The PASCAL Visual Object Classes Challenge

Theory of fuzzy integrals and its applications

Bones of recent mammals in the Amboseli Basin, southern Kenya, exhibit distinctive weathering characteristics that can be related to the time since death and to the local conditions of temperature, humidity and soil chemistry. A categorization of weathering characteristics into six stages, recognizable on descriptive criteria, provides a basis for investigation of weathering rates and processes. The time necessary to achieve each successive weathering stage has been calibrated using known-age carcasses. Most bones decompose beyond recognition in 10 to 15 yr. Bones of animals under 100 kg and juveniles appear to weather more rapidly than bones of large animals or adults. Small-scale rather than widespread environmental factors seem to have greatest influence on weathering characteristics and rates. Bone weathering is potentially valuable as evidence for the period of time represented in recent or fossil bone assemblages, in- cluding those on archeological sites, and may also be an important tool in censusing populations of animals in modern ecosystems.

Taphonomic and Ecologic Information Form Bone Weathering

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

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Deep learning and process understanding for data-driven Earth system science

Human-Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.

/pdf/human-robot-interaction-a-survey-4q8yi606a9.pdf

Human-Robot Interaction: A Survey

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Linguistic summarization of video for fall detection using voxel person and fuzzy logic

Previously, we put forth a new computer vision system for indoor well-being monitoring of elderly populations based on the use of multiple stereo camera pairs. That approach involves combining the strengths of image space with three dimensional volume element (voxel) space techniques. However, that system is fundamentally limited because it is based on color imagery from visible light cameras. In this article, we extend our prior research and consider a new, inexpensive infrared depth camera device, the Microsoft Kinect. Advantages, such as the ability to operate 24–7 in low-to-no light conditions, and shortcomings are detailed. In addition, we discuss necessary algorithmic extensions to our mixed image and voxel space framework for the Kinect sensor. Experiments are performed in a laboratory designed to resemble an elders living quarter. Vision findings are evaluated using our prior high-level linguistic summarization of human activity work. Preliminary results indicate that the Kinect sensor does indeed work in a wider range of operating conditions and it can produce activity descriptions that match that of a human.

A system for change detection and human recognition in voxel space using the Microsoft Kinect sensor

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

A major problem among the elderly involves falling. The recognition of falls from video first requires the segmentation of the individual from the background. To ensure privacy, segmentation should result in a silhouette that is a binary map indicating only the body position of the individual in an image. We have previously demonstrated a segmentation method based on color that can recognize the silhouette and detect and remove shadows. After the silhouettes are obtained, we extract features and train hidden Markov models to recognize future performances of these known activities. In this paper, we present preliminary results that demonstrate the usefulness of this approach for distinguishing between a few common activities, specifically with fall detection in mind. and dynamic environments. High-level knowledge was fused with low-level feature-based classification to handle a time-varying background, and a decision process based on a fuzzy logic inference system was used to detach the moving objects from the human silhouette. One central task in silhouette extraction is background modeling. Once a background model is established, those image regions with significantly different characteristics from the background are considered foreground objects. In (5), a Least Median of Squares method was used to construct a background model, and in (6), a differencing function was proposed to extract moving human silhouettes. Elgammal et al. (7) demonstrated a non-parametric kernel density estimation method to model and subtract the human from a background. The adaptive background updating handles small motion in the background scene, such as moving tree branches. A slightly different approach outlined in (8) is based on processing in the YUV color space, and is capable of detecting shadows and extracting

/pdf/recognizing-falls-from-silhouettes-48vptr54d7.pdf

Derek T. Anderson

Papers

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Linguistic summarization of video for fall detection using voxel person and fuzzy logic

A system for change detection and human recognition in voxel space using the Microsoft Kinect sensor

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

Recognizing falls from silhouettes.