Biologically motivated model for object detection and identification in real-world scenes

The classical computer vision methods can only weakly emulate some of the multi-level parallelisms in signal processing and information sharing that takes place in different parts of the primates’ visual system thus enabling it to accomplish many diverse functions of visual perception. One of the main functions of the primates’ vision is to detect and recognise objects in natural scenes despite all the linear and non-linear variations of the objects and their environment. The superior performance of the primates’ visual system compared to what machine vision systems have been able to achieve to date, motivates scientists and researchers to further explore this area in pursuit of more efficient vision systems inspired by natural models. In this paper building blocks for a hierarchical efficient object recognition model are proposed. Incorporating the attention-based processing would lead to a system that will process the visual data in a non-linear way focusing only on the regions of interest and hence reducing the time to achieve real-time performance. Further, it is suggested to modify the visual cortex model for recognizing objects by adding non-linearities in the ventral path consistent with earlier discoveries as reported by researchers in the neuro-physiology of vision.

A camera based digit location and recognition system for garment tracking

Garment information tracking is required for clean room garment management. In this paper, we present a camera-based robust system with implementation of Optical Character Reconition (OCR) techniques to fulfill garment label recognition. In the system, a camera is used for image capturing; an adaptive thresholding algorithm is employed to generate binary images; Connected Component Labelling (CCL) is then adopted for object detection in the binary image as a part of finding the ROI (Region of Interest); Artificial Neural Networks (ANNs) with the BP (Back Propagation) learning algorithm are used for digit recognition; and finally the system is verified by a system database. The system has been tested. The results show that it is capable of coping with variance of lighting, digit twisting, background complexity, and font orientations. The system performance with association to the digit recognition rate has met the design requirement. It has achieved real-time and error-free garment information tracking during the testing.

Benchmark datasets for detection and tracking

There is a rising demand for the quantitative performance evaluation of automated video surveillance. To advance research in this area, it is essential that comparisons in detection and tracking approaches may be drawn and improvements in existing methods can be measured. There are a number of challenges related to the proper evaluation of motion segmentation, tracking, event recognition, and other components of a video surveillance system that are unique to the video surveillance community. These include the volume of data that must be evaluated, the difficulty in obtaining ground truth data, the definition of appropriate metrics, and achieving meaningful comparison of diverse systems. This chapter provides descriptions of useful benchmark datasets and their availability to the computer vision community. It outlines some ground truth and evaluation techniques, and provides links to useful resources. It concludes by discussing the future direction for benchmark datasets and their associated processes.

IMILAST: a community effort to intercompare extratropical cyclone detection and tracking algorithms

The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen international teams applied their own algorithms to the same dataset—the period 1989–2009 of interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERAInterim) data. This experiment is part of the community project Intercomparison of Mid Latitude Storm Diagnostics (IMILAST; see www.proclim.ch/imilast/index.html). The spread of results for cyclone frequency, intensity, life cycle, and track location is presented to illustrate the impact of using different methods. Globally, methods agree well for geographical distribution in large oceanic regions, interannual variability of cyclone numbers, geographical patterns of strong trends, and distribution shape for many life cycle characteristics. In contrast, the largest disparities exist for the total numbers of cyclones, the detection of weak cyclones, and distribution in some densely populated regions. Consistency between methods is better for strong cyclones than for shallow ones. Two case studies of relatively large, intense cyclones reveal that the identification of the most intense part of the life cycle of these events is robust between methods, but considerable differences exist during the development and the dissolution phases.

Detection and attribution of human influence on regional precipitation

Understanding how human influence on climate is affecting precipitation around the world is immensely important for defining mitigation policies, and for adaptation planning. Yet despite increasing evidence for the influence of climate change on global patterns of precipitation, and expectations that significant changes in regional precipitation should have already occurred as a result of human influence on climate, compelling evidence of anthropogenic fingerprints on regional precipitation is obscured by observational and modelling uncertainties and is likely to remain so using current methods for years to come. This is in spite of substantial ongoing improvements in models, new reanalyses and a satellite record that spans over thirty years. If we are to quantify how human-induced climate change is affecting the regional water cycle, we need to consider novel ways of identifying the effects of natural and anthropogenic influences on precipitation that take full advantage of our physical expectations.

