A Collaborative Process for Developing Map Symbol Standards

Exploiting the relevant speech information that is embedded in facial images has been a significant research topic in recent years, because it has provided complementary information to acoustic signals for a wide range of automatic speech recognition (ASR) tasks. Visual information is particularly important in many real applications where acoustic signals are corrupted by environmental noises. This chapter reviews the most recent advances in feature extraction and representation for Visual Speech Recognition (VSR). In comparison with other surveys published in the past decade, this chapter presents a more up-to-date survey and highlights the strengths of two newly developed approaches (i.e., graph-based learning and deep learning) for VSR. In particular, we summarise the methods of using these two techniques to overcome one of the most challenging difficulties in this area-that is, how to automatically learn good visual feature representations from facial images to replace the widely used handcrafted features. This chapter concludes by discussing potential visual feature representation solutions that may overcome the remaining challenges in this domain.

We discuss the novel application of homeland security functional exercises to evaluate emergency managers’ use of simulation-based decision support tools for response to major coastal storms (e.g., hurricanes, tropical storms, nor’easters), such as Hurricane Katrina. The U.S. Department of Homeland Security’s “Homeland Security Exercise and Evaluation Program” (HSEEP) is the universally accepted standard for emergency management exercises in the United States and provides a templated format familiar to emergency management practitioners. Using HSEEP in a scenario-based approach to end-user evaluation provides a plausible, realistic environment that encourages open and honest player feedback. We present the results of two HSEEP-based functional exercises run as part of implementation research with the Coastal Hazards Analysis, Modeling, and Prediction (CHAMP) system. CHAMP combines high-resolution storm models with a database of critical infrastructure vulnerabilities to predict storm consequences and aid decision-making. Findings suggest that the HSEEP exercise format provides a ready-made process for evaluating emergency management tools in a format comfortable and familiar to participants. User feedback and observation data collected during the CHAMP workshops and exercises are used to inform real-world activation protocols and to guide ongoing development of the CHAMP system.

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

Content based Image Retrieval (CBIR) allows automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. Colour and texture features are important properties in CBIR systems. In this paper, a combined feature descriptor for CBIR is proposed to enhance the retrieval performance for CBIR. This method is developed by exploiting the wavelets and colour histogram moments. First, Haar wavelet is used to decompose colour images into wavelet coefficients. Then image feature extraction and similarity matching are performed by means of histogram moments. Ten categories of colour images are used to test the proposed technique. Experiment results show significant improvement of retrieval efficiency as compared to that of histogram moments as well as 2D Discrete Wavelet Transform.

Content-Based Image Retrieval (CBIR) allows to automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. Color, texture and shape information have been the primitive image descriptors in content-based image retrieval systems. This paper presents a fast and efficient image indexing and search system based on color and texture features. The color features are represented by combines 2-D histogram and statistical moments and texture features are represented by combines the direction measure and a gray level co-occurrence matrix (GLCM). The detailed experimental analysis is carried out using precision and recall ondataset Wang. The time analysis is also performed to compare processing speeds of the proposed method with the existing similar best. The experimental results demonstrated that proposed method achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. The performance is measured in terms of recall and precision; also the obtained performances are compared with several state-of-the-art algorithms and showed that our algorithm is simple, fast, and efficient in terms of results and memory. Keywords: Co-occurrence Matrix, Direction Measure, 2-D Histogram, Statistical Moments, CBIR, GLCM

The ever-increasing numbers of medical images in digital format are generated in clinical practice every day. These images include high-resolution and temporal data of various modalities and which constitute an important source of anatomical and functional information for diagnosis of diseases, research and education. So how to manage these large data and utilize them effectively and efficiently possess significant technical challenges. Thus, the technique of Content-based Medical Image Retrieval (CBMIR) emerges as the times require. However, current CBMIR is not sufficient to capture the semantic content of an image and difficult to provide good results according to the predefined categories in the medical domain for less using the medical knowledge. Accordingly, in this paper a mapping modelling of visual feature and knowledge representation is proposed to approach for medical image retrieval. Firstly, the low-level fusion visual features are extracted based on intensity, texture, and their extended versions. Secondly, a set of disjoint semantic tokens with appearance in lung CT images is selected to define a vocabulary based on medical knowledge representation. Finally, a mapping modeling is investigated to associate low-level visual image features with their high-level semantic. Experiments are conducted with a medical image database consisting of 220 lung medical images obtained from the Heilongjiang Province Hospital. And the comparison of the retrieval results shows that the approach proposed in this paper is effective.

