Pre-2015 projects – Page 3 – Visual Information Laboratory

Plane Detection From Single Images

Posted on 1st September 201323rd February 2021 by Katie Black

Our work involves the detection of planar structures from single images. This is inspired by human vision – since humans have an impressive ability to understand the content of both the real world and 2D images, without necessarily needing depth or parallax cues. As such, we take a machine learning route, and learn from a large set of images the relationship between image appearance and 3D structure.

There are two main parts to our method: first, plane recognition, which for a given, pre-segmented image region can classify it as being planar or not, and for planar regions estimate their 3D orientation with respect to the camera. This is done by representing the image region with standard image descriptors, within a bag of words framework enhanced with spatial information. These are used as input to a relevance vector machine classifier, to identify planes, and a regression algorithm to estimate orientation.

Second, the above is used for plane detection, where since we do not generally know the location of potentially planar regions in the image, we apply the plane recognition step repeatedly to overlapping segments of the image. These overlapping regions give allow us to calculate an estimate, at each of a set of salient points, whether they are likely to belong to a plane or not, and their likely orientation (by considering all the regions in which they lie). This point-wise local plane estimate is then segmented to give a discrete set of non-planar and oriented planar regions.

We have also shown (work in collaboration with José Martínez-Carranza) how this single-image plane detection can be useful for visual odometry, where by detecting the presence of likely planar structures from on frame while traversing an outdoor urban environment, planar features can be quickly initialised into the map, with a good prior estimate of their orientation. This allows rough 3D maps of the environment, incorporating higher-level structures, to be rapidly built.

Experimental Results

Plane Recognition

We found that the plane recognition algorithm was able to work well in a variety of outdoor scenes. As well as comprehensive cross-validation, we tested the algorithm on a set of images taken from a completely independent area of the city from the location of the test images (where the region of interest has been marked up by hand). Average classification (plane/non-plane) accuracy was 91.6%, and an orientation (normal vector estimation) error of 14.5 degrees. Some example results from this data set are shown here:

plane1-1000x205

The first three show successful plane detection with estimated orientations (green) compared to ground truth (blue); the last two show identification of non-planar regions.

Plane Detection

The full plane detection algorithm, involving finding planes in previously unseen images, and estimating their orientation, was also tested on an independent data set of images. A few example results are shown here:

plane2-1000x205

References

Visual mapping using learned structural priors (ICRA 2013)
Detecting planes and estimating their orientation from a single image (BMVC 2012)
Estimating planar structure in single images by learning from examples (ICPRAM 2012)

Penguin Identification

Posted on 18th February 201230th March 2021 by hc0639

Tilo Burghardt, Neill Campbell, Peter Barham, Richard Sherley

This early research was conducted between 2006-2009. The research aimed at exploring some first non-invasive identification solutions for problems in field biology and to better understand and help conserve endangered species. Specifically, we developed approaches to monitor individuals in uniquely patterned animal populations using techniques that originated in computer vision and human biometrics. Work was centred around the African penguin (Spheniscus demersus).

During the project we provided a proof of concept for an autonomously operating prototype system capable of monitoring and recognising a group of individual African penguins in their natural environment without tagging or otherwise disturbing the animals. The prototype system was limited to very good acquisitional and environmental conditions, and operated on animals with sufficiently complex natural patterns.

Research was conducted together with the Animal Demography Unit at the University of Cape Town, South Africa. The project was funded by the Leverhulme Trust, with long-term support in the field from the Earthwatch Institute, and with pilot tests run in collaboration with Bristol Zoo Gardens.

Whilst deep learning approaches of today have replaced most of the traditional identification techniques of the 2000s, the practical and applicational insights gained in this project helped inform some of our current work on animal biometrics.

Human pose estimation using motion

Posted on 1st January 201323rd February 2021 by hc0639

Ben Daubney, David Gibson, Neill Campbell

Currently we are researching how to extract human pose from a sparse set of moving features. This work is inspired from psychophisical experiments using the Moving Light Display (MLD), where it has been shown that a small set of moving points attached to the key joints of a person could convey a wealth of information to an observer about the person being viewed, such as their mood or gender. Unlike the typical MLD’s used in the physchophysics community ours are automatically generated by applying a standard feature tracker to a sequence of images.

