Texture analysis using Gabor wavelets

Receptive field profiles of simple cells in the visual cortex have been shown to resemble even- symmetric or odd-symmetric Gabor filters. Computational models employed in the analysis of textures have been motivated by two-dimensional Gabor functions arranged in a multi-channel architecture. More recently wavelets have emerged as a powerful tool for non-stationary signal analysis capable of encoding scale-space information efficiently. A multi-resolution implementation in the form of a dyadic decomposition of the signal of interest has been popularized by many researchers. In this paper, Gabor wavelet configured in a \u27rosette\u27 fashion is used as a multi-channel filter-bank feature extractor for texture classification. The \u27rosette\u27 spans 360 degrees of orientation and covers frequencies from dc. In the proposed algorithm, the texture images are decomposed by the Gabor wavelet configuration and the feature vectors corresponding to the mean of the outputs of the multi-channel filters extracted. A minimum distance classifier is used in the classification procedure. As a comparison the Gabor filter has been used to classify the same texture images from the Brodatz album and the results indicate the superior discriminatory characteristics of the Gabor wavelet. With the test images used it can be concluded that the Gabor wavelet model is a better approximation of the cortical cell receptive field profiles

Hybrid predictive/VQ lossless image coding

A multiplicative autoregressive model is used in a lossless predictive image coding scheme. The use of vector quantisation (VQ) for compression of the model coefficients leads to an improved compression ratio. Both image adaptive and universal codebooks are considered. A comparative analysis of the new coder is presented through simulation results

Shape VQ-based adaptive predictive lossless image coder

A new shape adaptive predictive lossless image coder is proposed. Three classes of block shapes are delineated with associated “optimum” predctors. Each image is partitioned into sub-blocks that are classified into one of the three classes using vector quantisation. The encoder then employs the predictor corresponding to the class of the block under consideration. Performance evaluation of the proposed coder in comparison with four other lossless coders includmg lossless JPEG indicates its superiority

Fall risk assessment in older people using inertial sensors

Abstract of paper that was presented at The 12th National Conference of Emerging Researchers in Ageing

Knowledge-based semantic image segmentation and global precedence effect

This paper introduces a knowledge-based semantic image segmentation which extracts the object(s)-of-interest from the image. Image templates are the high-level knowledge in the system. The major contribution of this work is the use of the Global Precedence Effect (forest before trees) of the human visual system (HVS) in image analysis and understanding. The object-of-interest is searched for hierarchically through an irregular pyramid by an affine invariant comparison between the different region combinations and the template starting from lowest to the highest resolutions. The global/large size objects are found at lower resolutions with significantly lower computational complexity

Adaptive bag-of-visual word modelling using stacked-autoencoder and particle swarm optimisation for the unsupervised categorisation of images

© The Institution of Engineering and Technology 2020 The bag-of-visual words (BOVWs) have been recognised as an effective mean of representing images for image classification. However, its reliance on a visual codebook developed using handcrafted image feature extraction algorithms and vector quantisation via k-means clustering often results in significant computational overhead, and poor classification accuracies. Therefore, this study presents an adaptive BOVW modelling, in which image feature extraction is achieved using deep feature learning and the amount of computation required for the development of visual codebook is minimised using a batch implementation of particle swarm optimisation. The proposed method is tested using Caltech-101 image dataset, and the results confirm the suitability of the proposed method in improving the categorisation performance while reducing the computational load

Wavelet-based feature-adaptive adaptive resonance theory neural network for texture identification

A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extraction based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ‘‘lowlevel’’ features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ‘‘cognitive system.’’ The novelty of the model developed in this paper lies in the use of a self-organizing input layer to the fuzzy ART. Evaluation of the model is performed by using natural textures, and results obtained show that the developed model is capable of performing the texture recognition task effectively. Applications of the developed model include the study of artificial vision systems motivated by the human visual system model

3D geometric modelling of hand-woven textile

Geometric modeling and haptic rendering of textile has attracted significant interest over the last decade. A haptic representation is created by adding the physical properties of an object to its geometric configuration. While research has been conducted into geometric modeling of fabric, current systems require time-consuming manual recognition of textile specifications and data entry. The development of a generic approach for construction of the 3D geometric model of a woven textile is pursued in this work. The geometric model would be superimposed by a haptic model in the future work. The focus at this stage is on hand-woven textile artifacts for display in museums. A fuzzy rule based algorithm is applied to the still images of the artifacts to generate the 3D model. The derived model is exported as a 3D VRML model of the textile for visual representation and haptic rendering. An overview of the approach is provided and the developed algorithm is described. The approach is validated by applying the algorithm to different textile samples and comparing the produced models with the actual structure and pattern of the samples

An analysis of medical image processing methods for segmentation of the inner ear

This study explores software development methods and subsequent results for delineation of the inner ear using medical image processing techniques with clinical relevance such as for pre- and post- operative evaluations, surgical planning and exploration. Methods for data acquisition and segmentation of ilmer ear anatomy, specifically the cochlea, are analyzed. Segmentation methods for extracting and rendering the cochlea from Computed Tomography are implemented using an ITK/VTK approach, and results are provided for comparison. These include variations of region-growing, threshold-based and level set segmentation methods. The analysis focuses on image acquisition, registration and extraction of the complex cochlear spiral and surrounding anatomy, with previous comparisons reviewing a broadspectrum of medical image segmentation strategies. The review is intended to provide a comparative analysis of recent methods in segmentation of middle and inner ear anatomy, and ensuing results in this field of medical image processing

Automatic Affect Perception Based on Body Gait and Posture: A Survey

There has been a growing interest in machine-based recognition of emotions from body gait and its combination with other modalities. In order to highlight the major trends and state of the art in this area, the literature dealing with machine-based human emotion perception through gait and posture is explored. Initially the effectiveness of human intellect and intuition in perceiving emotions in a range of cultures is examined. Subsequently, major studies in machine-based affect recognition are reviewed and their performance is compared. The survey concludes by critically analysing some of the issues raised in affect recognition using gait and posture, and identifying gaps in the current understanding in this area