Contextual visual localization: cascaded submap classification, optimized saliency detection, and fast view matching

In this paper, we present a novel coarse-to-fine visual localization approach: contextual visual localization. This approach relies on three elements: (i) a minimal-complexity classifier for performing fast coarse localization (submap classification); (ii) an optimized saliency detector which exploits the visual statistics of the submap; and (iii) a fast view-matching algorithm which filters initial matchings with a structural criterion. The latter algorithm yields fine localization. Our experiments show that these elements have been successfully integrated for solving the global localization problem. Context, that is, the awareness of being in a particular submap, is defined by a supervised classifier tuned for a minimal set of features. Visual context is exploited both for tuning (optimizing) the saliency detection process, and to select potential matching views in the visual database, close enough to the query view.

Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets.

Saliency detection using hierarchical manifold learning

Saliency detection is critical to many applications in computer vision by eliminating redundant backgrounds. The saliency detection approaches can be divided into two categories, i.e., top-down and bottom-up. Among them, bottom-up models have attracted more attention due to their simple mechanisms. However, many existing bottom-up models are not robust to crowded backgrounds because of missing salient regions within feedforward frameworks which is often not effective for complex scenes. We tackle these problems by modifying and extending a bottom-up saliency detection model through three phases, (1) constructing a hierarchical sequence of images from the perspective of entropy, (2) estimated mid-level cues are used as feedback information, (3) subsequently generating saliency maps by global context and local uniqueness in a graph-based framework. We also compare the proposed bottom-up model with state-of-the-art approaches on two benchmark datasets to evaluate its saliency detection performance. The experimental results demonstrate that the proposed bottom-up saliency detection approach is not only robust to both cluttered and clean scenes, but also able to obtain objects with different scales.

Channel blurring and consumer expectations : evidence in Chile

An important trend in Chilean retailing industry is the increase in channel blurring. This investigation attempts to identify the relevant store attributes for different retail formats (grocery, department store, drug store, and home improvement). Do consumer store attribute saliency vary for different retail formats? Interviews identified twelve salient store attributes for the different retail formats. Survey results showed differences in store attribute saliencies for consumers when shopping at different formats. Seven of the twelve variables showed significant differences across formats. However, two attributes were relatively important for all four retail formats: product quality and responsiveness of employees.

Queensland Aborigines, multiple realities and the social sources of suffering. Part 2: Suicide, spirits and symbolism

This is the second part of a paper that explores a range of magico-religious experiences such as immaterial voices and visions, in terms of local cultural, moral and socio-political circumstances in an Aboriginal town in rural Queensland. This part of the paper explores the political and cultural symbolism and meaning of suicide. It charts the saliency of suicide amongst two groups of kin and cohorts and the social meaningfulness and problematic of the voices and visions in relation to suicide, to identity and family forms and to funerals and a heavily drinking lifestyle. I argue that voices and visions are used to reinterpret social experience and to establish meaning and that tragically suicide evokes connectivity rather than anomie and here cannot be understood merely as an individualistic act or evidence of individual pathology. Rather it is about transformation and crossing a threshold to join an enduring domain of Aboriginality. In this life world, where family is the highest social value and where a relational view of persons holds sway, the individualistic practice of psychiatric and other helping professions, is a considerable problem.

Hessian-based affine adaptation of salient local image features

Affine covariant local image features are a powerful tool for many applications, including matching and calibrating wide baseline images. Local feature extractors that use a saliency map to locate features require adaptation processes in order to extract affine covariant features. The most effective extractors make use of the second moment matrix (SMM) to iteratively estimate the affine shape of local image regions. This paper shows that the Hessian matrix can be used to estimate local affine shape in a similar fashion to the SMM. The Hessian matrix requires significantly less computation effort than the SMM, allowing more efficient affine adaptation. Experimental results indicate that using the Hessian matrix in conjunction with a feature extractor that selects features in regions with high second order gradients delivers equivalent quality correspondences in less than 17% of the processing time, compared to the same extractor using the SMM.

