Hyperspectral imaging for food applications

Food quality analysis is a key area where reliable, nondestructive and accurate measures are required. Hyperspectral imaging is a technology which meets all of these requirements but only if appropriate signal processing techniques are implemented. In this paper, a discussion of some of these state-of-the-art processing techniques is followed by an explanation of four different applications of hyperspectral imaging for food quality analysis: shelf life estimation of baked sponges; beef quality prediction; classification of Chinese tea leaves; and classification of rice grains. The first two of these topics investigate the use of hyperspectral imaging to produce an objective measure about the quality of the food sample. The final two studies are classification problems, where an unknown sample is assigned to one of a previously defined set of classes

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

Vision-based techniques for gait recognition

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance

Novel features in accelerometer-based gait analysis for long-term monitoring of Parkinson’s disease : a signature of gait.

PhD ThesisParkinson’s Disease (PD) is a neurodegenerative disease that can lead to restricted or slowed movement, gait impairments and increased risk of falling. Over recent decades, instrumented gait analysis (IGA) has contributed much to the understanding of gait impairments in PD. Due to the complexity of gait and high clinical interest a plethora of features have been suggested for gait analysis in the literature pertaining to several groups such as: traditional spatio-temporal (e.g. gait speed), frequency domain, etc. A subset of these traditional gait features has been proposed and validated in PD and older adults as a comprehensive model of gait comprising five factors: pace, rhythm, asymmetry, variability, and postural control. Analysis of gait may be grouped into the assessment of two types of variability, namely, within-subject variability which is needed for personal disease management and inter-subject variability which is useful in quantifying the overall impact of PD on gait. Advances in wearable technology have led to much smaller devices (e.g. accelerometers) being commercially available in conjunction with greatly increased battery lives to the degree that not only lab-based but also continuous recordings over 7 days (real-world) are possible. Wearable technology-based gait analysis is indeed emerging as a powerful tool to detect early disease and monitor progression. Data recorded as part of the ICICLE-GAIT 1 study provides acceleration data for over 100 people with PD and age-matched control subjects in both lab and realworld conditions. These datasets form the basis for the development of a new Phase plot methodology for gait analysis in PD. In this thesis I present a novel methodology for both assessing PD and tracking individual disease progression over multiple timescales. To accomplish this, I introduce a new feature domain, the Phase domain, based on a particular type of recurrence plot known as a Poincar´e plot. Poincar´e plots are sometimes referred to in the literature as return maps, self-similarity plots or Phase plots. Phase plots were being used in the early 1990s in ECG studies to produce self-similarity plots of beat-to-beat intervals. This technique proved to be reliable in detecting atrial fibrillation. The rare instances of its application to other fields are very limited and do not demonstrate any modification or development beyond that which has been used in ECG studies for decades. I develop methodology for application to gait analysis and, indeed, any cyclical biosignals. In this thesis I used the data from the ICICLE-GAIT study to demonstrate that with specific modifications and newly identified features (comprising the Phase domain), this novel Phase plot methodology is highly applicable to gait analysis within PD and provides a framework for: (i) identifying and characterising PD and (ii) individual disease tracking over the years following diagnosis. Throughout these analyses, traditional gait features serve as an established reference and benchmark. I employ statistical methods, such as non-linear mixed effects models and Statistical Parametric Mapping, to model PD progression and assess the clinical utility of Phase plots. I also used Discrete-Time Markov chain modelling, longitudinal analyses, and functional principal components analysis to demonstrate that Phase plots provide an objective, personalised, and clinically relevant signature of gait. In the case of PD patients (and controls to a lesser extent) four distinct Phase plot Types emerge and occur with high within-subject reproducibility, hence the signature interpretation. Many features within the Phase domain proved to be highly sensitive to the disease (people with PD versus controls). Using lab-based data, the Phase domain features outperformed traditional spatio-temporal features in classifying PD. Each domain of features performed similarly well in the prediction of MDS-UPDRS 2 (a useful proxy for PD progression). Specifically, part III of the UPDRS scale was used as this relates to motor function. In real-world conditions Phase plot features showed sensitivity to disease state and physical capability across multiple timescales e.g., daily fluctuations, and also across 18-month follow up time points. The Phase plot-based signature of gait is validated under lab-based conditions to reflect participants’ capacity for gait as well as under real-world conditions as a compact means of monitoring PD and walking performance through gait

