A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TPAMI.2007.70774Segmentation involves separating an object from the background in a given image. The use of image information alone often leads to poor segmentation results due to the presence of noise, clutter or occlusion. The introduction of shape priors in the geometric active contour (GAC) framework has proved to be an effective way to ameliorate some of these problems. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, using level-sets. Following the work of Leventon et al., we propose to revisit the use of PCA to introduce prior knowledge about shapes in a more robust manner. We utilize kernel PCA (KPCA) and show that this method outperforms linear PCA by allowing only those shapes that are close enough to the training data. In our segmentation framework, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description permits to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, occlusions, or smearing

Statistical Shape Analysis using Kernel PCA

©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.641417DOI:10.1117/12.641417Presented at Image Processing Algorithms and Systems, Neural Networks, and Machine Learning, 16-18 January 2006, San Jose, California, USA.Mercer kernels are used for a wide range of image and signal processing tasks like de-noising, clustering, discriminant analysis etc. These algorithms construct their solutions in terms of the expansions in a high-dimensional feature space F. However, many applications like kernel PCA (principal component analysis) can be used more effectively if a pre-image of the projection in the feature space is available. In this paper, we propose a novel method to reconstruct a unique approximate pre-image of a feature vector and apply it for statistical shape analysis. We provide some experimental results to demonstrate the advantages of kernel PCA over linear PCA for shape learning, which include, but are not limited to, ability to learn and distinguish multiple geometries of shapes and robustness to occlusions

River Otter Distribution in Nebraska

The river otter (Lontra canadensis) was extirpated from Nebraska, USA, in the early 1900s and reintroduced starting in 1986. Information is needed regarding the distribution of river otters in Nebraska before decisions can be made regarding its conservation status. Understanding distribution of a species is critically important for effective management. We investigated river otter distribution in Nebraska with occupancy modeling and maximum entropy (Maxent) modeling using 190 otter sign observations on Nebraska’s navigable rivers and 380 historical otter records from November 1977 to April 2014. Both methods identified the Platte River, Elkhorn River, central and eastern Niobrara River, and southern Loup River system as core areas within the distribution of otters in Nebraska. The Maxent model provided more liberal estimates of site occupancy and identified some smaller rivers as being within the distribution of otters in Nebraska, which were not identified using occupancy modeling. We recommend that multiple data sets and analysis methods be used to estimate species distribution because this allows for the broadest geographical coverage and decreases the likelihood of overlooking areas with fewer animal records. If further reintroduction efforts or translocation efforts are to take place in the future, we recommend focusing on areas with high modeled occupancy but few historical and survey records

Impact of future climate change on water temperature and thermal habitat for keystone fishes in the Lower Saint John River, Canada

Water temperature is a key determinant of biological processes in rivers. Temperature in northern latitude rivers is expected to increase under climate change, with potentially adverse consequences for cold water-adapted species. In Canada, little is currently known about the timescales or magnitude of river temperature change, particularly in large (≥104 km2) watersheds. However, because Canadian watersheds are home to a large number of temperature-sensitive organisms, there is a pressing need to understand the potential impacts of climate change on thermal habitats. This paper presents the results of a study to simulate the effects of climate change on the thermal regime of the lower Saint John River (SJR), a large, heavily impounded, socio-economically important watershed in eastern Canada. The CEQUEAU hydrological-water temperature model was calibrated against river temperature observations and driven using meteorological projections from a series of regional climate models. Changes in water temperature were assessed for three future periods (2030–2034, 2070–2074 and 2095–2099). Results show that mean water temperature in the SJR will increase by approximately ~1 °C by 2070–2074 and a further ~1 °C by 2095–2099, with similar findings for the maximum, minimum and standard deviation. We calculated a range of temperature metrics pertaining to the Atlantic Salmon and Striped Bass, key species within the SJR. Results show that while the SJR will become increasingly thermally-limiting for Atlantic Salmon, the Striped Bass growth season may actually lengthen under climate change. These results provide an insight into how climate change may affect thermal habitats for fish in eastern Canadian rivers

