Plane-extraction from depth-data using a Gaussian mixture regression model

We propose a novel algorithm for unsupervised extraction of piecewise planar models from depth-data. Among other applications, such models are a good way of enabling autonomous agents (robots, cars, drones, etc.) to effectively perceive their surroundings and to navigate in three dimensions. We propose to do this by fitting the data with a piecewise-linear Gaussian mixture regression model whose components are skewed over planes, making them flat in appearance rather than being ellipsoidal, by embedding an outlier-trimming process that is formally incorporated into the proposed expectation-maximization algorithm, and by selectively fusing contiguous, coplanar components. Part of our motivation is an attempt to estimate more accurate plane-extraction by allowing each model component to make use of all available data through probabilistic clustering. The algorithm is thoroughly evaluated against a standard benchmark and is shown to rank among the best of the existing state-of-the-art methods.Comment: 11 pages, 2 figures, 1 tabl

HOW TO GROUP MARKET PARTICIPANTS? HETEROGENEITY IN HEDGING BEHAVIOR

Using a generalized mixture model, we model individual heterogeneity by identifying groups of participants that respond in a similar manner to the determinants of economic behavior. The procedure emphasizes the role of theory as the determinants of behavior are used to simultaneously explain market activities and to discriminate among groups of market participants. We show the appealing properties of this modeling approach by comparing it with two often used grouping methods in an empirical study in which we estimate the factors affecting market participants' hedging behavior.Institutional and Behavioral Economics,

Quality Assurance and other Marketing Management Elements as Key Success Factors for Entering a New Market: a Case Presentation of Functional Food Market in Indonesia

Based on its distinctive profiles functional-food (FF) can be considered as a mixture of food and pharmaceutical items. Apparently, these different characteristics that exist beyond conventional food products contribute success of the commercialization of new innovative FFs. Therefore, assumption can be made by arguing that for the marketing a FF a distinctive marketing-strategic beyond the one usually used for the conventional food products must be employed. This study was pursued with the main aims to understand the consumers psychological factors and to find out elements important to setting up the marketing strategy. These two findings will be then used as basis for designing a distinctive marketing strategy for a FF. We found that consumers psychological set varied across different sample groups. Therefore, segmentation plays a significant role. Due to the fact that most of the consumers had a medium to high involvement level, communication strategy becomes a salient means for the marketing of FFs. According to the respondents some important extrinsic/intrinsic quality based elements of FF can be generally incorporated into the communication platforms for FF. From the industrys point of view this study showed that internal organizational and management, market attractiveness and trade capability are the most important elements supporting a firm in developing a new innovative FF in Indonesia.product quality, communication, segmentation, key success factors for market entry, functional food in Indonesia., Financial Economics, International Relations/Trade,

Object identification and characterization with hyperspectral imagery to identify structure and function of Natura 2000 habitats

Habitat monitoring of designated areas under the EU Habitats Directive requires every 6 years information on area, range, structure and function for the protected (Annex I) habitat types. First results from studies on heathland areas in Belgium and the Netherlands show that hyperspectral imagery can be an important source of information to assist the evaluation of the habitat conservation status. Hyperspectral imagery can provide continuous maps of habitat quality indicators (e.g., life forms or structure types, management activities, grass, shrub and tree encroachment) at the pixel level. At the same time, terrain managers, nature conservation agencies and national authorities responsible for the reporting to the EU are not directly interested in pixels, but rather in information at the level of vegetation patches, groups of patches or the protected site as a whole. Such local level information is needed for management purposes, e.g., exact location of patches of habitat types and the sizes and quality of these patches within a protected site. Site complexity determines not only the classification success of remote sensing imagery, but influences also the results of aggregation of information from the pixel to the site level. For all these reasons, it is important to identify and characterize the vegetation patches. This paper focuses on the use of segmentation techniques to identify relevant vegetation patches in combination with spectral mixture analysis of hyperspectral imagery from the Airborne Hyperspectral Scanner (AHS). Comparison with traditional vegetation maps shows that the habitat or vegetation patches can be identified by segmentation of hyperspectral imagery. This paper shows that spectral mixture analysis in combination with segmentation techniques on hyperspectral imagery can provide useful information on processes such as grass encroachment that determine the conservation status of Natura 2000 heathland areas to a large extent. A limitation is that both advanced remote sensing approaches and traditional field based vegetation surveys seem to cause over and underestimations of grass encroachment for specific categories, but the first provides a better basis for monitoring if specific species are not directly considered

Modeling Farmers' Use of Market Advisory Services

In an effort to improve marketing of their products, many farmers use market advisory services (MAS). To date, there is only fragmented anecdotal information about how farmers actually use the recommendations of market advisory services in their marketing plans, and how they choose among these services. Based on the literature on consulting services usage, a conceptual framework is developed in which perceived performance of the MAS regarding realized crop price and risk reduction, and the match between the MAS and the farmer's marketing philosophy drive MAS usage. To account for possible heterogeneity among farmers regarding to the use of MAS, we introduce a mixture-modeling framework that is able to identify unobserved heterogeneity. With this modeling framework we are able to simultaneously investigate the relationship between market advisory usage and the key components of our conceptual model for each unobservable segment in the population. A large scale interview of US farmers that contained several experiments revealed that farmers' use of MAS not only depends on the outcome of their services (price and risk reduction performance) but also on the way these services are delivered, i.e., the match of marketing philosophy between farmers and MAS. The influence of the factors in our conceptual model did not influenced farmers MAS usage equally across the whole sample. Using the generalized mixture model framework we found 5 segments that differed regarding the influence that these factors have on farmers MAS usage. The heterogeneity of the farmers appeared to be unobserved, in that it could not be traced back to observable variables such as age and region. It is the decision-making process itself, as reflected in our conceptual model, that caused the heterogeneity.Marketing,

A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region