81 research outputs found
Interpreting line drawings of curved objects,”
Abstract In this paper, we study the problem of interpreting line drawings of scenes composed of opaque regular solid objects bounded by piecewise smooth surfaces with no markings or texture on them. It is assumed that the line drawing has been formed by orthographic projection of such a scene under general viewpoint, that the line drawing is error free, and that there are no lines due to shadows or specularities. Our definition implicitly excludes laminae, wires, and the apices of cones. A major component of the interpretation of line drawings is line labelling. By line labelling we mean (a) classification of each image curve as corresponding to either a depth or orientation discontinuity in the scene, and (b) further subclassification of each kind of discontinuity. For a depth discontinuity we determine whether it is a limb-a locus of points on the surface where the line of sight is tangent to the surface-or an occluding edge-a tangent plane discontinuity of the surface. For an orientation discontinuity, we determine whether it corresponds to a convex or concave edge. This paper presents the first mathematically rigorous scheme for labelling line drawings of the class of scenes described. Previous schemes for labelling line drawings of scenes containing curved objects were heuristic, incomplete, and lacked proper mathematical justification. By analyzing the projection of the neighborhoods of different kinds of points on a piecewise smooth surface, we are able to catalog all local labelling possibilities for the different types of junctions in a line drawing. An algorithm is developed which utilizes this catalog to determine all legal labellings of the line drawing. A local minimum complexity rule-at each vertex select those labellings which correspond to the minimum number of faces meeting at the vertex-is used in order to prune highly counter-intuitive interpretations. The labelling scheme was implemented and tested on a number of line drawings. The labellings obtained are few and by and large in accordance with human interpretations
A feature-based reverse engineering system using artificial neural networks
Reverse Engineering (RE) is the process of reconstructing CAD models from
scanned data of a physical part acquired using 3D scanners. RE has attracted a
great deal of research interest over the last decade. However, a review of the
literature reveals that most research work have focused on creation of free form
surfaces from point cloud data. Representing geometry in terms of surface patches
is adequate to represent positional information, but can not capture any of the
higher level structure of the part. Reconstructing solid models is of importance
since the resulting solid models can be directly imported into commercial solid
modellers for various manufacturing activities such as process planning, integral
property computation, assembly analysis, and other applications.
This research discusses the novel methodology of extracting geometric features
directly from a data set of 3D scanned points, which utilises the concepts of
artificial neural networks (ANNs). In order to design and develop a generic
feature-based RE system for prismatic parts, the following five main tasks were
investigated. (1) point data processing algorithms; (2) edge detection strategies;
(3) a feature recogniser using ANNs; (4) a feature extraction module; (5) a CAD
model exchanger into other CAD/CAM systems via IGES.
A key feature of this research is the incorporation of ANN in feature recognition.
The use of ANN approach has enabled the development of a flexible feature-based
RE methodology that can be trained to deal with new features. ANNs
require parallel input patterns. In this research, four geometric attributes extracted
from a point set are input to the ANN module for feature recognition: chain codes,
convex/concave, circular/rectangular and open/closed attribute. Recognising each
feature requires the determination of these attributes. New and robust algorithms
are developed for determining these attributes for each of the features.
This feature-based approach currently focuses on solving the feature recognition
problem based on 2.5D shapes such as block pocket, step, slot, hole, and boss,
which are common and crucial in mechanical engineering products. This approach
is validated using a set of industrial components. The test results show that the
strategy for recognising features is reliable
Mixed-integer Nonlinear Optimization: a hatchery for modern mathematics
The second MFO Oberwolfach Workshop on Mixed-Integer Nonlinear Programming (MINLP) took place between 2nd and 8th June 2019. MINLP refers to one of the hardest Mathematical Programming (MP) problem classes, involving both nonlinear functions as well as continuous and integer decision variables. MP is a formal language for describing optimization problems, and is traditionally part of Operations Research (OR), which is itself at the intersection of mathematics, computer science, engineering and econometrics. The scientific program has covered the three announced areas (hierarchies of approximation, mixed-integer nonlinear optimal control, and dealing with uncertainties) with a variety of tutorials, talks, short research announcements, and a special "open problems'' session
3D object reconstruction using computer vision : reconstruction and characterization applications for external human anatomical structures
Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201
Developing & tailoring multi-functional carbon foams for multi-field response
As technological advances occur, many conventional materials are incapable of providing the unique multi-functional characteristics demanded thus driving an accelerated focus to create new material systems such as carbon and graphite foams. The improvement of their mechanical stiffness and strength, and tailoring of thermal and electrical conductivities are two areas of multi-functionality with active interest and investment by researchers. The present research focuses on developing models to facilitate and assess multi-functional carbon foams in an effort to expand knowledge. The foundation of the models relies on a unique approach to finite element meshing which captures the morphology of carbon foams. The developed models also include ligament anisotropy and coatings to provide comprehensive information to guide processing researchers in their pursuit of tailorable performance. Several illustrations are undertaken at multiple scales to explore the response of multi-functional carbon foams under coupled field environments providing valuable insight for design engineers in emerging technologies. The illustrations highlight the importance of individual moduli in the anisotropic stiffness matrix as well as the impact of common processing defects when tailoring the bulk stiffness. Furthermore, complete coating coverage and quality interface conditions are critical when utilizing copper to improve thermal and electrical conductivity of carbon foams
Robust computational intelligence techniques for visual information processing
The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training.
Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined.
Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed.
The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola
Object-Aware Tracking and Mapping
Reasoning about geometric properties of digital cameras and optical physics enabled
researchers to build methods that localise cameras in 3D space from a video
stream, while – often simultaneously – constructing a model of the environment.
Related techniques have evolved substantially since the 1980s, leading to increasingly
accurate estimations. Traditionally, however, the quality of results is strongly
affected by the presence of moving objects, incomplete data, or difficult surfaces
– i.e. surfaces that are not Lambertian or lack texture. One insight of this work is
that these problems can be addressed by going beyond geometrical and optical constraints,
in favour of object level and semantic constraints. Incorporating specific
types of prior knowledge in the inference process, such as motion or shape priors,
leads to approaches with distinct advantages and disadvantages.
After introducing relevant concepts in Chapter 1 and Chapter 2, methods for building
object-centric maps in dynamic environments using motion priors are investigated
in Chapter 5. Chapter 6 addresses the same problem as Chapter 5, but presents
an approach which relies on semantic priors rather than motion cues. To fully exploit
semantic information, Chapter 7 discusses the conditioning of shape representations
on prior knowledge and the practical application to monocular, object-aware
reconstruction systems
From surfaces to objects : Recognizing objects using surface information and object models.
This thesis describes research on recognizing partially obscured objects using
surface information like Marr's 2D sketch ([MAR82]) and surface-based geometrical
object models. The goal of the recognition process is to produce a fully
instantiated object hypotheses, with either image evidence for each feature or
explanations for their absence, in terms of self or external occlusion.
The central point of the thesis is that using surface information should be
an important part of the image understanding process. This is because surfaces
are the features that directly link perception to the objects perceived (for
normal "camera-like" sensing) and because surfaces make explicit information
needed to understand and cope with some visual problems (e.g. obscured features).
Further, because surfaces are both the data and model primitive, detailed
recognition can be made both simpler and more complete.
Recognition input is a surface image, which represents surface orientation and
absolute depth. Segmentation criteria are proposed for forming surface patches
with constant curvature character, based on surface shape discontinuities which
become labeled segmentation- boundaries.
Partially obscured object surfaces are reconstructed using stronger surface based
constraints. Surfaces are grouped to form surface clusters, which are 3D
identity-independent solids that often correspond to model primitives. These are
used here as a context within which to select models and find all object features.
True three-dimensional properties of image boundaries, surfaces and surface
clusters are directly estimated using the surface data.
Models are invoked using a network formulation, where individual nodes
represent potential identities for image structures. The links between nodes are
defined by generic and structural relationships. They define indirect evidence relationships
for an identity. Direct evidence for the identities comes from the data
properties. A plausibility computation is defined according to the constraints inherent
in the evidence types. When a node acquires sufficient plausibility, the
model is invoked for the corresponding image structure.Objects are primarily represented using a surface-based geometrical model.
Assemblies are formed from subassemblies and surface primitives, which are
defined using surface shape and boundaries. Variable affixments between assemblies
allow flexibly connected objects.
The initial object reference frame is estimated from model-data surface relationships,
using correspondences suggested by invocation. With the reference
frame, back-facing, tangential, partially self-obscured, totally self-obscured and
fully visible image features are deduced. From these, the oriented model is used
for finding evidence for missing visible model features. IT no evidence is found,
the program attempts to find evidence to justify the features obscured by an unrelated
object. Structured objects are constructed using a hierarchical synthesis
process.
Fully completed hypotheses are verified using both existence and identity
constraints based on surface evidence.
Each of these processes is defined by its computational constraints and are
demonstrated on two test images. These test scenes are interesting because they
contain partially and fully obscured object features, a variety of surface and solid
types and flexibly connected objects. All modeled objects were fully identified
and analyzed to the level represented in their models and were also acceptably
spatially located.
Portions of this work have been reported elsewhere ([FIS83], [FIS85a], [FIS85b],
[FIS86]) by the author
Error Detection and Recovery for Robot Motion Planning with Uncertainty
Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions
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