Representing team behaviours from noisy data using player role

Due to their unobtrusive nature, vision-based approaches to tracking sports players have been preferred over wearable sensors as they do not require the players to be instrumented for each match. Unfortunately however, due to the heavy occlusion between players, variation in resolution and pose, in addition to fluctuating illumination conditions, tracking players continuously is still an unsolved vision problem. For tasks like clustering and retrieval, having noisy data (i.e. missing and false player detections) is problematic as it generates discontinuities in the input data stream. One method of circumventing this issue is to use an occupancy map, where the field is discretised into a series of zones and a count of player detections in each zone is obtained. A series of frames can then be concatenated to represent a set-play or example of team behaviour. A problem with this approach though is that the compressibility is low (i.e. the variability in the feature space is incredibly high). In this paper, we propose the use of a bilinear spatiotemporal basis model using a role representation to clean-up the noisy detections which operates in a low-dimensional space. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labeled data.

Corner detection in images under different noise levels

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or keypoint detectors.

Scale adaptive tracking using mean shift and efficient feature matching

The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency.

Improving robot vision models for object detection through interaction

We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.

Safety envelope for security

We present an approach for detecting sensor spoofing attacks on a cyber-physical system. Our approach consists of two steps. In the first step, we construct a safety envelope of the system. Under nominal conditions (that is, when there are no attacks), the system always stays inside its safety envelope. In the second step, we build an attack detector: a monitor that executes synchronously with the system and raises an alarm whenever the system state falls outside the safety envelope. We synthesize safety envelopes using a modified machine learning procedure applied on data collected from the system when it is not under attack. We present experimental results that show effectiveness of our approach, and also validate the several novel features that we introduced in our learning procedure.

Is the sun too bright in Queensland? An approach to robust outdoor colour beacon detection

Using cameras onboard a robot for detecting a coloured stationary target outdoors is a difficult task. Apart from the complexity of separating the target from the background scenery over different ranges, there are also the inconsistencies with direct and reflected illumination from the sun,clouds, moving and stationary objects. They can vary both the illumination on the target and its colour as perceived by the camera. In this paper, we analyse the effect of environment conditions, range to target, camera settings and image processing on the reported colours of various targets. The analysis indicates the colour space and camera configuration that provide the most consistent colour values over varying environment conditions and ranges. This information is used to develop a detection system that provides range and bearing to detected targets. The system is evaluated over various lighting conditions from bright sunlight, shadows and overcast days and demonstrates robust performance. The accuracy of the system is compared against a laser beacon detector with preliminary results indicating it to be a valuable asset for long-range coloured target detection.

Parallel attentive visual tracking

The research reported here addresses the problem of detecting and tracking independently moving objects from a moving observer in real-time, using corners as object tokens. Corners are detected using the Harris corner detector, and local image-plane constraints are employed to solve the correspondence problem. The approach relaxes the restrictive static-world assumption conventionally made, and is therefore capable of tracking independently moving and deformable objects. Tracking is performed without the use of any 3-dimensional motion model. The technique is novel in that, unlike traditional feature-tracking algorithms where feature detection and tracking is carried out over the entire image-plane, here it is restricted to those areas most likely to contain-meaningful image structure. Two distinct types of instantiation regions are identified, these being the “focus-of-expansion” region and “border” regions of the image-plane. The size and location of these regions are defined from a combination of odometry information and a limited knowledge of the operating scenario. The algorithms developed have been tested on real image sequences taken from typical driving scenarios. Implementation of the algorithm using T800 Transputers has shown that near-linear speedups are achievable, and that real-time operation is possible (half-video rate has been achieved using 30 processing elements).