Model-based vehicle detection and classification using orthographic approximations

This paper reports the current state of work to simplify our previous model-based methods for visual tracking of vehicles for use in a real-time system intended to provide continuous monitoring and classification of traffic from a fixed camera on a busy multi-lane motorway. The main constraints of the system design were: (i) all low level processing to be carried out by low-cost auxiliary hardware, (ii) all 3-D reasoning to be carried out automatically off-line, at set-up time. The system developed uses three main stages: (i) pose and model hypothesis using 1-D templates, (ii) hypothesis tracking, and (iii) hypothesis verification, using 2-D templates. Stages (i) & (iii) have radically different computing performance and computational costs, and need to be carefully balanced for efficiency. Together, they provide an effective way to locate, track and classify vehicles.

Detection and attribution of Atlantic salinity changes

Annual textural banding in Holocene estuarine silts, Severn Estuary Levels (SW Britain): patterns, cause and implications

A high-resolution textural study has been made of laminated and banded estuarine silts exposed intertidally at representative localities and horizons in the Holocene deposits of the Severn Estuary Levels. The laminae, on a submillimetre to millimetre scale, are sharp-based, graded couplets formed of a lower silty part overlain by a finer-textured clayey element. The centimetre- to decimetre-scale banding is formed of laminae in alternating, gradually intergrading sets of relatively coarse and relative fine-grained examples. At outcrop in the field, the banding is recognizable because the coarse sets prove to be recessive to varying degrees under the influence of weathering and current action. Independent evidence at two localities points toward an annual origin for the banding; at a third it arose during part of what appears to have been a relatively short period. Quantified physical arguments suggest that the textural banding is a response of suspended fine sediment to marked seasonal changes in sea temperature and windiness. The banded silts occur in four distinct stratigraphical contexts and record high deposition rates (order 0.01-0.1 m/yr). Because physical factors determine their textures, the silts potentially afford insights in all contexts into aspects of changing Holocene climatic conditions. In one context, the thickness of the bands points to high (order 0.01-0.1 m/yr) but comparatively short-lived (order 10s-100s yrs) rates of relative water-level rise. In the others, however, the banding has no implications for sea-level behaviour, and simply records gross environmental disequilibrium, for example, the recovery of mudflats/marshes after an erosional episode. Similarly, because on account of their rapid accumulation the banded silts preserve animal and human tracks and trackways especially well, they provide an archive of animal and human behaviour in the area during the Holocene.

Remote gas detection and quantitative analysis from infrared emission spectra obtained by Fourier transform infrared spectroscopy

Techniques for obtaining quantitative values of the temperatures and concentrations of remote hot gaseous effluents from their measured passive emission spectra have been examined in laboratory experiments. The high sensitivity of the spectrometer in the vicinity of the 2397 cm-1 band head region of CO2 has allowed the gas temperature to be calculated from the relative intensity of the observed rotational lines. The spatial distribution of the CO2 in a methane flame has been reconstructed tomographically using a matrix inversion technique. The spectrometer has been calibrated against a black body source at different temperatures and a self absorption correction has been applied to the data avoiding the need to measure the transmission directly. Reconstruction artifacts have been reduced by applying a smoothing routine to the inversion matrix.

