Reverse engineering the way humans rank textures

Petrou, Maria; Talebpour, Alireza; Kadyrov, A. A.

doi:10.1007/s10044-006-0054-6

Cited by 9 publications

(10 citation statements)

References 24 publications

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“…In addition to color, voice-triggered synesthetes often report texture perceptions, for example a voice might be “smooth but granulated” or “[with a] soft center and very slight fuzziness around the outside.” Despite our knowledge of perceptions such as these, no systematic investigation of visual texture perceptions in synesthesia has yet been conducted—which is perhaps not surprising considering that it is not easy to quantify texture or to relate this quantification to perceptual categories (Petrou et al, 2007; Clarke et al, 2011). Eagleman and Goodale (2009) state: “Quantitatively testing these prevalences [of texture concurrents] will be a challenge: it is straightforward to develop a user-friendly color chooser […], but not so with the multidimensional varieties of texture” (Eagleman and Goodale, 2009, 291).…”

Section: Introductionmentioning

confidence: 99%

Color and texture associations in voice-induced synesthesia

Moos¹,

Simmons²,

Simner³

et al. 2013

Front. Psychol.

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Voice-induced synesthesia, a form of synesthesia in which synesthetic perceptions are induced by the sounds of people's voices, appears to be relatively rare and has not been systematically studied. In this study we investigated the synesthetic color and visual texture perceptions experienced in response to different types of “voice quality” (e.g., nasal, whisper, falsetto). Experiences of three different groups—self-reported voice synesthetes, phoneticians, and controls—were compared using both qualitative and quantitative analysis in a study conducted online. Whilst, in the qualitative analysis, synesthetes used more color and texture terms to describe voices than either phoneticians or controls, only weak differences, and many similarities, between groups were found in the quantitative analysis. Notable consistent results between groups were the matching of higher speech fundamental frequencies with lighter and redder colors, the matching of “whispery” voices with smoke-like textures, and the matching of “harsh” and “creaky” voices with textures resembling dry cracked soil. These data are discussed in the light of current thinking about definitions and categorizations of synesthesia, especially in cases where individuals apparently have a range of different synesthetic inducers.

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Section: Introductionmentioning

confidence: 99%

Color and texture associations in voice-induced synesthesia

Moos¹,

Simmons²,

Simner³

et al. 2013

Front. Psychol.

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“…However, visual inspection using a dendrograms 1 suggest that the data are reasonable. Furthermore, the perceptual similarity matrix has been extensively used to design intuitive database and browsing environments and allows for statistically significantly faster navigation [6] when compared to environments based on the trace transform [11]. However, the authors recognise that this possibility can by no means be dismissed and what is really required is to significantly increase the numbers of observers in the grouping experiments and we have planned a series of further observer sessions that we will analyse later this year.…”

Section: Discussionmentioning

confidence: 99%

“…These results were questioned by Heaps & Hande [8] who argued that a dimensional model of texture is inappropriate and that texture similarity judgements are context dependent. Better results have been obtained for computational similarity by Long & Leow [9] and Petrou et al [11] however, these again used the limited Brodatz album which provides a relatively sparse sampling of texture space.…”

Section: Introductionmentioning

confidence: 93%

“…For Experiment 2 the illumination and viewing conditions were kept constant (elevation 45 • and azimuth 135 • ) to provide one image per sample. These images were printed out onto photographic card (280g/m 2 ), dimensions 10.16 × 10.16cm, and a grouping experiment, similar to those used by Rao & Lohse [12] and Petrou et al [11], was carried out. Thirty participants were asked to sort the images into similar subsets; although due to the large size of the database, only six participants carried out a grouping experiment on the whole set.…”

Section: Texture Database and Perceptual Datamentioning

confidence: 99%

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Perceptual Similarity: A Texture Challenge

Clarke¹,

Halley²,

Newell³

et al. 2011

Procedings of the British Machine Vision Conference 2011

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Over the last thirty years evaluation of texture analysis algorithms has been dominated by two databases: Brodatz has typically been used to provide single images of approximately 100 texture classes, while CUReT consists of multiple images of 61 physical samples captured under a variety of illumination conditions. While many highly successful approaches have been developed for classification, the challenging question of measuring perceived inter-class texture similarity has rarely been addressed. In this paper we introduce a new texture database which includes a collection of 334 samples together with perceptual similarity data collected from experiments with 30 human observers. We have tested four of the leading texture algorithms and they provide accurate (≈ 100%) performance in a CUReT style classification task, however, a second experiment shows that resulting inter-class distances do not correlate well with the perceptual data.

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“…The Trace Transform (TT) [5] has characterized and classified successfully and automatically different types of images [6] [7] [8]. This method extracts features associated with the image using several functionals, one after another, to reduce the complexity of the image.…”

Section: Introductionmentioning

confidence: 99%

Sensibility analysis of the Trace Transform on land coverage images

Brenes

Torres-Rojas

Ossa

2015

2015 4th International Work Conference on Bioinspired Intelligence (IWOBI)

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Aerial or satellital images may be used to produce terrain coverage maps, which in time are a very useful and important tool for decision-making in several fields including biodiversity, telecommunications and natural disaster management. The Trace Transform method can be used to process these images. This method extracts features from the images by applying a series of functionals to produce a numeric representation that will be used for classification later on. This model depends on several factors in order to have an efficient operation, among them, the frequency parameters of the traces, classifier type and land coverage. Experimentation on feature extraction time and precision rate revealed that the frequency parameters, specially ∆τ , and the classifier type can affect both of them.

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Reverse engineering the way humans rank textures

Cited by 9 publications

References 24 publications

Color and texture associations in voice-induced synesthesia

Color and texture associations in voice-induced synesthesia

Perceptual Similarity: A Texture Challenge

Sensibility analysis of the Trace Transform on land coverage images

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