Robust Texture Classification Research Articles

Simultaneous Velocity and Texture Classification from a Neuromorphic Tactile Sensor Using Spiking Neural Networks

The neuroTac, a neuromorphic visuo-tactile sensor that leverages the high temporal resolution of event-based cameras, is ideally suited to applications in robotic manipulators and prosthetic devices. In this paper, we pair the neuroTac with Spiking Neural Networks (SNNs) to achieve a movement-invariant neuromorphic tactile sensing method for robust texture classification. Alongside this, we demonstrate the ability of this approach to extract movement profiles from purely tactile data. Our systems achieve accuracies of 95% and 83% across their respective tasks (texture and movement classification). We then seek to reduce the size and spiking activity of our networks with the aim of deployment to edge neuromorphic hardware. This multi-objective optimisation investigation using Pareto frontiers highlights several design trade-offs, where high activity and large network sizes can both be reduced by up to 68% and 94% at the cost of slight decreases in accuracy (8%).

Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform. The multi-layered perceptron neural network is employed as an image texture classifier. The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.

Cross-Complementary Local Binary Pattern for Robust Texture Classification

The performance of local binary pattern (LBP) and many LBP-based variants is usually limited by rotation, illumination, scale, viewpoint, and the number of training samples. In view of this, this letter presents a robust image descriptor named crosscomplementary LBP (CCLBP) for texture classification. Based on the continuous rotation invariance and highly discriminative characteristic of principal curvatures, significant local geometrical information, which is complementary to LBP is obtained. Then, the resulting information is quantized and encoded into a binary pattern. To enhance the robustness to scale, viewpoint, and the number of training samples, a multiscale and multiresolution analysis is explored by diversifying two parameters accordantly. Subsequently, a cross-scale joint feature representation is conducted on the generated complementary binary responses, resulting in the proposed CCLBP, which captures a highly discriminative information but with low dimensionality. Experimental results on three standard texture databases demonstrate that the proposed CCLBP achieves competitive performance or outperforms state-of-the-art texture descriptors while enjoying a succinct feature representation. Impressively, under the premise of maintaining complete rotation invariance, the performance of the CCLBP approach against illumination, viewpoint, and scale changes has been improved, especially when the number of training samples is limited.

Robust Texture Classification by Aggregating Pixel-Based LBP Statistics

This letter addresses the texture classification problem through a pixel-based local binary pattern (LBP) statistics aggregation mechanism. Real-world texture images often present challenges for classification algorithms in terms of intra-class variability due, among others, to variable illumination. The LBP operator, a state-of-the-art texture descriptor, possesses key properties for tackling real-world texture images: discriminative power and invariance against monotonic gray level changes. We propose a novel texture classification approach that increases the robustness of LBP-based methods with respect to any type of intra-class variations. The method locally characterizes each pixel with an LBP code histogram and globally computes the label of a textured image by aggregating pixel labels through a voting process. Our approach can be in principle applied to any LBP version, as it focuses on how statistics are computed from LBP codes. We show that the proposed pixel-based approach improves upon traditional LBP block-based approaches in terms of classification accuracy by up to 5.1 p.p. on the public Outex database for the classic LBP with various neighborhoods as well as for various LBP extensions.

Matrix based cyclic spectral estimator for fast and robust texture classification

Utilization of cyclostationarity is a fresh paradigm in texture classification. This paper employs the Strip Spectral Correlation Analyzer (SSCA) as the new and superior method of such a category. The SSCA has been much more computational efficient than the other spectral correlation estimators, such as the FFT-Accumulated Method (FAM) or Direct Frequency Smoothing (DFS). Further, for comparable efficacy of the cyclostationary based analyzers, two new algorithms for implementation of both SSCA and FAM are proposed. The algorithms are fast, parallel, and linear-algebraic based, which brings many advantages in computational competence, feature generation flexibility, simplicity, and hardware implementation. SSCA as the unused promising texture analyzer and the new FAM implementation are compared with other state of the art methods in the case of classification accuracy, noise resistance and feature efficiency.

Radon Representation-Based Feature Descriptor for Texture Classification

In this paper, we aim to handle the intraclass variation resulting from the geometric transformation and the illumination change for more robust texture classification. To this end, we propose a novel feature descriptor called Radon representation-based feature descriptor (RRFD). RRFD converts the original pixel represented images into Radon-pixel images by using the Radon transform. The new Radon-pixel representation is more informative in geometry and has a much lower dimension. Subsequently, RRFD efficiently achieves affine invariance by projecting an image (or an image patch) from the space of Radon-pixel pairs onto an invariant feature space by using a ratiogram, i.e., the histogram of ratios between the areas of triangle pairs. The illumination invariance is also achieved by defining an illumination invariant distance metric on the invariant feature space. Comparing to the existing Radon transform-based texture features, which only achieve rotation and/or scaling invariance, RRFD achieves affine invariance. The experimental results on CUReT show that RRFD is a powerful feature descriptor that is suitable for texture classification.