Investigating age related changes in taste and affects on sensory perceptions of oral nutritional supplements

Background: sip feeds are oral nutritional supplements (ONSs) that are commonly prescribed to malnourished patients to improve their nutritional and clinical status. However, ONSs are poorly consumed and frequently wasted, with sweetness being identified as one of the factors leading to patients’ dislike of ONSs. Objectives: to investigate if age affects sweetness thresholds and if this impacts upon perceived sweetness intensity, hedonic (sweetness and overall) and ranked preference of ONS products. Design: prospective, observational. Subjects: thirty-six young adults (18–33 years) and 48 healthy older adults (63–85 years). Setting: Department of Food and Nutritional Sciences and the Clinical Health Sciences at the University of Reading. Methods: detection and recognition threshold levels, basic taste identification and ‘just about right’ level of sweetness were examined. Three ONSs (chocolate, vanilla, strawberry) and sucrose solutions were evaluated for hedonic sweetness, overall hedonic liking, sweetness intensity and rank preference. Results: significant differences were found in both sweetness detection and recognition thresholds (P = 0.0001) between young and older adults, with older adults more likely to incorrectly identify the taste (P = 0.0001). Despite the deterioration in sweetness sensitivity among the older adults, there were no significant differences found in sweetness intensity perceived for the ONS products presented (P > 0.05) when compared with the young adults. However, across both groups sweetness intensity was found to be correlated with overall product dislike across all flavour variants tested (R = 0.398, P = 0.0001). Conclusions: sweetness appears to be one of many factors contributing to the dislike of ONSs. Manufacturers are encouraged to reconsider the formulations of these products so that beneficial effects of ONSs can be delivered in a more palatable and acceptable form and wastage reduced.

Detection and enumeration of sulphate-reducing bacteria in estuarine sediments by competitive PCR

The distribution of sulphate-reducing bacteria (SRB) in the sediments of the Colne River estuary, Essex, UK covering different saline concentrations of sediment porewater was investigated by the use of quantitative competitive PCR. Here, we show that a new PCR primer set and a new quantitative method using PCR are useful tools for the detection and the enumeration of SRB in natural environments. A PCR primer set selective for the dissimilatory sulphite reductase gene (dsr) of SRB was designed. PCR amplification using the single set of dsr-specific primers resulted in PCR products of the expected size from all 27 SRB strains tested, including Gram-negative and positive species. Sixty clones derived from sediment DNA using the primers were sequenced and all were closely related with the predicted dsr of SRB. These results indicate that PCR using the newly designed primer set are useful for the selective detection of SRB from a natural sample. This primer set was used to estimate cell numbers by dsr selective competitive PCR using a competitor, which was about 20% shorter than the targeted region of dsr. This procedure was applied to sediment samples from the River Colne estuary, Essex, UK together with simultaneous measurement of in situ rates of sulphate reduction. High densities of SRB ranging from 0.2 - 5.7 × 108 cells ml-1 wet sediment were estimated by the competitive PCR assuming that all SRB have a single copy of dsr. Using these estimates cell specific sulphate reduction rates of 10-17 to 10-15 mol of SO42- cell-1 day-1 were calculated, which is within the range of, or lower than, those previously reported for pure cultures of SRB. Our results show that the newly developed competitive PCR technique targeted to dsr is a powerful tool for rapid and reproducible estimation of SRB numbers in situ and is superior to the use of culture-dependent techniques.

Group stochastic search for object detection and tracking

A technique is presented for locating and tracking objects in cluttered environments. Agents are randomly distributed across the image, and subsequently grouped around targets. Each agent uses a weightless neural network and a histogram intersection technique to score its location. The system has been used to locate and track a head in 320x240 resolution video at up to 15fps.

A new algorithm for OFDM joint data detection and phase noise cancellation

This paper proposes a new iterative algorithm for OFDM joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the problem of "overfitting" such that the iterative approach may converge to a trivial solution. Although it is essential for this joint approach, the overfitting problem was relatively less studied in existing algorithms. In this paper, specifically, we apply a hard decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the phase noise, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical simulations are also given to verify the proposed algorithm.

Hybrid fault detection and isolation in an heater bank of an HVAC system

In this work a hybrid technique that includes probabilistic and optimization based methods is presented. The method is applied, both in simulation and by means of real-time experiments, to the heating unit of a Heating, Ventilation Air Conditioning (HVAC) system. It is shown that the addition of the probabilistic approach improves the fault diagnosis accuracy.