The paradigm of visual attention has been widely investigated and applied to many computer vision applications. In this study, the authors propose a new saliency-based visual attention algorithm applied to object acquisition. The proposed algorithm automatically extracts points of visual attention (PVA) in the scene, based on different feature saliency maps. Each saliency map represents a specific feature domain, such as textural, contrast, and statistical-based features. A feature selection, based on probability of detection and false alarm rate and repeatability criteria, is proposed to choose the most efficient feature combination for saliency map. Motivated by the assumption that the extracted PVA represents the most visually salient regions in the image, they suggest using the visual attention approach for object acquisition. A comparison with other well-known algorithms for point of interest detection shows that the proposed algorithm performs better. The proposed algorithm was successfully tested on synthetic, charge-coupled device (CCD), and infrared (IR) images. Evaluation of the algorithm for object acquisition, based on ground truth, is carried out using synthetic images, which contain multiple examples of objects, with various sizes and brightness levels. A high probability of correct detection (greater than 90%) with a low false alarm rate (about 20 false alarms per image) was achieved.

Due to inherent characteristics of multiscale and orientation, normalised Gabor features have been successfully used in face recognition. Various previous works have showcased the strength and feasibility of this approach, especially on its robustness against local variations. However, the projected features are numerous and substantial in dimension, which is largely due to the convolution of multiscale and orientation of wavelets. Such features, when used in practical face recognition, would require relatively lengthy classification process, particularly when it involves computationally extensive local classifier or experts, such as ensembles of local cosine similarity (ELCS) classifier. The authors address this issue by simultaneously reducing the size of Gabor features laterally and locally using a manifold learning method called locally linear embedding (LLE). This method is thus denoted as locally lateral normalised local Gabor feature vector with LLE (LGFV/LN/LLE). Results on several publicly available face datasets reveal the superiority of the authors’ approach in terms of improvements in feature compression of LGFV features by up to a reduction of 95% of total dimensionality while increasing the average classification accuracy by 26%. Altogether, the authors show that their LGFV/LN/LLE augmented by ELCS classifiers delivers equivalent result when compared against the state-of-the-art.

The AKAZE algorithm is a typical image registration algorithm that has the advantage of high computational efficiency based on non-linear diffusion. However, it is weaker than the scale-invariant feature transformation (SIFT) algorithm in terms of robustness and stability. We propose a new and improved version of the AKAZE algorithm by using the SIFT descriptor based on sparse random projection (SRP). The proposed method not only retains the advantage of high efficiency of the AKAZE algorithm in feature detection but also has the stability of the SIFT descriptor. Moreover, the computational complexity due to the high dimension of the SIFT descriptor, which limits the speed of feature matching, is drastically reduced by the SRP strategy. Experiments on several benchmark image datasets demonstrate that the proposed algorithm can significantly improve the stability of the AKAZE algorithm, and the results suggest the better matching performance and robustness of the feature descriptor.

This study introduces an extension of the sGLOH2 local image descriptor inspired by RootSIFT ‘square rooting’ as a way to indirectly alter the matching distance used to compare the descriptor vectors. The extended descriptor, named RootsGLOH2, achieved the best results in terms of matching accuracy and robustness among the latest state-of-the-art non-deep descriptors in recent evaluation contests dealing with both planar and non-planar scenes. RootsGLOH2 also achieves a matching accuracy very close to that obtained by the best deep descriptors to date. Beside confirming that ‘square rooting’ has beneficial effects on sGLOH2 as it happens on SIFT, experimental evidence shows that classical norm-based distances, such as the Euclidean and Manhattan distances, only provide suboptimal solutions to the problem of local image descriptor matching. This suggests matching distance design as a topic to investigate further in the near future.

A feature-based retinal image registration (RIR) technique aligns multiple fundus images and composed of pre-processing, feature point extraction, feature descriptor, matching and geometrical transformation. Challenges in RIR include difference in scaling, intensity and rotation between images. The scale and intensity differences can be minimised with consistent imaging setup and image enhancement during the pre-processing, respectively. The rotation can be addressed with feature descriptor method that robust to varying rotation. Therefore, a feature descriptor method is proposed based on statistical properties (FiSP) to describe the circular region surrounding the feature point. From the experiments on public Fundus Image Registration dataset, FiSP established 99.227% average correct matches for rotations between 0° and 180°. Then, FiSP is paired with Harris corner, scale-invariant feature transform (SIFT), speeded-up robust feature (SURF), Ghassabi's and D-Saddle feature point extraction methods to assess its registration performance and compare with the existing feature-based RIR techniques, namely generalised dual-bootstrap iterative closet point (GDB-ICP), Harris-partial intensity invariant feature descriptor (PIIFD), Ghassabi's–SIFT, H-M 16, H-M 17 and D-Saddle–histogram of oriented gradients (HOG). The combination of SIFT–FiSP registered 64.179% of the image pairs and significantly outperformed other techniques with mean difference between 25.373 and 60.448% (p = <0.001*).

This study proposes a distinctive and robust spatial and geometric histograms (SGHs) feature descriptor for three-dimensional (3D) local surface description. The authors also introduce a new local reference frame for the generation of their SGH descriptor. To fully describe a local surface, the SGH descriptor considers both spatial distribution and geometrical characteristics in its underlying support region. To encode neighbourhood information, the SGH descriptor is constructed using histogram statistics with spatial partition and interpolation strategies. The performance of the SGH descriptor was rigorously tested on six public datasets for applications of both 3D object recognition and registration. Compared to eight state-of-the-art descriptors, experimental results show that SGH achieves the best performance on noise-free data. It also produces the best results even under different nuisances. The promising descriptiveness and robustness of their SGH descriptor have been fully demonstrated.

Representation of dynamic textures (DTs), well-known as a sequence of moving textures, is a challenging problem in video analysis due to the disorientation of motion features. Analysing DTs to make them ‘understandable’ plays an important role in different applications of computer vision. In this study, an efficient approach for DT description is proposed by addressing the following novel concepts. First, the beneficial properties of dense trajectories are exploited for the first time to efficiently describe DTs instead of the whole video. Second, two substantial extensions of local vector pattern operator are introduced to form a completed model which is based on complemented components to enhance its performance in encoding directional features of motion points in a trajectory. Finally, the authors present a new framework, called directional dense trajectory patterns, which takes advantage of directional beams of dense trajectories along with spatio-temporal features of their motion points in order to construct dense-trajectory-based descriptors with more robustness. Evaluations of DT recognition on different benchmark datasets (i.e. UCLA, DynTex, and DynTex++) have verified the interest of the authors’ proposal.