The result is a set of features that are far more noisy and unreliable than those tradtionally used. The purpose of this research is to try to better understand how the temporal dimension of a sequence of images can be exploited at a much lower level than currently used to estimate pose.

Analysis of moth camouflage

Posted on 18th February 201223rd February 2021 by hc0639

David Gibson, Neill Campbell

A half million pound BBSRC collaboration with Biological sciences and experimental Psychology, the aim of this project is to develop a computational theory of animal camouflage, with models specific to the visual systems of birds and humans. Moths have been chosen for this study as they are a particularly good demonstrators of a wide range of cryptic and disruptive camouflage in nature. Using psychophysically plausible low-level image features, learning algorithms are used to determine the effectiveness of camouflage examples. The ability to generate and process large numbers of camouflage examples enables predictive computational models to be created and compared to the performance of human and bird subjects. Such comparisons will give insights into what aspects of moth camouflage are important for avoiding detection and recognition by birds and humans and thereby, give insight into the mechanisms being employed by bird and human visual systems

Active contours

Posted on 18th September 201223rd February 2021 by hc0639

Majid Mirmehdi, Xianghua Xie, Ronghua Yang

Active contours finding boundaries in the brain Active contour models, commonly known as snakes, have been widely used for object localisation, shape recovery, and visual tracking due to their natural handling of shape variations. The introduction of the Level Set method into snakes has greatly enhanced their potential in real world applications.

Since 2002, we have developed some novel active contour models. The first one aims to bridge (image gradient) boundary based approach and region-based approach. In this work, a level set based geometric snake, enhanced for more tolerance towards weak edges and noise, is introduced. It is based on the principle of the conjunction of the traditional gradient flow forces with new region constraints. We refer to this as the Region-aided Geometric Snake or RAGS. The image gradient provides local information of object boundaries, while the region information offers global definition of boundaries. In this framework, the region constrains can be conveniently customerised and plugged into the snake model.

The second model, called Charged Contour Model (CCM),is a migration of Charged Particle Model (CPM) into the active contour framework. The basic idea is to introduce particle dynamics into contour based deformable models. CCM performs better than CPM in the sense that it guarantees closed contours, i.e. it eliminates the ambiguities in contour reconstruction. Also, CCM is much more efficient. In comparison to geodesic snake, CCM is more robust to weak edges and less sensitive to noise interference.

The third model, CACE (Charged Active Contour model based on Electrostatics), is a further development of the CCM. The snake, implicitly embedded in level sets, propagates under the joint influence of a boundary attraction force and a boundary competition force. Its vector field dynamically adapts by updating itself when a contour reaches a boundary (which differs from CCM). The model is then more invariant to initialisation and possesses better convergence abilities. Analytical and comparative results are presented on synthetic and real images.

MAC model is a result of our most recent effort in developing new active contour models. The proposed external force field that is based on magnetostatics and hypothesized magnetic interactions between the active contour and object boundaries. The major contribution of the method is that the interaction of its forces can greatly improve the active contour in capturing complex geometries and dealing with difficult initializations, weak edges and broken boundaries. The proposed method is shown to achieve significant improvements when compared against six well-known and state-of-the-art shape recovery methods, including the geodesic snake, the generalized version of GVF snake, the combined geodesic and GVF snake, and the charged particle model.

For more information, please see our active contours site.

On-line learning of shape information

Posted on 18th April 201223rd February 2021 by hc0639

John Chiverton, Majid Mirmehdi, Xianghua Xie

Tracking of objects and simultaneously identifying an accurate outline of the tracked object is a complicated computer vision problem to solve because of the changing nature of the high-dimensional image information. Prior information is often included into models, such as probability distribution functions on a prior definition of shape to alleviate potential problems due to e.g. ambiguity as to what should actually be tracked in the image data. However supervised learning and or training is not always possible for new unseen objects or unforeseen configurations of shape, e.g. for silhouettes of 3-D objects. We are therefore interested and are currently investigating ways to include high-level shape information into active contour based tracking frameworks without a supervised pre-processing stage.