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

MATLAB/Simulink simulation and C2000 code generation model for a grid-interactive 3-phase 4-wire inverter

This work is a MATLAB/Simulink model of a controller for a three-phase, four-wire, grid-interactive inverter. The model provides capacity for simulating the performance of power electroinic hardware, as well as code generation for an embedded controller. The implemented hardware topology is a three-leg bridge with a neutral connection to the centre-tap of the DC bus. An LQR-based current controller and MAF-based phase detector are implemented. The model is configured for code generation for a Texas Instruments TMS320F28335 Digital Signal Processor (DSP).

Robotic detection and tracking of Crown-Of-Thorns starfish

This paper presents a novel vision-based underwater robotic system for the identification and control of Crown-Of-Thorns starfish (COTS) in coral reef environments. COTS have been identified as one of the most significant threats to Australia's Great Barrier Reef. These starfish literally eat coral, impacting large areas of reef and the marine ecosystem that depends on it. Evidence has suggested that land-based nutrient runoff has accelerated recent outbreaks of COTS requiring extensive use of divers to manually inject biological agents into the starfish in an attempt to control population numbers. Facilitating this control program using robotics is the goal of our research. In this paper we introduce a vision-based COTS detection and tracking system based on a Random Forest Classifier (RFC) trained on images from underwater footage. To track COTS with a moving camera, we embed the RFC in a particle filter detector and tracker where the predicted class probability of the RFC is used as an observation probability to weight the particles, and we use a sparse optical flow estimation for the prediction step of the filter. The system is experimentally evaluated in a realistic laboratory setup using a robotic arm that moves a camera at different speeds and heights over a range of real-size images of COTS in a reef environment.

Does failed chronic wet cough response to antibiotics predict bronchiectasis?

Aim: To determine whether a child with chronic wet cough and poor response to at least 4 weeks of oral antibiotics is more likely to have bronchiectasis. Methods: All chest multi-detector computerised tomography (MDCT) scans at a single paediatric tertiary hospital from April 2010 to August 2012 were reviewed retrospectively so as to identify those ordered by respiratory physicians for assessment of children with a chronic wet cough. Information regarding age, sex, ethnicity, indication for imaging and the response to at least 4 weeks of antibiotics before having the scan were recorded from their charts. The data were analysed using simple and multiple logistic regression. Results: Of the 144 (87 males) eligible children, 106 (65 males, 30 Indigenous) aged 10–199 months had MDCT scan evidence of bronchiectasis. Antibiotic data were available for 129 children. Among the 105 children with persistent cough despite at least 4 weeks of antibiotics, 88 (83.8%) had bronchiectasis, while of the 24 children whose cough resolved after antibiotics, only six (25.0%) received this diagnosis (adjusted OR 20.9; 95% CI 5.36 to 81.8). Being Indigenous was also independently associated with radiographic evidence of bronchiectasis (adjusted OR 5.86; 95% CI 1.20 to 28.5). Conclusions: Further investigations including a MDCT scan should be considered in a child with a chronic wet cough that persists following 4 weeks of oral antibiotics. However, while reducing the likelihood of underlying bronchiectasis, responding well to a single prolonged course of antibiotics does not exclude this diagnosis completely.

Combining HPLC-DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex moutan

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations

Angular dependence of the response of the nanoDot OSLD system for measurements at depth in clinical megavoltage beams

