Energy-Based Segmentation of Very Sparse Range Surfaces

This paper describes a new segmentation technique for very sparse surfaces which is based on minimizing the energy of the surfaces in the scene. While it could be used in almost any system as part of surface reconstruction/model recovery, the algorithm is designed to be usable when the depth information is scattered and very sparse, as is generally the case with depth generated by stereo algorithms. We show results from a sequential algorithm that processes laser range-finder data or synthetic data. We then discuss a parallel implementation running on the parallel Connection Machine. The idea of segmentation by energy minimization is not new. However, prior techniques have relied on discrete regularization or Markov random fields to model the surfaces to build smooth surfaces and detect depth edges. Both of the aforementioned techniques are ineffective at energy minimization for very sparse data. In addition, this method does not require edge detection and is thus also applicable when edge information is unreliable or unavailable. Our model is extremely general; it does not depend on a model of the surface shape but only on the energy for bending a surface. Thus the surfaces can grow in a more data-directed manner. The technique presented herein models the surfaces with reproducing kernel-based splines, which can be shown to solve a regularized surface reconstruction problem. From the functional form of these splines we derive computable bounds on the energy of a surface over a given finite region. The computation of the spline, and the corresponding surface representation are quite efficient for very sparse data. An interesting property of the algorithm is that it makes no attempt to determine segmentation boundaries; the algorithm can be viewed as a classification scheme which segments the data into collections of points which are "from" the same surface. Among the significant advantages of the method is the capacity to process overlapping transparent surfaces, as well as surfaces with large occluded areas

On the Recovery of Superellipsoids

Superellipsoids are parameterized solids which can appear like cubes or spheres or octahedrons or 8-pointed stars or anything in between. They can also be stretched bent tapered and combined with boolean to model a wide range of objects. Columbia's vision group is interested in using superquadrics as model primitives for computer vision applications because they are flexible enough to allow modeling of many objects, yet they can be described by a few (5-14) numbers. This paper discusses research into the recovery of superellipsoids from 3-D information, in particular range data. This research can be divided into two parts, a study of potential error-of-fit measures for recovering superquadrics, and implementation and experimentation with a system which attempts to recover superellipsoids by minimizing an error-of-fit measure. This paper presents an overview of work in both areas. Included are data from an initial comparison of 4 error-of-fit measures in terms of the inter-relationship between each measure and the parameters defining the superellipsoid. Also discussed is an experimental system which employs a nonlinear least square minimization technique to recover the parameters. This paper discusses both the advantages of this technique, and some of its major drawbacks. Examples are presented, using both synthetic and range-data, where the system successfully recovers superlliposids. Including "negative" volumes as would occur if superellipsoids were used in a constructive solid modeling system

Recovery of superquadrics from depth information

Superquadrics are a a class volumetric primitive which can model objects including rectangular solids with rounded corners, ellipsoids, octaheadrons, 8-pointed stars, hyperbolic sheets, and toroids with cross sections ranging from rectangles with rounded corners to elliptical regions. They can be stretched, bent, tapered and combined with boolean operations to model a wide range of objects. This paper discusses our progress at attempting to recover a subclass of superquadrics from 3D depth data. The first section of this paper presents a mathematical definition of superquadrics. Some of the rationale for using superquadrics for object recognition is then discussed. Briefly, superquadrics are flexible enough to represent a wide class of objects, but are simple enough to be recovered from 3d data. Additionally, the surface and its normal surface both have well defined inside-out functions which provide a useful tool for their recovery. The third section examines some of the difficulties to be encountered when modeling objects with superquadrics, or attempting to recover superquadrics from 3D data. These difficulties include the general problems of a non-orthogonal representation, difficulties of dealing with objects which are not exactly representable with CSG operations on the primitives, the need to recognize negative objects. Certain numerical instabilities and some problems caused by using the inside-out function as an approximation of the distance of a point from the superquadric. Our current system employs a nonlinear least square minimization technique on the inside-out function to recover the parameters. After discussing the details of the current system, the paper presents examples, using noisy synthetic data, where the system succe88fully uses multiple views to recover underlying superquadrics. Also presented are examples using range data, including the recovery of a negative superellipsiod. Some pros and cons of our approach as well as few conclusions, and a discussion of our planned future work appear in the final section. The main result is that least square minimization using the inside-out function allows both positive and negative instances of superellipsoids to be recovered from depth data. A second preliminary result is that a single view of a superquadric may not be sufficient for reconstruction without additional assumption

