Numerous scenarios exist where it is necessary or advantageous to classify surface material at a distance from a moving forward-facing camera. Examples include the use of image based sensors for assessing and predicting terrain type in association with the control or navigation of autonomous vehicles. In many real scenarios, the upcoming terrain might not just be flat but may also be oblique and vehicles may need to change speed and gear to ensure safe and clean motion.
Blur-robust texture features
Videos captured with moving cameras, particularly those attached to biped robots, often exhibit blur due to incorrect focus or slow shutter speed. Blurring effects generally alter the spatial and frequency characteristics of the content and this may reduce the performance of a classifier. Robust texture features are therefore developed to deal with this problem. [Matlab Code]
Terrain classification from body-mounted cameras during human locomotion
A novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion is introduced. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to support terrain classification. Gait is taken into account in two ways. Firstly, for key frame selection, when regions with homogeneous texture characteristics are updated, the frequency variations of the textured surface are analysed and used to adaptively define filter coefficients. Secondly, it is incorporated in the parameter estimation process where probabilities of path consistency are employed to improve terrain-type estimation [Matlab Code]. Figures below show the proposed process of terrain classification for tracked regions and a result. [PDF]
Label 1 (green), Label 2 (red) and Label 3 (blue) correspond to the areas classified as hard surfaces, soft surfaces and unwalkable areas, respectively. The size of the circle indicates probabilities – bigger implies higher confidence of classification.
Planar orientation estimation by texture
The gradient of a road or terrain influences the appropriate speed and power of a vehicle traversing it. Therefore, gradient prediction is necessary if autonomous vehicles are to optimise their locomotion. A novel texture-based method for estimating the orientation of planar surfaces under the basic assumption of homogeneity has been developed for scenarios that only a single image source exists, which also includes where a region of interest is too further to employ a depth estimation technique.
Terrain classification from body-mounted cameras during human locomotion. N. Anantrasirichai, J. Burn and David Bull. IEEE Transactions on Cybernetics. [PDF] [Matlab Code].
Projective image restoration using sparsity regularization. N. Anantrasirichai, J. Burn and David Bull. ICIP 2013. [PDF] [Matlab Code]
Robust texture features for blurred images using undecimated dual-tree complex wavelets. N. Anantrasirichai, J. Burn and David Bull. ICIP 2014. [PDF] [Matlab Code]
Orientation estimation for planar textured surfaces based on complex wavelets. N. Anantrasirichai, J. Burn and David Bull. ICIP 2014. [PDF]
Robust texture features based on undecimated dual-tree complex wavelets and local magnitude binary patterns. N. Anantrasirichai, J. Burn and David Bull. ICIP 2015. [PDF]
Various types of atmospheric distortion can influence the visual quality of video signals during acquisition. Typical distortions include fog or haze which reduce contrast, and atmospheric turbulence due to temperature variations or aerosols. An effect of temperature variation is observed as a change in the interference pattern of the light refraction, causing unclear, unsharp, waving images of the objects. This obviously makes the acquired imagery difficult to interpret.
This project introduced a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to provide accurate detail from objects behind the distorting layer, a simple and efficient frame selection method is proposed to pick informative ROIs from only good-quality frames. We solve the space-variant distortion problem using region-based fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). We also propose an object alignment method for pre-processing the ROI since this can exhibit significant offsets and distortions between frames. Simple haze removal is used as the final step. We refer to this algorithm as CLEAR (for code please contact me) (Complex waveLEt fusion for Atmospheric tuRbulence). [PDF] [VIDEOS]
Atmospheric turbulence mitigation using complex wavelet-based fusion. N. Anantrasirichai, Alin Achim, Nick Kingsbury, and David Bull. IEEE Transactions on Image Processing. [PDF] [Sequences] [Code: please contact me]
Mitigating the effects of atmospheric distortion using DT-CWT fusion. N. Anantrasirichai, Alin Achim, David Bull, and Nick Kingsbury. In Proceedings of the IEEE International Conference on Image Processing (ICIP 2012). [PDF] [BibTeX]
Mitigating the effects of atmospheric distortion on video imagery : A review. University of Bristol, 2011. [PDF]
Mitigating the effects of atmospheric distortion. University of Bristol, 2012. [PDF]
Texture-preserving image enhancement for Optical Coherence Tomography
This project developed novel image enhancement algorithms for retinal optical coherence tomography (OCT). These images contain a large amount of speckle causing them to be grainy and of very low contrast. To make these images valuable for clinical interpretation, our method offers speckle removal, while preserving useful information contained in each retinal layer starts with multi-scale despeckling based on a dual-tree complex wavelet transform (DT-CWT). The OCT image is further enhanced through a smoothing process that uses a novel adaptive-weighted bilateral filter (AWBF). This offers the desirable property of preserving texture within the OCT image layers. The enhanced OCT image is then segmented to extract inner retinal layers that contain useful information for eye research. Our layer segmentation technique is also performed in DT-CWT domain. Finally we also developed an OCT/fundus image registration algorithm which is helpful when two modalities are used together for diagnosis and for information fusion.
Figure below shows B-scans of retinal OCT images at ONH (top) and macula (bottom). Left: raw OCT images show grainy texture. Middle: despeckled images using with Cauchy Model*. Right: enhanced images using AWBF. [CODE] [PDF]
Texture analysis on Ocular imaging for Glaucoma disease regression
The project analysed texture in the OCT image layers on retinal disease glaucoma. An automated texture classification method for glaucoma detection has been developed. Methodology for classification and feature extraction based on robust principle component analysis of texture descriptors was established. Also, the technique using multi-modal information fusion which incorporates data from visual field measurements with OCT and retinal fundus photography was developed. [PDF]
Video databases, used for benchmarking and evaluating the performance of new video technologies, should represent the full breadth of consumer video content. The parameterisation of video databases using low-level features has proven to be an effective way of quantifying the diversity within a database. However, without a comprehensive understanding of the importance and relative frequency and of these features in the content people actually consume, the utility of such information is limited. In collaboration with the BBC, the “What’s on TV” is a large-scale analysis of the low-level features that exist in contemporary broadcast video. The project aims to establish an efficient set of features that can be used to characterise the spatial and temporal variation in modern consumer content. The meaning and relative significance of this feature set, together with the shape of their frequency distributions, represent highly valuable information for researchers wanting to model the diversity of modern consumer content in representative video databases.
The following work presents a graphical user interface (GUI), for automatic segmentation of granule cores and membranes, in transmission electron microscopy images of beta cells. The system is freely available for academic research. Two test images are also included. The highlights of our approach are:
A fully automated algorithm for granule segmentation.
A novel shape regularizer to promote granule segmentation.
A dual region-based active contour for accurate core segmentation.
A novel convergence filter for granule membrane verification.
A precision of 91% and recall of 87% is observed against manual segmentations.
Video content distributors, codec developers and researchers in related fields often rely on subjective assessments to ensure that their video processing procedures result in satisfactory quality. The current 10-second recommendation for the length of test sequences in subjective video quality assessment studies, however, has recently been questioned. Not only do sequences of this length depart from modern cinematic shooting styles, the use of shorter sequences would enable substantial efficiency improvements to the data collection process. This project, therefore, aims to explore the impact upon viewer rating behaviour of using different length video sequences and the consequent savings that could be made in time, labour and money .