Extending quality and covariate analyses for gait biometrics

Recognising humans by the way they walk has attracted a significant interest in recent years due to its potential use in a number of applications such as automated visual surveillance. Technologies utilising gait biometrics have the potential to provide safer society and improve quality of life. However, automated gait recognition is a very challenging research problem and some fundamental issues remain unsolved.At the moment, gait recognition performs well only when samples acquired in similar conditions are matched. An operational automated gait recognition system does not yet exist. The primary aim of the research presented in this thesis is to understand the main challenges associated with deployment of gait recognition and to propose novel solutions to some of the most fundamental issues. There has been lack of understanding of the effect of some subject dependent covariates on gait recognition performance. We have proposed a novel dataset that allows analyses of various covariates in a principled manner. The results of the database evaluation revealed that elapsed time does not affect recognition in the short to medium term, contrary to what other studies have concluded. The analyses show how other factors related to the subject affect recognition performance.Only few gait recognition approaches have been validated in real world conditions. We have collected a new dataset at two realistic locations. Using the database we have shown that there are many environment related factors that can affect performance. The quality of silhouettes has been identified as one of the most important issues for translating gait recognition research to the ‘real-world’. The existing quality algorithms proved insufficient and therefore we extended quality metrics and proposed new ways of improving signature quality and therefore performance. A new fully working automated system has been implemented.Experiments using the system in ‘real-world’ conditions have revealed additional challenges not present when analysing datasets of fixed size. In conclusion, the research has investigated many of the factors that affect current gait recognition algorithms and has presented novel approaches of dealing with some of the most important issues related to translating gait recognition to real-world environments

Human Identification Using Gait

Keeping in view the growing importance of biometric signatures in automated security and surveillance systems, human gait recognition provides a low-cost non-obtrusive method for reliable person identification and is a promising area for research. This work employs a gait recognition process with binary silhouette-based input images and Hidden Markov Model (HMM)-based classification. The performance of the recognition method depends significantly on the quality of the extracted binary silhouettes. In this work, a computationally low-cost fuzzy correlogram based method is employed for background subtraction. Even highly robust background subtraction and shadow elimination algorithms produce erroneous outputs at times with missing body portions, which consequently affect the recognition performance. Frame Difference Energy Image (FDEI) reconstruction is performed to alleviate the detrimental effect of improperly extracted silhouettes and to make the recognition method robust to partial incompleteness. Subsequently, features are extracted via two methods and fed to the HMM based classifier which uses Viterbi decoding and Baum-Welch algorithm to compute similarity scores and carry out identification. The direct method uses extracted wavelet features directly for classification while the indirect method maps the higher-dimensional features into a lower dimensional space by means of a Frame-to-Exemplar-Distance (FED) vector. The FED uses the distance measure between pre-determined exemplars and the feature vectors of the current frame as an identification criterion. This work achieves an overall sensitivity of 86.44 % and 71.39 % using the direct and indirect approaches respectively. Also, variation in recognition performance is observed with change in the viewing angle and N and optimal performance is obtained when the path of subject parallel to camera axis (viewing angle of 0 degree) and at N = 5. The maximum recognition accuracy levels of 86.44 % and 80.93 % with and without FDEI reconstruction respectively also demonstrate the significance of FDEI reconstruction step

Pose Invariant Gait Analysis And Reconstruction

One of the unique advantages of human gait is that it can be perceived from a distance. A varied range of research has been undertaken within the field of gait recognition. However, in almost all circumstances subjects have been constrained to walk fronto-parallel to the camera with a single walking speed. In this thesis we show that gait has sufficient properties that allows us to exploit the structure of articulated leg motion within single view sequences, in order to remove the unknown subject pose and reconstruct the underlying gait signature, with no prior knowledge of the camera calibration. Articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The variation of motion out of this plane is subtle and negligible in comparison to this major plane of motion. Subsequently, we can model human motion by employing a cardboard person assumption. A subject's body and leg segments may be represented by repeating spatio-temporal motion patterns within a set of bilaterally symmetric limb planes. The static features of gait are defined as quantities that remain invariant over the full range of walking motions. In total, we have identified nine static features of articulated leg motion, corresponding to the fronto-parallel view of gait, that remain invariant to the differences in the mode of subject motion. These features are hypothetically unique to each individual, thus can be used as suitable parameters for biometric identification. We develop a stratified approach to linear trajectory gait reconstruction that uses the rigid bone lengths of planar articulated leg motion in order to reconstruct the fronto-parallel view of gait. Furthermore, subject motion commonly occurs within a fixed ground plane and is imaged by a static camera. In general, people tend to walk in straight lines with constant velocity. Imaged gait can then be split piecewise into natural segments of linear motion. If two or more sufficiently different imaged trajectories are available then the calibration of the camera can be determined. Subsequently, the total pattern of gait motion can be globally parameterised for all subjects within an image sequence. We present the details of a sparse method that computes the maximum likelihood estimate of this set of parameters, then conclude with a reconstruction error analysis corresponding to an example image sequence of subject motion

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders