Orientation integration in detection and discrimination of contrast-modulated patterns

Orientation detection and discrimination thresholds were measured for Gabor ‘envelopes’ formed from contrast-modulation of luminance ‘carriers’. Consistent with previous research differences between carrier and envelope orientation had no effect on sensitivity to envelopes. Using plaid carriers in which the proportion of contrast modulation ‘carried’ by each plaid component was systematically manipulated, it was shown that this tolerance to carrier-envelope orientation difference reflects linear summation across orientation indicative of a single second-stage channel coding for contrast-defined structure. That contrast envelopes did not exhibit linear summation across spatial-frequency, nor across combinations of orientation and spatial-frequency differences, suggests that these second-order channels operate only within certain spatial scales. Using arrays of Gabor micropatterns as carriers in which the orientation distribution of the carriers was manipulated independently of the difference between envelope orientation and mean carrier orientation, it was further demonstrated that the locus of orientation integration must occur prior to envelope detection. In the context of two-stage models that incorporate a non-linearity between the stages, the pattern of results obtained is consistent with the operation of an orientation pooling process between first-stage and second-stage channels, analogous to having all filters of the first-stage feed into all filters of the second-stage within the same spatial-frequency band.

Visual mouse : SIFT detection and PCA recognition

The paper presents the Visual Mouse (VM), a novel and simple system for interaction with displays via hand gestures. Our method includes detecting bare hands using the fast SIFT (Scale-Invariant Feature Transform) algorithm saving long training time of the Adaboost algorithm, tracking hands based on the CAMShift algorithm, recognizing hand gestures in cluttered background via Principle Components Analysis (PCA) without extracting clear-cut hand contour, and defining simple and robustly interpretable vocabularies of hand gestures, which are subsequently used to control a computer mouse. The system provides a fast and simple interaction experience without the need for more expensive hardware and software.

A novel defect detection and identification method in optical inspection

Optical inspection techniques have been widely used in industry as they are non-destructive. Since defect patterns are rooted from the manufacturing processes in semiconductor industry, efficient and effective defect detection and pattern recognition algorithms are in great demand to find out closely related causes. Modifying the manufacturing processes can eliminate defects, and thus to improve the yield. Defect patterns such as rings, semicircles, scratches, and clusters are the most common defects in the semiconductor industry. Conventional methods cannot identify two scale-variant or shift-variant or rotation-variant defect patterns, which in fact belong to the same failure causes. To address these problems, a new approach is proposed in this paper to detect these defect patterns in noisy images. First, a novel scheme is developed to simulate datasets of these 4 patterns for classifiers' training and testing. Second, for real optical images, a series of image processing operations have been applied in the detection stage of our method. In the identification stage, defects are resized and then identified by the trained support vector machine. Adaptive resonance theory network 1 is also implemented for comparisons. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.

The prevalence and recognition of major depression among low-level aged care residents with and without cognitive impairment

Previous research has demonstrated a high level of depression in nursing homes. The current study was designed to determine the prevalence of depression, using a structured diagnostic interview, among older people with and without mild-moderate cognitive impairment residing in low-level care facilities. The results demonstrated that, consistent with previous research in nursing homes, 16.9% of older people were diagnosed with major depressive disorder. Less than half of these cases had been detected or treated. Individuals with moderate cognitive impairment were more likely to be depressed, but cognitive impairment did not appear to act as a strong impediment to the detection of depression by general practitioners. A low awareness of their use of antidepressant medications was demonstrated among older people prescribed this treatment, including those with normal cognitive function. Reasons for the poor recognition of depression among older people are discussed.

Detection and classification of road signs in natural environments

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.

Vehicle detection and classification by measuring and processing magnetic signal

This paper presents novel vehicle detection and classification method by measuring and processing magnetic signal based on single micro-electro- mechanical system (MEMS) magnetic sensor. When a vehicle moves over the ground, it generates a succession of impacts on the earth's magnetic field, which can be detected by single magnetic sensor. The magnetic signal measured by the magnetic sensor is related to the moving direction and the type of the vehicle. Generally, the recognition rate using single sensor detector is not high. In order to improve the recognition rate, a novel feature extraction algorithm and a novel vehicle classification and recognition algorithm are presented. The concavity and convexity areas, and the angles of concave and convex parts of the waveform are extracted. An improved support vector machine (ISVM) classifier is developed to perform vehicle classification and recognition. The effectiveness of the proposed approach is verified by outdoor experiments.

