Matthew J. Kyan

An Approach to Ballet Dance Training through MS Kinect and Visualization in a CAVE Virtual Reality Environment

This article proposes a novel framework for the real-time capture, assessment, and visualization of ballet dance movements as performed by a student in an instructional, virtual reality (VR) setting. The acquisition of human movement data is facilitated by skeletal joint tracking captured using the popular Microsoft (MS) Kinect camera system, while instruction and performance evaluation are provided in the form of 3D visualizations and feedback through a CAVE virtual environment, in which the student is fully immersed. The proposed framework is based on the unsupervised parsing of ballet dance movement into a structured posture space using the spherical self-organizing map (SSOM). A unique feature descriptor is proposed to more appropriately reflect the subtleties of ballet dance movements, which are represented as gesture trajectories through posture space on the SSOM. This recognition subsystem is used to identify the category of movement the student is attempting when prompted (by a virtual instructor) to perform a particular dance sequence. The dance sequence is then segmented and cross-referenced against a library of gestural components performed by the teacher. This facilitates alignment and score-based assessment of individual movements within the context of the dance sequence. An immersive interface enables the student to review his or her performance from a number of vantage points, each providing a unique perspective and spatial context suggestive of how the student might make improvements in training. An evaluation of the recognition and virtual feedback systems is presented.

Visualization of Trunk Muscle Synergies During Sitting Perturbations Using Self-Organizing Maps (SOM)

The purpose of this study was to demonstrate the use of the self-organizing map (SOM) method for visualization, modeling, and comparison of trunk neuromuscular synergies during perturbed sitting. Thirteen participants were perturbed at the level of the sternum, in eight directions during sitting. Electromyographic (EMG) responses of ten trunk muscles involved in postural control were recorded. The SOM was used to encode the EMG responses on a 2-D projection (i.e., visualization). The result contains similar patterns mapped close together on the plot therefore forming clusters of data. Such visualization of ten EMG responses, following eight directional perturbations, allows for comparisons of direction-dependent postural synergies. Direction-dependent neuromuscular response models for each muscle were then constructed from the SOM visualization. The results demonstrated that the SOM was able to encode neuromuscular responses, and the SOM visualization showed direction-dependent differences in the postural synergies. Moreover, each muscle was modeled using the SOM-based method, and derived models showed that all muscles, except for one, produced a Gaussian fit for direction-dependent responses. Overall, SOM analysis offers a reverse engineering method for exploration and comparison of complex neuromuscular systems, which can describe postural synergies at a glance.

A combined just noticeable distortion model guided image watermarking

Perceptual watermarking should take full advantage of the results from human visual system (HVS) studies. Just noticeable distortion (JND), which refers to the maximum distortion that the HVS does not perceive, gives us a way to model the HVS accurately. In this paper, we exploit a combined JND model, which represents an additional, accurate, perceptual visibility threshold profile to guide watermarking for digital images. The combined JND model-guided watermarking scheme, where visual models are fully used to determine image-dependent upper bounds on watermark insertion, allows us to provide the maximum strength transparent watermark. Experimental results confirm the improved performance of our combined JND model. Our combined JND model is capable of yielding higher injected-watermark energy without introducing noticeable distortion to the original image and outperforms the relevant existing visual models. Simulation results show that the proposed JND model-guided image watermarking scheme is more robust than other algorithms based on the relevant existing perceptual models while retaining the watermark transparency. At the same time, the proposed combined JND model has much lower computational complexity compared with the relevant existing perceptual models.

Visual saliency's modulatory effect on just noticeable distortion profile and its application in image watermarking

Perceptual watermarking should take full advantage of the results from human visual system (HVS) studies. Just noticeable distortion (JND) gives us a way to model the HVS accurately. In this paper, another very important aspect affecting human perception, visual saliency, is introduced to modulate JND model. Based on the visual saliency’s modulatory effect on JND model which incorporates visual attention’s influence on visual sensitivity, the saliency modulated JND profile guided image watermarking scheme is proposed. The saliency modulated JND profile guided watermarking scheme, where the visual sensitivity model combined with visual saliency’s modulatory effect is fully used to determine image-dependent upper bounds on watermark insertion, allows us to provide the maximum strength transparent watermark. Experimental results confirm the improved performance of our saliency modulated JND profile guided watermarking scheme in terms of transparency and robustness. Our watermarking scheme is capable of shaping lower injected-watermark energy onto more sensitive regions and higher energy onto the less perceptually significant regions in the image, which yields better visual quality of the watermarked image. At the same time, the proposed saliency modulated JND profile guided image watermarking scheme is more robust compared to unmodulated JND profile guided image watermarking scheme.

