Dynamic weather conditions, which mainly include rain and snow, make prevailing algorithms for many applications of outdoor video analysis and computer vision lapse. To remove dynamic weather conditions, the authors propose a pixel-wise framework combining a detection method with a removal approach. Dynamic weather conditions are detected by a strategy-driven state transition, which integrates static initialisation using K-means clustering with dynamic maintenance of Gaussian mixture model. Moreover, a variable time window is presented for removal of rain and snow. Each component of the framework is addressed using detailed descriptions of corresponding algorithms. Experiments demonstrate the effectiveness of the method on detection and removal of dynamic weather conditions.

Firefly algorithm (FA) is a new meta-heuristic optimisation algorithm that mimics the social behaviour of fireflies flying in the tropical and temperate summer sky. In this study, a novel application of FA is presented as it is applied to solve tracking problem. A general optimisation-based tracking architecture is proposed and the parameters’ sensitivity and adjustment of the FA in tracking system are studied. Experimental results show that the FA-based tracker can robustly track an arbitrary target in various challenging conditions. The authors compare the speed and accuracy of the FA with three typical tracking algorithms including the particle filter, meanshift and particle swarm optimisation. Comparative results show that the FA-based tracker outperforms the other three trackers.

The ‘gaze estimation’ problem, because of its manifold applications including human–computer interaction especially for the handicapped, has been a topic of research for many years. Recently, thanks to technological advances, non-intrusive methods based on image processing employed in broader applications, are addressed more than before. One of the promising approaches to gaze estimation is based on projective geometry. In projective geometry-based approaches, an ellipse is fitted to the image of iris boundary and from its parameters, two solutions are obtained, only one of which is valid for the gaze direction. Since the ellipse parameters are not adequate for disambiguation, in a previous work, the accurate coordinates of eye corners in three-dimensional, as complementary information, is obtained through another system increasing the cost and complexity of the overall system. In this article, a new technique to select the valid solution, based on eye geometry and iris image, is developed. In the proposed technique, relative position of the pupil centre with respect to the iris centre is used as the complementary information and a novel algorithm is proposed for its extraction. The performance of the proposed technique was evaluated on 600 real images and with only one failure on selecting the valid solution demonstrated an accuracy of 99.8% for disambiguation.

This study describes a complete system for multiple-target tracking in image sequences. The target appearance is represented as a set of weighted clusters in colour space. This is in contrast to the more typical use of colour histograms to model target appearance. The use of clusters allows a more flexible and accurate representation of the target, which demonstrates the benefits for tracking. However, it also introduces a number of computational difficulties, as calculating and matching cluster signatures are both computationally intensive tasks. To overcome this, the authors introduce a new formulation of incremental medoid-shift clustering that operates faster than mean shift in multi-target tracking scenarios. This matching scheme is integrated into a Bayesian tracking framework. Particle filters, a special case of Bayesian filters where the state variables are non-linear and non-Gaussian, are used in this study. An adaptive model update procedure is proposed for the cluster signature representation to handle target changes with time. The model update procedure is demonstrated to work successfully on a synthetic dataset and then on real datasets. Successful tracking results are shown on public datasets. Both qualitative and quantitative evaluations have been carried out to demonstrate the improved performance of the proposed multi-target tracking system. A higher tracking accuracy in long image sequences has been achieved compared to other standard tracking methods.

How to set the cluster number plays a key role in many clustering applications. To address this issue, this study introduces an automatic clustering method based on evolutionary algorithms (EAs). The basic idea is to convert a clustering problem into a global optimisation problem and tackle it by an EA. A new validity index, which balances the inter-cluster consistency and the intra-cluster consistency, is proposed to be the objective function. Three adaptive coding schemes, which can deal with variable-length optimisation problems by using a fixed-length chromosome, are designed to detect the cluster number automatically. The validity index and adaptive coding schemes are incorporated in an EA for automatic clustering. The authors approach is compared with some widely used validity indices and an adaptive coding scheme on some artificial data sets and two real-world problems. The experimental results suggest that their method not only successfully detects the correct cluster numbers but also achieve stable results for most of test problems.

This study presents a new unsupervised hybrid classifier for natural texture identification in aerial images. The proposed strategy combines through the fuzzy integral (FI) six well-tested base supervised classifiers. This automation is based on the generation of a general rule inferred through decision tree learning, ID3 strategy from the training data. This rule allows generation of a partition of the set of images that the base classifiers use to estimate automatically their parameters. These parameters are the inputs to calculate the relative importance of each classifier in their combination by the FI. The resulting classifier has been compared with related techniques getting an improvement of 8.04% average. The study includes discussion on this comparison.

The authors present a new approach for affine distorted planar curve matching and exploit it for occluded object recognition. There are two main contributions in the study: First, a novel affine-invariant curve descriptor (AICD) based on a new-defined affine-invariant signature and its unsigned sum is proposed to represent the local shape of a curve with high distinctiveness. Second, a part-to-part curve matching algorithm is developed by combining AICD with a curve segmentation strategy based on inflexion points, which can be applied to object recognition under affine distortions and partial occlusions. Experimental results demonstrate that the proposed method exhibits effectiveness in occluded object recognition better than the state-of-the-art partial curve matching methods.