Automated annotation of landmark images using community contributed datasets and web resources

A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching oers 93.6% classication accuracy; the selection of appropriate tags for use in annotation performs well (F1M of 0.59), and it subsequently automatically identies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases

Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost

Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With regards to map storage, the mammalian brain appears to take a diametrically opposed approach to all current robotic mapping systems. Where robotic mapping systems attempt to solve the data association problem to minimise representational aliasing, neurons in the brain intentionally break data association by encoding large (potentially unlimited) numbers of places with a single neuron. In this paper, we propose a novel method based on supervised learning techniques that seeks out regularly repeating visual patterns in the environment with mutually complementary co-prime frequencies, and an encoding scheme that enables storage requirements to grow sub-linearly with the size of the environment being mapped. To improve robustness in challenging real-world environments while maintaining storage growth sub-linearity, we incorporate both multi-exemplar learning and data augmentation techniques. Using large benchmark robotic mapping datasets, we demonstrate the combined system achieving high-performance place recognition with sub-linear storage requirements, and characterize the performance-storage growth trade-off curve. The work serves as the first robotic mapping system with sub-linear storage scaling properties, as well as the first large-scale demonstration in real-world environments of one of the proposed memory benefits of these neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and Automation Letter

Relative Facial Action Unit Detection

This paper presents a subject-independent facial action unit (AU) detection method by introducing the concept of relative AU detection, for scenarios where the neutral face is not provided. We propose a new classification objective function which analyzes the temporal neighborhood of the current frame to decide if the expression recently increased, decreased or showed no change. This approach is a significant change from the conventional absolute method which decides about AU classification using the current frame, without an explicit comparison with its neighboring frames. Our proposed method improves robustness to individual differences such as face scale and shape, age-related wrinkles, and transitions among expressions (e.g., lower intensity of expressions). Our experiments on three publicly available datasets (Extended Cohn-Kanade (CK+), Bosphorus, and DISFA databases) show significant improvement of our approach over conventional absolute techniques. Keywords: facial action coding system (FACS); relative facial action unit detection; temporal information;Comment: Accepted at IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs Colorado, USA, 201

Vision-based retargeting for endoscopic navigation

Endoscopy is a standard procedure for visualising the human gastrointestinal tract. With the advances in biophotonics, imaging techniques such as narrow band imaging, confocal laser endomicroscopy, and optical coherence tomography can be combined with normal endoscopy for assisting the early diagnosis of diseases, such as cancer. In the past decade, optical biopsy has emerged to be an effective tool for tissue analysis, allowing in vivo and in situ assessment of pathological sites with real-time feature-enhanced microscopic images. However, the non-invasive nature of optical biopsy leads to an intra-examination retargeting problem, which is associated with the difficulty of re-localising a biopsied site consistently throughout the whole examination. In addition to intra-examination retargeting, retargeting of a pathological site is even more challenging across examinations, due to tissue deformation and changing tissue morphologies and appearances. The purpose of this thesis is to address both the intra- and inter-examination retargeting problems associated with optical biopsy. We propose a novel vision-based framework for intra-examination retargeting. The proposed framework is based on combining visual tracking and detection with online learning of the appearance of the biopsied site. Furthermore, a novel cascaded detection approach based on random forests and structured support vector machines is developed to achieve efficient retargeting. To cater for reliable inter-examination retargeting, the solution provided in this thesis is achieved by solving an image retrieval problem, for which an online scene association approach is proposed to summarise an endoscopic video collected in the first examination into distinctive scenes. A hashing-based approach is then used to learn the intrinsic representations of these scenes, such that retargeting can be achieved in subsequent examinations by retrieving the relevant images using the learnt representations. For performance evaluation of the proposed frameworks, extensive phantom, ex vivo and in vivo experiments have been conducted, with results demonstrating the robustness and potential clinical values of the methods proposed.Open Acces

A stacked LSTM based approach for reducing semantic pose estimation error

© 1963-2012 IEEE. Achieving high estimation accuracy is significant for semantic simultaneous localization and mapping (SLAM) tasks. Yet, the estimation process is vulnerable to several sources of error, including limitations of the instruments used to perceive the environment, shortcomings of the employed algorithm, environmental conditions, or other unpredictable noise. In this article, a novel stacked long short-term memory (LSTM)-based error reduction approach is developed to enhance the accuracy of semantic SLAM in presence of such error sources. Training and testing data sets were constructed through simulated and real-time experiments. The effectiveness of the proposed approach was demonstrated by its ability to capture and reduce semantic SLAM estimation errors in training and testing data sets. Quantitative performance measurement was carried out using the absolute trajectory error (ATE) metric. The proposed approach was compared with vanilla and bidirectional LSTM networks, shallow and deep neural networks, and support vector machines. The proposed approach outperforms all other structures and was able to significantly improve the accuracy of semantic SLAM. To further verify the applicability of the proposed approach, it was tested on real-time sequences from the TUM RGB-D data set, where it was able to improve the estimated trajectories