Unusual scene detection using distributed behaviour model and sparse representation

The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.

Fusion of morphological images for airborne target detection

Several track-before-detection approaches for image based aircraft detection have recently been examined in an important automated aircraft collision detection application. A particularly popular approach is a two stage processing paradigm which involves: a morphological spatial filter stage (which aims to emphasize the visual characteristics of targets) followed by a temporal or track filter stage (which aims to emphasize the temporal characteristics of targets). In this paper, we proposed new spot detection techniques for this two stage processing paradigm that fuse together raw and morphological images or fuse together various different morphological images (we call these approaches morphological reinforcement). On the basis of flight test data, the proposed morphological reinforcement operations are shown to offer superior signal to-noise characteristics when compared to standard spatial filter options (such as the close-minus-open and adaptive contour morphological operations). However, system operation characterised curves, which examine detection verses false alarm characteristics after both processing stages, illustrate that system performance is very data dependent.

HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection

The quick detection of abrupt (unknown) parameter changes in an observed hidden Markov model (HMM) is important in several applications. Motivated by the recent application of relative entropy concepts in the robust sequential change detection problem (and the related model selection problem), this paper proposes a sequential unknown change detection algorithm based on a relative entropy based HMM parameter estimator. Our proposed approach is able to overcome the lack of knowledge of post-change parameters, and is illustrated to have similar performance to the popular cumulative sum (CUSUM) algorithm (which requires knowledge of the post-change parameter values) when examined, on both simulated and real data, in a vision-based aircraft manoeuvre detection problem.

Single-subject analysis reveals varied knee mechanics during step landing in response to isokinetic fatigue of the quadriceps

Introduction: The ability to regulate joint stiffness and coordinate movement during landing when impaired by muscle fatigue has important implications for knee function. Unfortunately, the literature examining fatigue effects on landing mechanics suffers from a lack of consensus. Inconsistent results can be attributed to variable fatigue models, as well as grouping variable responses between individuals when statistically detecting differences between conditions. There remains a need to examine fatigue effects on knee function during landing with attention to these methodological limitations. Aim: The purpose of this study therefore, was to examine the effects of isokinetic fatigue on pre-impact muscle activity and post-impact knee mechanics during landing using singlesubject analysis. Methodology: Sixteen male university students (22.6+3.2 yrs; 1.78+0.07 m; 75.7+6.3 kg) performed maximal concentric and eccentric knee extensions in a reciprocal manner on an isokinetic dynamometer and step-landing trials on 2 occasions. On the first occasion each participant performed 20 step-landing trials from a knee-high platform followed by 75 maximal contractions on the isokinetic dynamometer. The isokinetic data was used to calculate the operational definition of fatigue. On the second occasion, with a minimum rest of 14 days, participants performed 2 sets of 20 step landing trials, followed by isokinetic exercise until the operational definition of fatigue was met and a final post-fatigue set of 20 step-landing trials. Results: Single-subject analyses revealed that isokinetic fatigue of the quadriceps induced variable responses in pre impact activation of knee extensors and flexors (frequency, onset timing and amplitude) and post-impact knee mechanics(stiffness and coordination). In general however, isokinetic fatigue induced sig nificant (p<0.05) reductions in quadriceps activation frequency, delayed onset and increased amplitude. In addition, knee stiffness was significantly (p<0.05) increased in some individuals, as well as impaired sagittal coordination. Conclusions: Pre impact activation and post-impact mechanics were adjusted in patterns that were unique to the individual, which could not be identified using traditional group-based statistical analysis. The results suggested that individuals optimised knee function differently to satisfy competing demands, such as minimising energy expenditure, as well as maximising joint stability and sensory information.

Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Global flash multifocal electroretinogram : early detection of local functional changes and its correlations with optical coherence tomography and visual field loss in diabetic eyes

Purpose: To investigate the correlations of the global flash multifocal electroretinogram (MOFO mfERG) with common clinical visual assessments – Humphrey perimetry and Stratus circumpapillary retinal nerve fiber layer (RNFL) thickness measurement in type II diabetic patients. Methods: Forty-two diabetic patients participated in the study: ten were free from diabetic retinopathy (DR) while the remainder suffered from mild to moderate non-proliferative diabetic retinopathy (NPDR). Fourteen age-matched controls were recruited for comparison. MOFO mfERG measurements were made under high and low contrast conditions. Humphrey central 30-2 perimetry and Stratus OCT circumpapillary RNFL thickness measurements were also performed. Correlations between local values of implicit time and amplitude of the mfERG components (direct component (DC) and induced component (IC)), and perimetric sensitivity and RNFL thickness were evaluated by mapping the localized responses for the three subject groups. Results: MOFO mfERG was superior to perimetry and RNFL assessments in showing differences between the diabetic groups (with and without DR) and the controls. All the MOFO mfERG amplitudes (except IC amplitude at high contrast) correlated better with perimetry findings (Pearson’s r ranged from 0.23 to 0.36, p<0.01) than did the mfERG implicit time at both high and low contrasts across all subject groups. No consistent correlation was found between the mfERG and RNFL assessments for any group or contrast conditions. The responses of the local MOFO mfERG correlated with local perimetric sensitivity but not with RNFL thickness. Conclusion: Early functional changes in the diabetic retina seem to occur before morphological changes in the RNFL.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

Fast power line detection and localization using steerable filter for active UAV guidance

In this paper we present a fast power line detection and localisation algorithm as well as propose a high-level guidance architecture for active vision-based Unmanned Aerial Vehicle (UAV) guidance. The detection stage is based on steerable filters for edge ridge detection, followed by a line fitting algorithm to refine candidate power lines in images. The guidance architecture assumes an UAV with an onboard Gimbal camera. We first control the position of the Gimbal such that the power line is in the field of view of the camera. Then its pose is used to generate the appropriate control commands such that the aircraft moves and flies above the lines. We present initial experimental results for the detection stage which shows that the proposed algorithm outperforms two state-of-the-art line detection algorithms for power line detection from aerial imagery.

Detection of tissue plasminogen activator (t-PA) and plasminogen activator inhibitor 2(PAI-2) in gingival crevicular fluid from healthy, gingivitis and periodontitis patients

Background: The regulation of plasminogen activation is a key element in controlling proteolytic events in the extracellular matrix. Our previous studies had demonstrated that in inflamed gingival tissues, tissue-type plasminogen activator (t-PA) is significantly increased in the extracellular matrix of the connective tissue and that interleukin 1β (IL-1β) can up regulate the level of t-PA and plasminogen activator inhibitor-2 (PAI-2) synthesis by human gingival fibroblasts. Method: In the present study, the levels of t-PA and PAI-2 in gingival crevicular fluid (GCF) were measured from healthy, gingivitis and periodontitis sites and compared before and after periodontal treatment. Crevicular fluid from106 periodontal sites in 33 patients were collected. 24 sites from 11 periodontitis patients received periodontal treatment after the first sample collection and post-treatment samples were collected 14 days after treatment. All samples were analyzed by enzyme-linked immunosorbent assay (ELISA) for t-PA and PAI-2. Results: The results showed that significantly high levels of t-PA and PAI-2 in GCF were found in the gingivitis and periodontitis sites. Periodontal treatment led to significant decreases of PAI-2, but not t-PA, after 14 days. A significant positive linear correlation was found between t-PA and PAI-2 in GCF (r=0.80, p<0.01). In the healthy group, different sites from within the same subject showed little variation of t-PA and PAI-2 in GCF. However, the gingivitis and periodontitis sites showed large variation. These results suggest a good correlation between t-PA and PAI-2 with the severity of periodontal conditions. Conclusion: This study indicates that t-PA and PAI-2 may play a significant rôle in the periodontal tissue destruction and tissue remodeling and that t-PA and PAI-2 in GCF may be used as clinical markers to evaluate the periodontal diseases and assess treatment.

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.