Operationalizing Ecological Resilience Concepts for Managing Species and Ecosystems at Risk

This review provides an overview and integration of the use of resilience concepts to guide natural resources management actions. We emphasize ecosystems and landscapes and provide examples of the use of these concepts from empirical research in applied ecology. We begin with a discussion of definitions and concepts of ecological resilience and related terms that are applicable to management. We suggest that a resilience-based framework for management facilitates regional planning by providing the ability to locate management actions where they will have the greatest benefits and determine effective management strategies. We review the six key components of a resilience-based framework, beginning with managing for adaptive capacity and selecting an appropriate spatial extent and grain. Critical elements include developing an understanding of the factors influencing the general and ecological resilience of ecosystems and landscapes, the landscape context and spatial resilience, pattern and process interactions and their variability, and relationships among ecological and spatial resilience and the capacity to support habitats and species. We suggest that a spatially explicit approach, which couples geospatial information on general and spatial resilience to disturbance with information on resources, habitats, or species, provides the foundation for resilience-based management. We provide a case study from the sagebrush biome that illustrates the use of geospatial information on ecological and spatial resilience for prioritizing management actions and determine effective strategies

Editorial: Operationalizing the Concepts of Resilience and Resistance for Managing Ecosystems and Species at Risk

Ecological resilience is essential for maintaining ecosystem services in an era of rapid global change, but successful attempts to operationalize it for managing ecosystems at risk have been limited. Clear formulation and application of ecological resilience concepts can guide ecosystem management so that it enhances the capacity of ecosystems to resist and recover from disturbances and provides adaptive space for periods of ecological reorganization. As originally defined, ecological resilience measures the amount of perturbation required to change an ecosystem from one set of processes and structures to a different set of processes and structures, or the amount of disturbance that a system can withstand before it shifts into a new regime or alternative stable state (Holling, 1973). In applied ecology, ecological resilience is increasingly used to evaluate the capacity of ecosystems to absorb, persist, and adapt to inevitable and often unpredictable change, and to use that information to determine the most effective management strategies (e.g., Chambers et al., 2014; Curtin and Parker, 2014; Pope et al., 2014; Seidl et al., 2016). As the scale and magnitude of ecological change increases, operationalizing ecological resilience for ecosystem management becomes ever more important. To date, much of the literature on ecological resilience has focused on theory, definitions, and broad conceptualizations (e.g., Gunderson, 2000; Folke et al., 2004, 2010; Walker et al., 2004; Folke, 2006; Gunderson et al., 2010). Much of the more applied research has focused on the importance of species diversity and species functional attributes in affecting responses to stress and disturbance (e.g., Pope et al., 2014; Angeler and Allen, 2016; Baho et al., 2017; Roberts et al., 2018). Recent, interdisciplinary research demonstrates that information on the relationships between an ecosystem’s environmental characteristics (climate, topography, soils, and potential biota) and its response to stress and disturbance provides a viable mechanism for assessing ecosystem resilience and relative risks (Chambers et al., 2014; Hessburg et al., 2016; Cushman et al., 2017; Kaszta et al., 2019). Approaches have been developed that enable application of resilience concepts at the scales needed for effective management of ecosystems experiencing progressive and deleterious change. For example, in the sagebrush biome of the western U.S. the concepts of resilience to fire and resistance to non-native invasive annual grasses have recently been used in an interagency framework to enhance conservation and restoration and help prevent listing of greater sage-grouse (Centrocercus urophasianus) under the Endangered Species Act (Chambers et al., 2017). In ecosystems around the globe, levels of ecological stress and disturbance are increasing while resources for natural resources management remain limited. Fully developing the capacity to operationalize the concept of ecological resilience can enable managers to prioritize the types and locations of management activities needed to optimize ecosystem conservation and restoration

Ambulance Reliability Control

The current ambulance design was studied in an effort to determine the features of the patient compartment that hinder EMT performance and compromise passenger safety. These features were redesigned to improve the functionality, reliability and safety of the modern ambulance through improved material selection, mechanical designs, optimized ergonomics and advanced technologies