Purpose The purpose of this investigation was to assess the angular dependence of a commercial optically stimulated luminescence dosimeter (OSLD) dosimetry system in MV x-ray beams at depths beyondd max and to find ways to mitigate this dependence for measurements in phantoms. Methods Two special holders were designed which allow a dosimeter to be rotated around the center of its sensitive volume. The dosimeter's sensitive volume is a disk, 5 mm in diameter and 0.2 mm thick. The first holder rotates the disk in the traditional way. It positions the disk perpendicular to the beam (gantry pointing to the floor) in the initial position (0°). When the holder is rotated the angle of the disk towards the beam increases until the disk is parallel with the beam (“edge on,” 90°). This is referred to as Setup 1. The second holder offers a new, alternative measurement position. It positions the disk parallel to the beam for all angles while rotating around its center (Setup 2). Measurements with five to ten dosimeters per point were carried out for 6 MV at 3 and 10 cm depth. Monte Carlo simulations using GEANT4 were performed to simulate the response of the active detector material for several angles. Detector and housing were simulated in detail based on microCT data and communications with the manufacturer. Various material compositions and an all-water geometry were considered. Results For the traditional Setup 1 the response of the OSLD dropped on average by 1.4% ± 0.7% (measurement) and 2.1% ± 0.3% (Monte Carlo simulation) for the 90° orientation compared to 0°. Monte Carlo simulations also showed a strong dependence of the effect on the composition of the sensitive layer. Assuming the layer to completely consist of the active material (Al2O3) results in a 7% drop in response for 90° compared to 0°. Assuming the layer to be completely water, results in a flat response within the simulation uncertainty of about 1%. For the new Setup 2, measurements and Monte Carlo simulations found the angular dependence of the dosimeter to be below 1% and within the measurement uncertainty. Conclusions The dosimeter system exhibits a small angular dependence of approximately 2% which needs to be considered for measurements involving other than normal incident beams angles. This applies in particular to clinicalin vivo measurements where the orientation of the dosimeter is dictated by clinical circumstances and cannot be optimized as otherwise suggested here. When measuring in a phantom, the proposed new setup should be considered. It changes the orientation of the dosimeter so that a coplanar beam arrangement always hits the disk shaped detector material from the thin side and thereby reduces the angular dependence of the response to within the measurement uncertainty of about 1%. This improvement makes the dosimeter more attractive for clinical measurements with multiple coplanar beams in phantoms, as the overall measurement uncertainty is reduced. Similarly, phantom based postal audits can transition from the traditional TLD to the more accurate and convenient OSLD.

In situ reaction furnace for real-time XRD studies

The new furnace at the Materials Characterization by X-ray Diffraction beamline at Elettra has been designed for powder diffraction measurements at high temperature (up to 1373 K at the present state). Around the measurement region the geometry of the radiative heating element assures a negligible temperature gradient along the capillary and can accommodate either powder samples in capillary or small flat samples. A double capillary holder allows flow-through of gas in the inner sample capillary while the outer one serves as the reaction chamber. The furnace is coupled to a translating curved imaging-plate detector, allowing the collection of diffraction patterns up to 2[theta] [asymptotically equal to] 130°.

Defect-selective photothermal imaging: a sensitive tool for damage mapping in optical coatings

A defect-selective photothermal imaging system for the diagnostics of optical coatings is demonstrated. The instrument has been optimized for pump and probe parameters, detector performance, and signal processing algorithm. The imager is capable of mapping purely optical or thermal defects efficiently in coatings of low damage threshold and low absorbance. Detailed mapping of minor inhomogeneities at low pump power has been achieved through the simultaneous action of a low-noise fiber optic photothermal beam defection sensor and a common-mode-rejection demodulation (CMRD) technique. The linearity and sensitivity of the sensor have been examined theoretically and experimentally, and the signal to noise ratio improvement factor is found to be about 110 compared to a conventional bicell photodiode. The scanner is so designed that mapping of static or shock sensitive samples is possible. In the case of a sample with absolute absorptance of 3.8 x 10(-4), a change in absorptance of about 0.005 x 10(-4) has been detected without ambiguity, ensuring a contrast parameter of 760. This is about 1085% improvement over the conventional approach containing a bicell photodiode, at the same pump power. The merits of the system have been demonstrated by mapping two intentionally created damage sites in a MgF2 coating on fused silica at different excitation powers. Amplitude and phase maps were recorded for thermally thin and thick cases, and the results are compared to demonstrate a case which, in conventional imaging, would lead to a deceptive conclusion regarding the type and location of the damage. Also, a residual damage profile created by long term irradiation with high pump power density has been depicted.