2D parallel thinning and shrinking based on sufficient conditions for topology preservation

Thinning and shrinking algorithms, respectively, are capable of extracting medial lines and topological kernels from digital binary objects in a topology preserving way. These topological algorithms are composed of reduction operations: object points that satisfy some topological and geometrical constraints are removed until stability is reached. In this work we present some new sufficient conditions for topology preserving parallel reductions and fiftyfour new 2D parallel thinning and shrinking algorithms that are based on our conditions. The proposed thinning algorithms use five characterizations of endpoints

Building geometric models with hand-drawn sketches

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1998.Includes bibliographical references (p. 49-51).Architects work on drawings and models, not buildings. Today, in many architectural practices, drawings and models are produced in digital format using Computer-aided Design (CAD) tools. Unquestionably, digital media have changed the way in which many architects perform their day to day activities. But these changes have been limited to the more prosaic aspects of practice. To be sure, CAD systems have made the daily operations of many design offices more efficient; nevertheless, they have been of little use - and indeed are often a hindrance - in situations where the task at hand is more conjectural and speculative in nature, as it is during the early stages of a project. Well-intentioned efforts to insinuate CAD into these aspects of practice have only served to reveal the incongruities between the demands of designer and the configuration of the available tools. One of the chief attributes of design practice is that it is action performed at a distance through the agency of representations. This fundamental trait implies that we have to understand how computers help architects describe buildings if we are to understand how they might help architects design buildings. As obvious as this claim might seem, CAD programs can be almost universally characterized by a tacit denigration of visual representation. In this thesis, I examine properties of design drawings that make them useful to architects. I go on to describe a computer program that I have written that allows a designer to build geometric models using freehand sketches. This program illustrates that it is possible to design a software tool in a way that profits from, rather than negates, the power of visual representations.by Ewan E. Branda.M.S

Sensory processing and world modeling for an active ranging device

In this project, we studied world modeling and sensory processing for laser range data. World Model data representation and operation were defined. Sensory processing algorithms for point processing and linear feature detection were designed and implemented. The interface between world modeling and sensory processing in the Servo and Primitive levels was investigated and implemented. In the primitive level, linear features detectors for edges were also implemented, analyzed and compared. The existing world model representations is surveyed. Also presented is the design and implementation of the Y-frame model, a hierarchical world model. The interfaces between the world model module and the sensory processing module are discussed as well as the linear feature detectors that were designed and implemented

Single-Phase Flow of Non-Newtonian Fluids in Porous Media

The study of flow of non-Newtonian fluids in porous media is very important and serves a wide variety of practical applications in processes such as enhanced oil recovery from underground reservoirs, filtration of polymer solutions and soil remediation through the removal of liquid pollutants. These fluids occur in diverse natural and synthetic forms and can be regarded as the rule rather than the exception. They show very complex strain and time dependent behavior and may have initial yield-stress. Their common feature is that they do not obey the simple Newtonian relation of proportionality between stress and rate of deformation. Non-Newtonian fluids are generally classified into three main categories: time-independent whose strain rate solely depends on the instantaneous stress, time-dependent whose strain rate is a function of both magnitude and duration of the applied stress and viscoelastic which shows partial elastic recovery on removal of the deforming stress and usually demonstrates both time and strain dependency. In this article the key aspects of these fluids are reviewed with particular emphasis on single-phase flow through porous media. The four main approaches for describing the flow in porous media are examined and assessed. These are: continuum models, bundle of tubes models, numerical methods and pore-scale network modeling.Comment: 94 pages, 12 figures, 1 tabl

Application of general semi-infinite Programming to Lapidary Cutting Problems

We consider a volume maximization problem arising in gemstone cutting industry. The problem is formulated as a general semi-infinite program (GSIP) and solved using an interiorpoint method developed by Stein. It is shown, that the convexity assumption needed for the convergence of the algorithm can be satisfied by appropriate modelling. Clustering techniques are used to reduce the number of container constraints, which is necessary to make the subproblems practically tractable. An iterative process consisting of GSIP optimization and adaptive refinement steps is then employed to obtain an optimal solution which is also feasible for the original problem. Some numerical results based on realworld data are also presented

Automatic plant features recognition using stereo vision for crop monitoring

Machine vision and robotic technologies have potential to accurately monitor plant parameters which reflect plant stress and water requirements, for use in farm management decisions. However, autonomous identification of individual plant leaves on a growing plant under natural conditions is a challenging task for vision-guided agricultural robots, due to the complexity of data relating to various stage of growth and ambient environmental conditions. There are numerous machine vision studies that are concerned with describing the shape of leaves that are individually-presented to a camera. The purpose of these studies is to identify plant species, or for the autonomous detection of multiple leaves from small seedlings under greenhouse conditions. Machine vision-based detection of individual leaves and challenges presented by overlapping leaves on a developed plant canopy using depth perception properties under natural outdoor conditions is yet to be reported. Stereo vision has recently emerged for use in a variety of agricultural applications and is expected to provide an accurate method for plant segmentation and identification which can benefit from depth properties and robustness. This thesis presents a plant leaf extraction algorithm using a stereo vision sensor. This algorithm is used on multiple leaf segmentation and overlapping leaves separation using a combination of image features, specifically colour, shape and depth. The separation between the connected and the overlapping leaves relies on the measurement of the discontinuity in depth gradient for the disparity maps. Two techniques have been developed to implement this task based on global and local measurement. A geometrical plane from each segmented leaf can be extracted and used to parameterise a 3D model of the plant image and to measure the inclination angle of each individual leaf. The stem and branch segmentation and counting method was developed based on the vesselness measure and Hough transform technique. Furthermore, a method for reconstructing the segmented parts of hibiscus plants is presented and a 2.5D model is generated for the plant. Experimental tests were conducted with two different selected plants: cotton of different sizes, and hibiscus, in an outdoor environment under varying light conditions. The proposed algorithm was evaluated using 272 cotton and hibiscus plant images. The results show an observed enhancement in leaf detection when utilising depth features, where many leaves in various positions and shapes (single, touching and overlapping) were detected successfully. Depth properties were more effective in separating between occluded and overlapping leaves with a high separation rate of 84% and these can be detected automatically without adding any artificial tags on the leaf boundaries. The results exhibit an acceptable segmentation rate of 78% for individual plant leaves thereby differentiating the leaves from their complex backgrounds and from each other. The results present almost identical performance for both species under various lighting and environmental conditions. For the stem and branch detection algorithm, experimental tests were conducted on 64 colour images of both species under different environmental conditions. The results show higher stem and branch segmentation rates for hibiscus indoor images (82%) compared to hibiscus outdoor images (49.5%) and cotton images (21%). The segmentation and counting of plant features could provide accurate estimation about plant growth parameters which can be beneficial for many agricultural tasks and applications