The Federal Radiological Monitoring and Assessment Center (FRMAC) is a federal emergency response asset whose assistance may be requested by the Department of Homeland Security (DHS), the Department of Defense (DoD), the Environmental Protection Agency (EPA), the Nuclear Regulatory Commission (NRC), and state and local agencies to respond to a nuclear or radiological incident. It is an interagency organization with representation from the Department of Energy’s National Nuclear Security Administration (DOE/NNSA), the Department of Defense (DoD), the Environmental Protection Agency (EPA), the Department of Health and Human Services (HHS), the Federal Bureau of Investigation (FBI), and other federal agencies. FRMAC, in its present form, was created in 1987 when the radiological support mission was assigned to the DOE’s Nevada Operations Office by DOE Headquarters. The FRMAC asset, including its predecessor entities, was created, grew, and evolved to function as a response to radiological incidents. Radiological emergency response exercises showed the need for a coordinated approach to managing federal emergency monitoring and assessment activities. The mission of FRMAC is to coordinate and manage all federal radiological environmental monitoring and assessment activities during a nuclear or radiological incident within the United States in support of state,local, tribal governments, DHS, and the federal coordinating agency. Radiological emergency response professionals with the DOE’s national laboratories support the Radiological Assistance Program (RAP), National Atmospheric Release Advisory Center (NARAC), the Aerial MeasuringSystem (AMS), and the Radiation Emergency Assistance Center/Training Site (REAC/TS). These teams support the FRMAC to provide: Atmospheric transport modeling; Radiation monitoring; Radiological analysis and data assessments; and Medical advice for radiation injuries In support of field operations, the FRMAC provides geographic information systems, communications, mechanical, electrical, logistics, and administrative support. The size of the FRMAC is tailored to the incident and is comprised of emergency response professionals drawn from across the federal government. State and local emergency response teams may also integrate their operations with FRMAC, but are not required to.

Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's relevance feedback. The experimental results show that the proposed approach greatly reduces the user's effort of composing a query and captures the user's information need more precisely.

It's well known that visual information such as lip shape and its movement can indicate what the speaker is talking about. In this paper, we present an automatic lipreading system solely using visual information for recognizing isolated English digits from 0 to 9. A parameter set of a 14-point ASM lip model is used to describe the outer lip contour. The inner mouth information such as the teeth region and the mouth opening are also extracted. With appropriate normalization, the feature vectors containing the normalized outer lip features, inner mouth features and also their first order derivatives are obtained for training the HMM word models. Experiments have been carried out to investigate the recognition performance using our visual feature set compared with other traditional visual feature representations. An accuracy of 93% for speaker dependent recognition and 84% for speaker independent recognition is achieved using our visual feature representation. A real-time automatic lipreading system has been successfully implemented on a 1.9-GHz PC.

This work is about the relevance of Gibson's concept of affordances [1] for visual perception in interactive and autonomous robotic systems. In extension to existing functional views on visual feature representations, we identify the importance of learning in perceptual cueing for the anticipation of opportunities for interaction of robotic agents. We investigate how the originally defined representational concept for the perception of affordances - in terms of using either optical flow or heuristically determined 3D features of perceptual entities - should be generalized to using arbitrary visual feature representations. In this context we demonstrate the learning of causal relationships between visual cues and predictable interactions, and emphasize on a novel framework for cueing and hypothesis verification of affordances that could play an important role in future robot control architectures. We argue that affordance based perception should enable systems to react to environment stimuli both more efficient and autonomous, and provide a potential to plan on the basis of responses to more complex perceptual configurations. We verify the concept with a concrete implementation applying state-of-the-art visual descriptors and regions of interest within a simulated robot scenario and prove that these features were successfully selected for predicting opportunities of robot interaction.

Recently, the aspect of visual perception has been explored in the context of Gibson's concept of affordances [1] in various ways [4-9]. In extension to existing functional views on visual feature representations, we focus on the importance of learning in perceptual cueing for the anticipation of opportunities for interaction of robotic agents. Furthermore, we propose that the originally defined representational concept for the perception of affordances - in terms of using either optical flow or heuristically determined 3D features of perceptual entities - should be generalized towards using arbitrary visual feature representations. In this context we demonstrate the learning of causal relationships between visual cues and associated anticipated interactions, using visual information within the framework of Markov Decision Processes (MDPs). We emphasize a new framework for cueing and recognition of affordance-like visual entities that could play an important role in future robot control architectures. Affordance-like perception should enable systems to react to environment stimuli both more efficiently and autonomously, and provide a potential to plan on the basis of relevant responses to more complex perceptual configurations. We verify the concept with a concrete implementation of learning visual cues by reinforcement, applying state-of-the-art visual descriptors and regions of interest that were extracted from a simulated robot scenario and prove that these features were successfully selected for their relevance in predicting opportunities of robot interaction.

