Global PatentsLimits of Transnational Enforcement

Trimble, Marketa

doi:10.1093/acprof:oso/9780199840687.001.0001

Cited by 10 publications

(8 citation statements)

References 1 publication

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“…The purpose is to create image objects through partitioning the entire image into constituting objects (segments) based on the spectral signature and geometric characteristics of features. A multiresolution segmentation minimises the average heterogeneity and maximises their respective homogeneity (Trimble 2012). It is also used here.…”

Section: Image Segmentationmentioning

confidence: 99%

A socio-spatial methodology for evaluating urban land governance: the case of informal settlements

Alemie

Bennett

Zevenbergen

2015

Journal of Spatial Science

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Urban land has social and spatial dimensions. Governance of urban land should consider these dimensions. Existing methods of evaluating land governance tend to focus on the social dimensions: the spatial dimensions are considered less. A socio-spatial approach developed here is argued to fill this gap. This research supposes that informal settlements can be used to understand urban land governance. A conceptual framework that links urban land governance, socio-spatial dimensions and informal settlements is developed and tested through a case study. The results show that the socio-spatial methodology improved understanding of equity, efficiency and transparency as compared with the existing approaches which are solely based on poor-quality and unreliable data. This methodology can be used beyond informal settlements such as understanding infrastructural delivery and quality, mapping potential conflict areas and urban land uses where governance plays a great role. Overall, the socio-spatial methodology enabled an all-encompassing evaluation of urban land governance.

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Section: Image Segmentationmentioning

confidence: 99%

A socio-spatial methodology for evaluating urban land governance: the case of informal settlements

Alemie

Bennett

Zevenbergen

2015

Journal of Spatial Science

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“…We use several datasets in our evaluation: the COSEG dataset Wang et al 2012] containing several classes of smooth manifold shapes with ground truth per-face labeling, the correspondence benchmark of [Kim et al 2012a] that includes collections of polygon soups obtained from the 3D Warehouse [Trimble 2012] with consistently selected feature points. Further, to validate applicability of our method to analysis of very large and diverse datasets we created a set of large scale collections containing thousands of models.…”

Section: Resultsmentioning

confidence: 99%

Learning part-based templates from large collections of 3D shapes

et al. 2013

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As large repositories of 3D shape collections continue to grow, understanding the data, especially encoding the inter-model similarity and their variations, is of central importance. For example, many data-driven approaches now rely on access to semantic segmentation information, accurate inter-model point-to-point correspondence, and deformation models that characterize the model collections. Existing approaches, however, are either supervised requiring manual labeling; or employ super-linear matching algorithms and thus are unsuited for analyzing large collections spanning many thousands of models. We propose an automatic algorithm that starts with an initial template model and then jointly optimizes for part segmentation, point-to-point surface correspondence, and a compact deformation model to best explain the input model collection. As output, the algorithm produces a set of probabilistic part-based templates that groups the original models into clusters of models capturing their styles and variations. We evaluate our algorithm on several standard datasets and demonstrate its scalability by analyzing much larger collections of up to thousands of shapes.

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“…The recommendations from these SWOT analyses have been used to strengthen and guide this promising new field of GIScience (Hay & Castilla, 2006) Steiniger and Hay (2009). A leading commercial software product is eCognition (Trimble, 2012).…”

Section: Geobia Color and Texture Features In Object Recognitionmentioning

confidence: 99%

Environmental Object Recognition in a Natural Image

Aryal

Josselin

2014

International Journal of Agricultural and Environmental Information Systems

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Natural images, which are filled with intriguing stimuli of spatial objects, represent our cognition and are rich in spatial information. Accurate extraction of spatial objects is challenging due to the associated spatial and spectral complexities in object recognition. In this paper, the authors tackle the problem of spatial object extraction in a GEOgraphic Object Based Image Analysis framework taking psychological and mathematical complexities into account. In doing so, the authors experimented with human and GEOBIA based recognition and segmentation in an image of an area of natural importance, the Ventoux Mountain, France. Focus was given to scales, color, and texture properties at multiple levels in delineating the candidate spatial objects from the natural image. Such objects along with the original image were provided to the human subjects in two stages and three different groups of samples. The results of two stages were collated and analyzed. The analysis showed that there exist different ways to comprehend the geographical objects according to priori knowledge.

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Global PatentsLimits of Transnational Enforcement

Cited by 10 publications

References 1 publication

A socio-spatial methodology for evaluating urban land governance: the case of informal settlements

A socio-spatial methodology for evaluating urban land governance: the case of informal settlements

Learning part-based templates from large collections of 3D shapes

Environmental Object Recognition in a Natural Image

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