The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Preattentive texture discrimination with early vision mechanisms
Abstract
We present a model of human preattentive texture perception. This model consists of three stages: (1) convolution of the image with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses modeling outputs of V1 simple cells, (2) inhibition, localized in space, within and among the neural-response profiles that results in the suppression of weak responses when there are strong responses at the same or nearby locations, and (3) texture-boundary detection by using wide odd-symmetric mechanisms. Our model can predict the salience of texture boundaries in any arbitrary gray-scale image. A computer implementation of this model has been tested on many of the classic stimuli from psychophysical literature. Quantitative predictions of the degree of discriminability of different texture pairs match well with experimental measurements of discriminability in human observers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Textons, the elements of texture perception, and their interactions
Julesz, B. (1981) · Nature · doi:10.1038/290091a0 · Cited by 1,703
Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Describing Textures in the Wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., & Vedaldi, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.461 · Cited by 2,438
Unsupervised texture segmentation using Gabor filters
Jain, A. K., & Farrokhnia, F. (1991) · Pattern Recognition · doi:10.1016/0031-3203(91)90143-s · Cited by 2,182
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511
Self-citationRepresenting and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons
Leung, T., & Malik, J. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011126920638 · Cited by 1,498
Self-citationDollar, P., Tu, Z., Perona, P., & Belongie, S. (2009) · Procedings of the British Machine Vision Conference 2009 · doi:10.5244/c.23.91 · Cited by 1,237
Self-citationResidual Networks Behave Like Ensembles of Relatively Shallow Networks
Veit, A., Wilber, M., & Belongie, S. (2016) · arXiv · doi:10.48550/arxiv.1605.06431 · Cited by 705
A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques
Xie, X. (2008) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.268 · Cited by 500
Figure—Ground Segregation in a Recurrent Network Architecture
Roelfsema, P. R., Lamme, V. A. F., Spekreijse, H., & Bosch, H. (2002) · Journal of Cognitive Neuroscience · doi:10.1162/08989290260045756 · Cited by 254
Blindsight in normal observers
Kolb, F. C., & Braun, J. (1995) · Nature · doi:10.1038/377336a0 · Cited by 198
Learning optimized features for hierarchical models of invariant object recognition.
Wersing, H., & Körner, E. (2003) · Neural Computation · doi:10.1162/089976603321891800 · Cited by 169
Incremental grouping of image elements in vision
Roelfsema, P. R., & Houtkamp, R. (2011) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-011-0200-0 · Cited by 112
Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2578328 · Cited by 102
Self-citationVisual learning of texture descriptors for facial expression recognition in thermal imagery
Hernández, B., Olague, G., Hammoud, R., Trujillo, L., & Romero, E. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.08.012 · Cited by 97
Li, H., Su, X., Wang, J., Kan, H., Han, T., Zeng, Y., & Chai, X. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.11.001 · Cited by 59
A critical analysis on perceptual contrast and its use in visual information analysis and processing
Beghdadi, A., Qureshi, M. A., Amirshahi, S. A., Chetouani, A., & Pedersen, M. (2020) · IEEE Access · doi:10.1109/access.2020.3019350 · Cited by 31
Volumetric Semantic Segmentation Using Pyramid Context Features
Barron, J. T., Biggin, M. D., Arbelaez, P., Knowles, D. W., Keranen, S. V. E., & Malik, J. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.428 · Cited by 15
Self-citation
Metadata
- Authors
- Malik, J., & Perona, P.
- Venue
- Journal of the Optical Society of America A
- Publisher
- Optica Publishing Group
- Published
- 1990-05-01
- Volume
- 7
- Issue
- 5
- Pages
- 923
- ISSN
- 1084-7529 (ISSN-L)
- 1520-8532
- OpenAlex
- W2115738369
- Cited by
- 960
- References
- 1
- FWCI
- 13.26
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
