Process grammar and process history for 2D objects

This project is the written report for the course in Picture Processing at the Department of Computer Science, Aarhus University. The starting point is a paper by Michael Leyton in Artificial Intelligence 34, 1988: "A process grammar for shape". The paper describes how it is possible to derive the process history for an object from its state at two stages in its development. The aim of this project is to describe and test an algorithm for doing so

Disconnected Skeleton: Shape at its Absolute Scale

We present a new skeletal representation along with a matching framework to address the deformable shape recognition problem. The disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale. Our motivation is to rely on the stable properties of the shape instead of inaccurately measured secondary details. The new representation does not suffer from the common instability problems of traditional connected skeletons, and the matching process gives quite successful results on a diverse database of 2D shapes. An important difference of our approach from the conventional use of the skeleton is that we replace the local coordinate frame with a global Euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations. As a result, we can produce descriptions that are sensitive to any combination of changes in scale, position, orientation and articulation, as well as invariant ones.Comment: The work excluding {\S}V and {\S}VI has first appeared in 2005 ICCV: Aslan, C., Tari, S.: An Axis-Based Representation for Recognition. In ICCV(2005) 1339- 1346.; Aslan, C., : Disconnected Skeletons for Shape Recognition. Masters thesis, Department of Computer Engineering, Middle East Technical University, May 200

Mental Structures

An ongoing philosophical discussion concerns how various types of mental states fall within broad representational genera—for example, whether perceptual states are “iconic” or “sentential,” “analog” or “digital,” and so on. Here, I examine the grounds for making much more specific claims about how mental states are structured from constituent parts. For example, the state I am in when I perceive the shape of a mountain ridge may have as constituent parts my representations of the shapes of each peak and saddle of the ridge. More specific structural claims of this sort are a guide to how mental states fall within broader representational kinds. Moreover, these claims have significant implications of their own about semantic, functional, and epistemic features of our mental lives. But what are the conditions on a mental state's having one type of constituent structure rather than another? Drawing on explanatory strategies in vision science, I argue that, other things being equal, the constituent structure of a mental state determines what I call its distributional properties—namely, how mental states of that type can, cannot, or must co‐occur with other mental states in a given system. Distributional properties depend critically on and are informative about the underlying structures of mental states, they abstract in important ways from aspects of how mental states are processed, and they can yield significant insights into the variegation of psychological capacities

Relational Graph Representation Learning for Predicting Object Affordances

We address the problem of affordance classification for class-agnostic objects considering an open set of actions, by unsupervised learning of object interactions,inducing object affordance classes. A novel qualitative spatial representation incorporating depth information is used to construct Activity Graphs which encode object interactions. These Activity Graphs are clustered to obtain interaction classes, and subsequently extract classes of object affordances. Our experiments demonstrate that our method learns object affordances without being scene- or object-specific

Towards a Qualitative Reasoning on Shape Change and Object Division

We propose a qualitative representation for handling shape change and object division. We model the shape of a smooth curve in a two-dimensional plane together with its temporal change, using curvature extrema. The representation is based on Process-Grammar, which gives a causal account for each shape change. We introduce several rewriting rules to handle object division, that consist of making a tangent point, reconstruction, and separation. On the treatment of the division process, the expression can clarify the relative locations of multiple objects. We show formalization and application to represent a sequence of shape changes frequently observed in an organogenesis process

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

2D qualitative shape matching applied to ceramic mosaic assembly

A theory of shape recognition of 2D objects and its application in the ceramic industry for intelligent automation of the mosaic mural assembly process are presented in this paper. This theory qualitatively describes the shapes of the objects, considering: (i) shape boundary characteristics, such as angles, relative length, concavities, and curvature; and (ii) their color and size. The shapes to be recognized may be regular or irregular closed polygons, or closed curvilinear figures. Each figure is described as a symbolic character string that contains all its distinctive characteristics. This description is used to determine whether the shape of two figures matches. Then, given a design of a mosaic and given a set of physical ceramic tesserae, an application is developed in order to recognize the tesserae that form the mosaic, thus enabling the intelligent and automated assembly of ceramic mosaics

Swept regions and surfaces: Modeling and volumetric properties

We consider “swept regions” and “swept hypersurfaces” in (and especially ) which are a disjoint union of subspaces or obtained from a varying family of affine subspaces . We concentrate on the case where and are obtained from a skeletal structure . This generalizes the Blum medial axis of a region , which consists of the centers of interior spheres tangent to the boundary at two or more points, with denoting the vectors from the centers of the spheres to the points of tangency. We extend methods developed for skeletal structures so that they can be deduced from the properties of the individual intersections or and a relative shape operator , which we introduce to capture changes relative to the varying family . We use these results to deduce modeling properties of the global in terms of the individual , and determine volumetric properties of regions expressed as global integrals of functions on in terms of iterated integrals over the skeletal structure of which is then integrated over the parameter space

Object Recognition in 3D data using Capsules

The proliferation of 3D sensors induced 3D computer vision research for many application areas including virtual reality, autonomous navigation and surveillance. Recently, dierent methods have been proposed for 3D object classication. Many of the existing 2D and 3D classication methods rely on convolutional neural networks (CNNs), which are very successful in extracting features from the data. However, CNNs cannot address the spatial relationship between features due to the max-pooling layers, and they require vast amount of data for training. In this work, we propose a model architecture for 3D object classication, which is an extension of Capsule Networks (CapsNets) to 3D data. Our proposed architecture called 3D CapsNet, takes advantage of the fact that a CapsNet preserves the orientation and spatial relationship of the extracted features, and thus requires less data to train the network. We use ModelNet database, a comprehensive clean collection of 3D CAD models for objects, to train and test the 3D CapsNet model. We then compare our approach with ShapeNet, a deep belief network for object classication based on CNNs, and show that our method provides performance improvement especially when training data size gets smaller