Color histogram is one of the most widely used visual feature representations the JPEG/MPEG compressed domains, the DC coefficients are used commonly to form the color histograms without fully decompressing the image or the video bit stream. In this paper, we address the issues arising from the adaptation of DC based color histograms to the arbitrarily shaped MPEG-4 video objects. More specifically, we discuss the color space selection, quantization, and histogram computation with the consideration of the specific characteristics of the MPEG-4 video objects. We also propose a method for reducing the chroma keying artifacts that may occur at the boundaries of the objects. The experimental results show that great retrieval performance improvements are achieved by employing the proposed method in the presence of such artifacts.KeywordsColor SpaceColor HistogramRetrieval PerformanceRetrieval ResultBoundary BlockThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work is about the relevance of Gibson’s concept of affordances [1] for visual perception in interactive and autonomous robotic systems. In extension to existing functional views on visual feature representations, we identify the importance of learning in perceptual cueing for the anticipation of opportunities for interaction of robotic agents. We investigate how the originally defined representational concept for the perception of affordances – in terms of using either optical flow or heuristically determined 3D features of perceptual entities – should be generalized to using arbitrary visual feature representations. In this context we demonstrate the learning of causal relationships between visual cues and predictable interactions, using both 3D and 2D information. In addition, we emphasize a new framework for cueing and recognition of affordance-like visual entities that could play an important role in future robot control architectures. We argue that affordance-like perception should enable systems to react to environment stimuli both more efficient and autonomous, and provide a potential to plan on the basis of responses to more complex perceptual configurations. We verify the concept with a concrete implementation applying state-of-the-art visual descriptors and regions of interest that were extracted from a simulated robot scenario and prove that these features were successfully selected for their relevance in predicting opportunities of robot interaction.

Interactive Machine Learning considers that a robot is learning with and/or from a human. In this paper, we investigate the impact of human social traits on the robot learning. We explore social traits such as age (children vs. adult) and pathology (typical developing children vs. children with autistic spectrum disorders). In particular, we consider learning to recognize both postures and identity of a human partner. A human-robot posture imitation learning, based on a neural network architecture, is used to develop a multi-task learning framework. This architecture exploits three learning levels : 1) visual feature representation, 2) posture classification and 3) human partner identification. During the experiment the robot interacts with children with autism spectrum disorders (ASD), typical developing children (TD) and healthy adults. Previous works assessed the impact on learning of these social traits at the group level. In this paper, we focus on the analysis of individuals separately. The results show that the robot is impacted by the social traits of these different groups' individuals. First, the architecture needs to learn more visual features when interacting with a child with ASD (compared to a TD child) or with a TD child (compared to an adult). However, this surplus in the number of neurons helped the robot to improve the TD children's posture recognition but not that of children with ASD. Second, preliminary results show that this need of a neurons surplus while interacting with children with ASD is also generalizable to the identity recognition task.

Accurate detection of volatile organic compounds (VOCs) in complex background plays an important role in both natural and human-related activities, such as environmental protection, medical screening, food safety, and homeland security. Reliable monitoring of atmospheric VOCs is vital because vapor exposure can accelerate air-borne toxic generation and lead to tropospheric air pollution. While conventional analytical instruments such as gas chromatography-mass spectrometry (GC-MS) are largely used for identification of unknown VOCs, it is desirable for sensing technology to transition into a low cost, energy-efficient, and miniaturized media that can be implemented into personal devices.As one of the fastest growing types of chemical sensors, chemiresistors possess many desirable characteristics such as straightforward fabrication and easy to integrate. Sensing responses for nanoparticle-based chemiresistor are based on the sorbent-phase interaction of an analyte with the sensing materials, which can be explained by a thermally activated electron tunneling mechanism. The morphology of the gold-nanoparticle (AuNP) assembly (i.e. the interparticle distance) between the electrodes determines the baseline resistance of that individual sensor element.In this work, we demonstrate the detection and classification of 20 VOC vapors at room temperature using a multi-AuNP chemiresistor array fabricated with gold nanoparticles with chemically diverse ligands, including 4-(dimethylamino)pyridine (DMAP), tetradecylamine (TDA), and 4-aminothiophenol (4-ATP). The castellated electrode structures were fabricated using standard photolithography and liftoff processes. Classification accuracy was determined using a machine learning algorithm on a matrix of sensing response data. Supervised classifiers applied in this work include linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF). The results demonstrated that pattern recognition accuracy increases when a combination of ligands was used, as compared to sensors with one type of ligand. This proof-of-concept study illustrated that we were able to improve classification accuracy by choosing ideal sensing elements with optimized built-in redundancy. Meanwhile, a vapor sorption model was established to predict the chemiresistive sensor responses. One of the biggest challenges of AuNP-based chemiresistive sensor technology is finding the best candidates of materials that provide sufficient resolving power to differentiate chemicals in complex surroundings for molecular pattern recognition. To evaluate the sensor performance of ligand-analyte interactions, a UNIFAC (UNIQUAC Functional-group Activity Coefficients) model was established in this study to predict the sorption between a certain ligand material and VOC vapors. Therefore, a more performance-efficient sensing element library can be established for different types of analytes. Specifically, partition coefficients were determined from the UNIFAC method, and the sorbent phase concentration was obtained from modified Raoult’s Law. To validate our model, simulated UNIFAC partition coefficients were compared with experimental results and data reported from literature. Besides sensor responses, other characteristic parameters of chemiresistor performance are also studied, including sensitivity, sensitivity ratio, volumetric sensitivity, and molar sensitivity. This work illustrated the versatility of gold nanoparticle-based chemiresistor array systems for VOC detection, discrimination, and sensor signal prediction. Figure 1