An Algorithm to Recover Generalized Cylinders from a Single Intensity View

Understanding a scene involves the ability to recover the shape of objects in an environment. Generalized cylinders are a flexible, loosely defined class of parametric shapes capable of modeling many real-world objects. Straight homogeneous generalized cylinders are an important subclass of generalized cylinders whose cross sections are scaled versions of a reference curve. In this paper, a general method is presented for recovering straight homogeneous generalized cylinders from monocular intensity images. The algorithm is much more general in scope than any other developed to date. combining constraints derived from both contour and intensity information. We first demonstrate that contour information alone is insufficient to recover a straight homogeneous generalized cylinder uniquely. Next, we show that the sign and magnitude of the Gaussian curvature at a point varies among members of a contour-equivalent class. The image contour fails to constrain two parameters required to recover the shape of a generalized cylinder, the 3D axis location and the object tilt. Next, a method for "ruling" straight homogeneous generalized cylinder images is developed. Once the rulings of the image have been recovered, we show that all parameters derivable from contour alone can be recovered. To recover the two remaining parameters (modulo scale) not constrained by image contour requires incorporating additional information into the recovery process, e.g. intensity information. We derive a method for recovering the tilt of the object using the ruled contour image and intensity values along cross-sectional geodesics. In addition, we derive a method for recovering the location of the object's 3D axis from intensity values along meridians of the surface. Using the different methods outlined in this paper constitutes an algorithm for recovering all the shape parameters (modulo scale) of a straight homogeneous generalized cylinder

Impacts of traditional food consumption advisories: Compliance, changes in diet and loss of confidence in traditional foods

<p>Abstract</p> <p>Background</p> <p>Food consumption advisories are often posted when industrial activities are expected to affect the quality and availability of traditional foods used by First Nations. We were recently involved in a project and asked to summarize details regarding the impacts of traditional food consumption advisories with respect to compliance, broader changes in diet and loss of confidence in traditional foods by people.</p> <p>Methods</p> <p>Our review was not conducted as a formal systematic comprehensive review; rather, we focused on primary and grey literature presenting academic, health practitioner and First Nations viewpoints on the topic available from literature databases (i.e., PubMed, Web of Knowledge^SM) as well as the internet search engine Google. Some information came from personal communications.</p> <p>Results</p> <p>Our overview suggests that when communicated effectively and clearly, and when community members are involved in the process, consumption advisories can result in a decrease in contaminant load in people. On the other hand, consumption advisories can lead to cultural loss and have been linked to a certain amount of social, psychological, nutritional, economic and lifestyle disruption. In some cases, communities have decided to ignore consumption advisories opting to continue with traditional lifestyles believing that the benefits of doing so outweigh the risk of following advisories.</p> <p>Conclusions</p> <p>We identified that there are both positive and negative aspects to the issuance of traditional food consumption advisories. A number of variables need to be recognized during the development and implementation of advisories in order to ensure a balance between human health, maintenance of cultures and industrial activity.</p

Hunger among Inuit children in Canada

Background and objectives. Inuit populations may be at increased risk for experiencing poor nutrition or hunger due to limited access and availability to food. The prevalence and correlates of parental perceptions of hunger among a nationally representative sample of Inuit children in Canada have not yet been reported. Design. Data are from the 2006 Aboriginal Children&#x0027;s Survey (ACS). Sociodemographic information, dietary behaviours and hunger status were parent-reported via a household interview for Inuit children aged 2&#x2013;5 years (n=1,234). Prevalence of hunger was calculated among Inuit children by sociodemographic factors and by dietary behaviours. In addition, a multivariate logistic regression model was conducted to determine factors associated with parental perception of ever experiencing hunger. Results. The prevalence of Inuit children in Canada aged 2&#x2013;5 years ever experiencing hunger was 24.4%. Children who were reported to have experienced hunger consumed milk and milk products (p&#60;0.001); fish, eggs and meat (p&#60;0.05); fruits (p&#60;0.001); and vegetables (p&#60;0.001) significantly less often than never-hungry children. Fast food and processed foods, soft drinks and juice, and salty snacks, sweets and desserts were consumed as often as never-hungry children (all p&#x003E;0.05). The majority (81%) of Inuit parents/guardians of ever-hungry children sought help from family or friends. Factors associated with an increased likelihood of experiencing hunger include sociodemographic characteristics (such as income and household size), living in an Inuit region and living in a community with cultural activities. Conclusion. About 1 in 4 Inuit children were reported by their parents to have experienced hunger, and hunger was associated with region, sociodemographic and community factors. Future research could further examine the impact of ever experiencing hunger on the health status of Inuit children and their families in Canada

Wittgensteinian Anti-Scepticism and Epistemic Vertigo

status: publishe