture-selective neurons. The models provided parsimonious but powerful predictors for each neuron's preferred textures using a sparse combination of image statistics. As a whole population, the neuronal tuning was distributed in a way suitable for categorizing textures and quantitatively predicts human ability to discriminate textures. Together, we suggest that the collective representation of visual image statistics in V4 plays a key role in organizing the natural texture perception.texture perception | material perception | visual area V4 | single-cell recording | image analysis I n the visual world, objects are characterized in part by their shapes, but also by their textures (1). The wide variety of textures we experience enables us to segment objects from backgrounds, perceive object properties, and recognize materials. It is well established that the representation of complex shapes and contours is gradually built up along the ventral visual pathway (2-5). On the other hand, how textural information is processed in the cortex is largely unknown, although some recent studies have examined the representation of natural textures and surface properties in the macaque ventral visual areas (6-11). Because, unlike contours, textures cannot be described based on combinations of edge fragments, we need to consider different underlying cortical processing.In contrast to the limited knowledge available from physiology, computational descriptions of textures have been extensively developed in the fields of psychophysics (12-16) and computer vision (17)(18)(19). In one such description, Portilla and Simoncelli (20) proposed that textures could be represented using an ensemble of summary statistics, including features derived from the luminance histogram and the amplitudes of the outputs of Gabor-like filters, as well as higher-order statistics such as the correlations across the filter outputs (see Fig. 3 for details; hereafter, we call this collection of statistics "PS statistics," using the authors' initials). Portilla and Simoncelli successfully generated new textures that were visually indistinguishable from the originals solely by making their PS statistics identical. Their algorithm is particularly inspiring because PS statistics use filters and computations that share biological properties. It was recently shown, for example, that a version of their synthesis algorithm can generate perceptually indistinguishable visual images (visual metamers) (21) and that naturalisti...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Image statistics underlying natural texture selectivity of neurons in macaque V4

Okazawa

Tajima

Komatsu

2014

Proc. Natl. Acad. Sci. U.S.A.

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146

171

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“…Notice that we use the terms "edge" and "line" detectors merely as conventional descriptive terms; it is now generally understood that these terms are misleading if interpreted literally. Thus, the formal operators may well have rather immediate neurophysiological correlates (Hanazawa & Komatsu, 2001).…”

Section: Theorymentioning

confidence: 99%

Perception of illuminance flow in the case of anisotropic rough surfaces

Koenderink

Doorn

Pont

2007

Perception & Psychophysics

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“…Area V4 receives input from V1/V2 and is known to comprise neurons selective for various aspects of visual information, such as shape [38], color [55] or texture [24]. In this paper we are concerned with shape and, therefore, we are mainly interested in the function of shape-selective V4 neurons.…”

Section: Introductionmentioning

confidence: 99%

COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition

Azzopardi

Petkov

2014

Lecture Notes in Computer Science

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Abstract. The primate visual system has an impressive ability to generalize and to discriminate between numerous objects and it is robust to many geometrical transformations as well as lighting conditions. The study of the visual system has been an active reasearch field in neuropysiology for more than half a century. The construction of computational models of visual neurons can help us gain insight in the processing of information in visual cortex which we can use to provide more robust solutions to computer vision applications. Here, we demonstrate how inspiration from the functions of shape-selective V4 neurons can be used to design trainable filters for visual pattern recognition. We call this approach COSFIRE, which stands for Combination of Shifted Filter Responses. We illustrate how a COSFIRE filter can be configured to be selective for the spatial arrangement of lines and/or edges that form the shape of a given prototype pattern. Finally, we demonstrate the effectiveness of the COSFIRE approach in three applications: the detection of vascular bifurcations in retinal fundus images, the localization and recognition of traffic signs in complex scenes and the recognition of handwritten digits. This work is a further step in understanding how visual information is processed in the brain and how information on pixel intensities is converted into information about objects. We demonstrate how this understanding can be used for the design of effective computer vision algorithms.

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Influence of the Direction of Elemental Luminance Gradients on the Responses of V4 Cells to Textured Surfaces

Cited by 67 publications

References 25 publications

Image statistics underlying natural texture selectivity of neurons in macaque V4

Image statistics underlying natural texture selectivity of neurons in macaque V4

Perception of illuminance flow in the case of anisotropic rough surfaces

COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition

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