Semantic trajectory based event detection and event pattern mining

Video event detection is an effective way to automatically understand the semantic content of the video. However, due to the mismatch between low-level visual features and high-level semantics, the research of video event detection encounters a number of challenges, such as how to extract the suitable information from video, how to represent the event, how to build up reasoning mechanism to infer the event according to video information. In this paper, we propose a novel event detection method. The method detects the video event based on the semantic trajectory, which is a high-level semantic description of the moving object’s trajectory in the video. The proposed method consists of three phases to transform low-level visual features to middle-level raw trajectory information and then to high-level semantic trajectory information. Event reasoning is then carried out with the assistance of semantic trajectory information and background knowledge. Additionally, to release the users’ burden in manual event definition, a method is further proposed to automatically discover the event-related semantic trajectory pattern from the sample semantic trajectories. Furthermore, in order to effectively use the discovered semantic trajectory patterns, the associative classification-based event detection framework is adopted to discover the possibly occurred event. Empirical studies show our methods can effectively and efficiently detect video events.

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

Barriers to the detection and management of depression by palliative care professional carers among their patients : perspectives from professional carers and patients' family members

Introduction: Clinical depression is highly prevalent yet underdetected and poorly managed within palliative care settings.

Objectives: This qualitative study explored the identification, monitoring, and management of symptoms of depression in patients receiving palliative care from 2 juxtaposed perspectives that are of care providers and care recipients' family members. Examining the barriers that restrict professional carers detecting and managing depression in their patients was a central focus of the study.

Methods: Focus groups were held with 18 professional carers, including 8 holding managerial positions, across 2 palliative care services, 1 regional and 1 metropolitan, which provided both inpatient and community-based care. Individual interviews were conducted with 10 family members of patients who had received or were receiving palliative care through these services.

Results: Thematic analysis of these data identified that both professional carers and family members perceived that depression is a wide-spread concern for patients receiving palliative care; however, numerous barriers were identified that affect professional carers’ ability to identify depression. These included knowledge and training deficits, low self-efficacy, prioritization of physical concerns and time constraints, patient/family characteristics, and system/process issues. These themes (and related subthemes) are discussed in this article.

Conclusions: Specialized training in depression is recommended for professional carers in order to improve their depression-related knowledge, detection skills, and self-efficacy. The ultimate goal of such training is to increase the rate of recognition of depression that in turn will lead to appropriate treatment for depressed patients.

Doppler radar in respiratory monitoring : detection and analysis

This paper presents the preliminary results of our work in detecting respiration using Doppler Radar in the 2.7 GHz operating band. We demonstrate the capability of Doppler Radar in capturing breathing patterns under various breathing forms such as normal breathing, fast breathing, as well as different rate of inhale and exhale. From the captured signals, respiration rate was obtained using Fast Fourier Transform and validated. The proposed approach could potentially be used in number of applications involving breathing rate and breathing pattern analysis via non-contact methods.

Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using Dynamic Time Warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique. © 2008 IEEE.

A brief review on offshore wind turbine fault detection and recent development in condition monitoring based maintenance system

Offshore wind turbine requires more systematized operation and maintenance strategies to ensure systems are harmless, profitable and cost-effective. Condition monitoring and fault diagnostic systems ominously plays an important role in offshore wind turbine in order to cut down maintenance and operational costs. Condition monitoring techniques which describing complex faults and failure mode types and their generated traceable signs to provide cost-effective condition monitoring and predictive maintenance and their diagnostic schemes. Continuously monitor the condition of critical parts are the most efficient way to improve reliability of wind turbine. Implementation of Condition Based Maintenance (CBM) strategy provides right time maintenance decisions and Predictive Health Monitoring (PHM) data to overcome breakdown and machine downtime. Fault detection and CBM implementation is challenging for off shore wind farm due to the complexity of remote sensing, components health and predictive assessment, data collection, data analysis, data handling, state recognition, and advisory decision. The rapid expansion of wind farms, advanced technological development and harsh installation sites needs a successful CM approach. This paper aims to review brief status of recent development of CM techniques and focusing with major faults takes place in gear box and bearing, rotor and blade, pitch, yaw and tower system and generator and control system.

Research on seismic signals for vehicle targets and recognition by data fusion

This paper researches seismic signals of typical vehicle targets in order to extract features and to recognize vehicle targets. As a data fusion method, the technique of artificial neural networks combined with genetic algorithm(ANNCGA) is applied for recognition of seismic signals that belong to different kinds of vehicle targets. The technique of ANNCGA and its architecture have been presented. The algorithm had been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that seismic properties of target acquired are correct, ANNCGA data fusion method is effective to solve the problem of target recognition.

The power of play-break for automatic detection and browsing of self-consumable sport video highlights

To enable content-based retrieval, highlights extraction from broadcasted sport video has been an active research topic in the last decade. There is a well-known theory that high-level semantic, such as goal in soccer can be detected based on the occurrences of specific audio and visual features that can be extracted automatically. However, there is yet a definitive solution for the scope (i.e. start and end) of the detection for self consumable highlights. Thus, in this paper we will primarily demonstrate the benefits of using play-break for this purpose. Moreover, we also propose a browsing scheme that is based on integrated play-break and highlights (extended from [1]). To validate our approach, we will present the results from some experiments and a user study.