On-line signature verification using global features

On-line signature verification based on global features in an integration with Fisher Linear Discriminant Analysis (FLD) have been proposed in this paper. In the verification phase, distances of features of test signature are calculated against their corresponding template. Finally, these distances become inputs to FLD. User-dependent threshold has been used to evaluate the performance of our proposed method in comparison to other existing methods. We have used single-session and mix-session protocols for the evaluation of our proposed method using SUSIG database. Experimental results demonstrate the superiority of our approach in On-line signature verification in comparison with other techniques.

Visual and emotional salience influence eye movements

In natural vision both stimulus features and cognitive/affective factors influence an observers attention. However, the relationship between stimulus-driven (bottom-up) and cognitive/affective (top-down) factors remains controversial: How well does the classic visual salience model account for gaze locations? Can emotional salience counteract strong visual stimulus signals and shift attention allocation irrespective of bottom-up features? Here we compared Itti and Kochs [2000] and Spectral Residual (SR) visual salience model and explored the impact of visual salience and emotional salience on eye movement behavior, to understand the competition between visual salience and emotional salience and how they affect gaze allocation in complex scenes viewing. Our results show the insufficiency of visual salience models in predicting fixation. Emotional salience can override visual salience and can determine attention allocation in complex scenes. These findings are consistent with the hypothesis that cognitive/affective factors play a dominant role in active gaze control.

On-line Signature Verification Using Most Discriminating Features and Fisher Linear Discriminant Analysis (FLD)

In this work, we employ a combination of strategies for partitioning and detecting abnormal fluctuations in the horizontal and vertical trajectories of an on-line generated signature profile. Alternative partitions of these spatial trajectories are generated by splitting each of the related angle, velocity and pressure profiles into two regions representing both high and low activity. The overall process can be thought of as one that exploits inter-feature dependencies by decomposing signature trajectories based upon angle, velocity and pressure - information quite characteristic to an individualpsilas signature. In the verification phase, distances of each partitioned trajectory of a test signature are calculated against a similarly partitioned template trajectory for a known signer. Finally, these distances become inputs to Fisherpsilas Linear Discriminant Analysis (FLD). Experimental results demonstrate the superiority of our approach in On-line signature verification in comparison with other techniques.

Measuring the optical characteristics of medulloblastoma with optical coherence tomography

Medulloblastoma is the most common malignant pediatric brain tumor. Standard treatment consists of surgical resection, followed by radiation and high-dose chemotherapy. Despite these efforts, recurrence is common, leading to reduced patient survival. Even with successful treatment, there are often severe long-term neurologic impacts on the developing nervous system. We present two quantitative techniques that use a high-resolution optical imaging modality: optical coherence tomography (OCT) to measure refractive index, and the optical attenuation coefficient. To the best of our knowledge, this study is the first to demonstrate OCT analysis of medulloblastoma. Refractive index and optical attenuation coefficient were able to differentiate between normal brain tissue and medulloblastoma in mouse models. More specifically, optical attenuation coefficient imaging of normal cerebellum displayed layers of grey matter and white matter, which were indistinguishable in the structural OCT image. The morphology of the tumor was distinct in the optical attenuation coefficient imaging. These inherent properties may be useful during neurosurgical intervention to better delineate tumor boundaries and minimize resection of normal tissue.

Self-Organizing Maps for Topic Trend Discovery

The large volume of data on the Internet makes it extremely difficult to extract high-level information, such as recurring or time-varying trends in document content. Dimensionality reduction techniques can be applied to simplify the analysis process but the amount of data is still quite large. If the analysis is restricted to just text documents then Latent Dirichlet Allocation (LDA) can be used to quantify semantic, or topical, groupings in the data set. This paper proposes a method that combines LDA with the visualization capabilities of Self-Organizing Maps to track topic trends over time. By examining the response of a map over time, it is possible to build a detailed picture of how the contents of a dataset change.

On-Line Signature Verification: Directional Analysis of a Signature Using Weighted Relative Angle Partitions for Exploitation of Inter-Feature Dependencies

In this paper, we propose a new directional analysis tool for On-line signatures that decomposes the given input signature into directional bands on the basis of relative angles. Our directional analysis tool takes the independent trajectories (horizontal and vertical) as an input and then decomposes them into directional bands on the basis of relative angles. We have used both user-dependent and user-independent thresholds for selecting an optimal number of partitions for each signer. By decomposing signature trajectories based upon relative angles of an individual’s signature, the resulting process can be thought of as one that exploits inter-feature dependencies . In the verification phase, distances of each partitioned trajectory of a test signature are calculated against a similarly partitioned template trajectory for a known signer. Each partition is then weighted based on its quality and quantity. Experimental results demonstrate the superiority of our approach to On-line signature verification in comparison with other techniques.