Provably scale-covariant networks from oriented quasi quadrature measures in cascade

This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. Scale-space properties of the computational primitives are analysed and it is shown that the resulting representation allows for provable scale and rotation covariance. A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets.Comment: 12 pages, 3 figures, 1 tabl

Multi-Scale Colour Completed Local Binary Patterns for Scene and Event Sport Image Categorisation

The Local Binary Pattern (LBP) texture descriptor and some of its variant descriptors have been successfully used for texture classification and for a few other tasks such as face recognition, facial expression, and texture segmentation. However, these descriptors have been barely used for image categorisation because their calculations are based on the gray image and they are only invariant to monotonic light variations on the gray level. These descriptors ignore colour information despite their key role in distinguishing the objects and the natural scenes. In this paper, we enhance the Completed Local Binary Pattern (CLBP), an LBP variant with an impressive performance on texture classification. We propose five multiscale colour CLBP (CCLBP) descriptors by incorporating five different colour information into the original CLBP. By using the Oliva and Torralba (OT8) and Event sport datasets, our results attest to the superiority of the proposed CCLBP descriptors over the original CLBP in terms of image categorisation

Evaluating color texture descriptors under large variations of controlled lighting conditions

The recognition of color texture under varying lighting conditions is still an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than the others. In this paper we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how they are affected by small and large variation in the lighting conditions. The evaluation is performed on a new texture database including 68 samples of raw food acquired under 46 conditions that present single and combined variations of light color, direction and intensity. The database allows to systematically investigate the robustness of texture descriptors across a large range of variations of imaging conditions.Comment: Submitted to the Journal of the Optical Society of America

Adaptive CSLBP compressed image hashing

Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

This paper compares the effects of colour pre-processing on the classification performance of H&E-stained images. Variations in the tissue preparation procedures, acquisition systems, stain conditions and reagents are all source of artifacts that can affect negatively computer-based classification. Pre-processing methods such as colour constancy, transfer and deconvolution have been proposed to compensate the artifacts. In this paper we compare quantitatively the combined effect of six colour pre-processing procedures and 12 colour texture descriptors on patch-based classification of H&E-stained images. We found that colour pre-processing had negative effects on accuracy in most cases – particularly when used with colour descriptors. However, some pre-processing procedures proved beneficial when employed in conjunction with classic texture descriptors such as co-occurrence matrices, Gabor filters and Local Binary Patterns

Automated interpretation of benthic stereo imagery

Autonomous benthic imaging, reduces human risk and increases the amount of collected data. However, manually interpreting these high volumes of data is onerous, time consuming and in many cases, infeasible. The objective of this thesis is to improve the scientific utility of the large image datasets. Fine-scale terrain complexity is typically quantified by rugosity and measured by divers using chains and tape measures. This thesis proposes a new technique for measuring terrain complexity from 3D stereo image reconstructions, which is non-contact and can be calculated at multiple scales over large spatial extents. Using robots, terrain complexity can be measured without endangering humans, beyond scuba depths. Results show that this approach is more robust, flexible and easily repeatable than traditional methods. These proposed terrain complexity features are combined with visual colour and texture descriptors and applied to classifying imagery. New multi-dataset feature selection methods are proposed for performing feature selection across multiple datasets, and are shown to improve the overall classification performance. The results show that the most informative predictors of benthic habitat types are the new terrain complexity measurements. This thesis presents a method that aims to reduce human labelling effort, while maximising classification performance by combining pre-clustering with active learning. The results support that utilising the structure of the unlabelled data in conjunction with uncertainty sampling can significantly reduce the number of labels required for a given level of accuracy. Typically 0.00001–0.00007% of image data is annotated and processed for science purposes (20–50 points in 1–2% of the images). This thesis proposes a framework that uses existing human-annotated point labels to train a superpixel-based automated classification system, which can extrapolate the classified results to every pixel across all the images of an entire survey

Automated interpretation of benthic stereo imagery

Autonomous benthic imaging, reduces human risk and increases the amount of collected data. However, manually interpreting these high volumes of data is onerous, time consuming and in many cases, infeasible. The objective of this thesis is to improve the scientific utility of the large image datasets. Fine-scale terrain complexity is typically quantified by rugosity and measured by divers using chains and tape measures. This thesis proposes a new technique for measuring terrain complexity from 3D stereo image reconstructions, which is non-contact and can be calculated at multiple scales over large spatial extents. Using robots, terrain complexity can be measured without endangering humans, beyond scuba depths. Results show that this approach is more robust, flexible and easily repeatable than traditional methods. These proposed terrain complexity features are combined with visual colour and texture descriptors and applied to classifying imagery. New multi-dataset feature selection methods are proposed for performing feature selection across multiple datasets, and are shown to improve the overall classification performance. The results show that the most informative predictors of benthic habitat types are the new terrain complexity measurements. This thesis presents a method that aims to reduce human labelling effort, while maximising classification performance by combining pre-clustering with active learning. The results support that utilising the structure of the unlabelled data in conjunction with uncertainty sampling can significantly reduce the number of labels required for a given level of accuracy. Typically 0.00001–0.00007% of image data is annotated and processed for science purposes (20–50 points in 1–2% of the images). This thesis proposes a framework that uses existing human-annotated point labels to train a superpixel-based automated classification system, which can extrapolate the classified results to every pixel across all the images of an entire survey