Automatic region-of-interest detection and prioritisation for visually optimised coding of low bit rate videos

The increasing popularity of video consumption from mobile devices requires an effective video coding strategy. To overcome diverse communication networks, video services often need to maintain sustainable quality when the available bandwidth is limited. One of the strategy for a visually-optimised video adaptation is by implementing a region-of-interest (ROI) based scalability, whereby important regions can be encoded at a higher quality while maintaining sufficient quality for the rest of the frame. The result is an improved perceived quality at the same bit rate as normal encoding, which is particularly obvious at the range of lower bit rate. However, because of the difficulties of predicting region-of-interest (ROI) accurately, there is a limited research and development of ROI-based video coding for general videos. In this paper, the phase spectrum quaternion of Fourier Transform (PQFT) method is adopted to determine the ROI. To improve the results of ROI detection, the saliency map from the PQFT is augmented with maps created from high level knowledge of factors that are known to attract human attention. Hence, maps that locate faces and emphasise the centre of the screen are used in combination with the saliency map to determine the ROI. The contribution of this paper lies on the automatic ROI detection technique for coding a low bit rate videos which include the ROI prioritisation technique to give different level of encoding qualities for multiple ROIs, and the evaluation of the proposed automatic ROI detection that is shown to have a close performance to human ROI, based on the eye fixation data.

Image Region Salience for Improving Appearance-Based Place Recognition using a Supervised Classifier System

Many state of the art vision-based Simultaneous Localisation And Mapping (SLAM) and place recognition systems compute the salience of visual features in their environment. As computing salience can be problematic in radically changing environments new low resolution feature-less systems have been introduced, such as SeqSLAM, all of which consider the whole image. In this paper, we implement a supervised classifier system (UCS) to learn the salience of image regions for place recognition by feature-less systems. SeqSLAM only slightly benefits from the results of training, on the challenging real world Eynsham dataset, as it already appears to filter less useful regions of a panoramic image. However, when recognition is limited to specific image regions performance improves by more than an order of magnitude by utilising the learnt image region saliency. We then investigate whether the region salience generated from the Eynsham dataset generalizes to another car-based dataset using a perspective camera. The results suggest the general applicability of an image region salience mask for optimizing route-based navigation applications.

Tracking-based moving object detection

We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves three steps. Firstly, the spatial-temporal saliency is employed to detect moving objects. Secondly, the detected objects are tracked by mean shift in the subsequent frames. Finally, the saliency results are fused with the weight map generated by tracking to get refined detection results, and in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay.

Vision-based simultaneous localization and mapping in changing outdoor environments

For robots operating in outdoor environments, a number of factors, including weather, time of day, rough terrain, high speeds, and hardware limitations, make performing vision-based simultaneous localization and mapping with current techniques infeasible due to factors such as image blur and/or underexposure, especially on smaller platforms and low-cost hardware. In this paper, we present novel visual place-recognition and odometry techniques that address the challenges posed by low lighting, perceptual change, and low-cost cameras. Our primary contribution is a novel two-step algorithm that combines fast low-resolution whole image matching with a higher-resolution patch-verification step, as well as image saliency methods that simultaneously improve performance and decrease computing time. The algorithms are demonstrated using consumer cameras mounted on a small vehicle in a mixed urban and vegetated environment and a car traversing highway and suburban streets, at different times of day and night and in various weather conditions. The algorithms achieve reliable mapping over the course of a day, both when incrementally incorporating new visual scenes from different times of day into an existing map, and when using a static map comprising visual scenes captured at only one point in time. Using the two-step place-recognition process, we demonstrate for the first time single-image, error-free place recognition at recall rates above 50% across a day-night dataset without prior training or utilization of image sequences. This place-recognition performance enables topologically correct mapping across day-night cycles.

Towards inclusion: Provision for diversity in the transition to school

Policies of inclusion challenge the construct of readiness and require schools to prepare for the diversity of children as they transition to school. However, there is limited empirical evidence concerning how this challenge is met. This paper presents two Australian studies that investigate inclusive practices in the transition to school. Study 1 examined the predictors of child outcomes across a sample of 1831 children in 39 schools. The results indicate that both quantity and quality of programme provision influenced outcomes and that programme effects were particularly potent for children with diverse abilities and backgrounds. Study 2 focuses on pedagogy in three of the schools to highlight how this provision can be achieved. Results show that provisions were reactive, that saliency of children’s needs directed school practices and that professional knowledge impacted on measures of quality. Inclusive processes accounting for both child progress and broader family and teaching influences are necessary for improved transition to school.

Fast Accurate Transient Stability Simulations

The paper describes a Simultaneous Implicit (SI) approach for transient stability simulations based on an iterative technique using traingularised admittance matrix [1]. The reduced saliency of generator in the subtransient state is taken advantage of to speed up the algorithm. Accordingly, generator differential equations, except rotor swing, contain voltage proportional to fluxes in the main field, dampers and a hypothetical winding representing deep flowing eddy currents, as state variables. The simulation results are validated by comparison with two independent methods viz. Runge-Kutta simulation for a simplified system and a method based on modelling damper windings using conventional induction motor theory.