Unsupervised texture segmentation using Gabor filters
Anil K. Jain,Farshid Farrokhnia +1 more
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TLDR
A texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system is presented, which is based on reconstruction of the input image from the filtered images.About:
This article is published in Pattern Recognition.The article was published on 1991-12-01 and is currently open access. It has received 2351 citations till now. The article focuses on the topics: Image texture & Texture filtering.read more
Citations
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Conglomeration of Hand Shapes and Texture Information for Recognizing Gestures of Indian Sign Language Using Feed forward Neural Networks
TL;DR: This research paper highlights the use of shape and texture information for recognizing gestures of Indian sign language by extracting the hand segments from the original color gesture images and subjecting them to further processing.
Proceedings ArticleDOI
Spatial Texture Models for Video Compression
TL;DR: This paper implemented texture techniques and segmentation strategies in order to detect texture regions in video sequences using temporal motion techniques and is able to show an improvement over previous texture-based implementations in terms of compression efficiency.
Journal ArticleDOI
Sparse Texture Active Contour
TL;DR: A segmentation scheme based on the sparse representation of the texture information is proposed, and a non-parametric texture modeling technique is provided, but also the sparsity of the representation guarantees the computation efficiency.
Journal ArticleDOI
The Semi-Variogram and Spectral Distortion Measures for Image Texture Retrieval
TL;DR: Experimental results obtained from testing the proposed approach using the complete Brodatz database, and the University of Illinois at Urbana-Champaign texture database, suggests the effectiveness of the proposed Approach as a single-feature-based dissimilarity measure for real-time texture retrieval.
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Affective database for e-learning and classroom environments using Indian students’ faces, hand gestures and body postures
TL;DR: A new affective database for both the e-learning and classroom environments using the students’ facial expressions, hand gestures, and body postures is proposed and the classification accuracy of the database is analyzed using a few state-of-the-art machine and deep learning techniques.
References
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A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
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Statistical and structural approaches to texture
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
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Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.
TL;DR: Evidence is presented that the 2D receptive-field profiles of simple cells in mammalian visual cortex are well described by members of this optimal 2D filter family, and thus such visual neurons could be said to optimize the general uncertainty relations for joint 2D-spatial-2D-spectral information resolution.