9 research outputs found
Point-based medialness for 2D shape description and identification
Abstract We propose a perception-based medial point description of a natural form (2D: static or in articulated movement) as a framework for a shape representation which can then be efficiently used in biological species identification and matching tasks. Medialness is defined by adapting and refining a definition first proposed in the cognitive science literature when studying the visual attention of human subjects presented with articulated biological 2D forms in movement, such as horses, dogs and humans (walking, running). In particular, special loci of high medialness for the interior of a form in movement, referred to as âhot spotsâ, prove most attractive to the human perceptual system. We propose an algorithmic process to identify such hot spots. In this article we distinguish exterior from interior shape representation. We further augment hot spots with extremities of medialness ridges identifying significant concavities (from outside) and convexities (from inside). Our representation is strongly footed in results from cognitive psychology, but also inspired by know-how in art and animation, and the algorithmic part is influenced by techniques from more traditional computer vision. A robust shape matching algorithm is designed that finds the most relevant targets from a database of templates by comparing feature points in a scale, rotation and translation invariant way. The performance of our method has been tested on several databases. The robustness of the algorithm is further tested by perturbing the data-set at different levels
Medialness and the Perception of Visual Art
In this article we explore the practical use of medialness informed by perception studies as a representation and processing layer for describing a class of works of visual art. Our focus is towards the description of 2D objects in visual art, such as found in drawings, paintings, calligraphy, graffiti writing, where approximate boundaries or lines delimit regions associated to recognizable objects or their constitutive parts. We motivate this exploration on the one hand by considering how ideas emerging from the visual arts, cartoon animation and general drawing practice point towards the likely importance of medialness in guiding the interaction of the traditionally trained artist with the artifact. On the other hand, we also consider recent studies and results in cognitive science which point in similar directions in emphasizing the likely importance of medialness, an extension of the abstract mathematical representation known as âmedial axisâ or âVoronoi graphsâ, as a core feature used by humans in perceiving shapes in static or dynamic scenarios.We illustrate the use of medialness in computations performed with finished artworks as well as artworks in the process of being created, modified, or evolved through iterations. Such computations may be used to guide an artificial arm in duplicating the human creative performance or used to study in greater depth the finished artworks. Our implementations represent a prototyping of such applications of computing to art analysis and creation and remain exploratory. Our method also provides a possible framework to compare similar artworks or to study iterations in the process of producing a final preferred depiction, as selected by the artist
Point-based Medialness for Animal and Plant Identification
We introduce the idea of using a perception-based medial point description [9] of a natural form (2D static or in movement) as a framework for a part-based shape representation which can then be efficiently used in biological species identification and matching tasks. The first step is one of fuzzy medialness measurements of 2D segmented objects from intensity images which emphasises main shape information characteristics of an objectâs parts (e.g. concavities and folds along a contour). We distinguish interior from exterior shape description. Interior medialness is used to characterise deformations from straightness, corners and necks, while exterior medialness identifies the main concavities and inlands which are useful to verify parts extent and reason about articulation and movement. In a second step we identify a set of characteristic features points built from three types. We define (i) an Interior dominant point as a well localised peak value in medialness representation, while (ii) an exterior dominant point is evaluated by identifying a region of concavity sub-tended by a minimum angular support. Furthermore, (iii) convex point are extracted from the form to further characterise the elongation of parts. Our evaluated feature points, together are sufficiently invariant to shape move ment, where the articulation in moving objects are characterised by
exterior dominant points. In the third step, a robust shape matching algorithm is designed that finds the most relevant targets from a database of templates by comparing the dominant feature points in a scale, rotation and translation invariant way (inspired by the SIFT method [17]). The performance of our method has been tested on several databases. The robustness of the algorithm is further tested by perturbing the data-set at different scales
Movement description and gesture recognition for live media arts
The research aims to develop novel techniques able to recognise different sequential gestures, to the level where they will describe and compute articulated movements in real time. In the context of live media arts, the research outcomes would change the paradigm of creating, learning, performing, designing for live media arts, by giving feedback on performance after analysing, in real time, the streaming video of the performance
Point-based Medialness for Animal and Plant Identification
Abstract We introduce the idea of using a perception-based medial point description [#kovacs1998medial] of a natural form (2D static or in movement) as a framework for a part-based shape representation which can then be efficiently used in biological species identification and matching tasks. The first step is one of fuzzy medialness measurements of 2D segmented objects from intensity images which emphasises main shape information characteristics of an object's parts (e.g. concavities and folds along a contour). We distinguish interior from exterior shape description. Interior medialness is used to characterise deformations from straightness, corners and necks, while exterior medialness identifies the main concavities and inlands which are useful to verify parts extent and reason about articulation and movement. In a second step we identify a set of characteristic features points built from three types. We define (i) an Interior dominant point as a well localised peak value in medialness representation, while (ii) an exterior dominant point is evaluated by identifying a region of concavity sub-tended by a minimum angular support. Furthermore, (iii) convex point are extracted from the form to further characterise the elongation of parts. Our evaluated feature points, together are sufficiently invariant to shape movement, where the articulation in moving objects are characterised by exterior dominant points. In the third step, a robust shape matching algorithm is designed that finds the most relevant targets from a database of templates by comparing the dominant feature points in a scale, rotation and translation invariant way (inspired by the SIFT method [#lowe2004distinctive]). The performance of our method has been tested on several databases. The robustness of the algorithm is further tested by perturbing the data-set at different scales
Point-Based Medialness for Movement Computing
We introduce the idea of using a perception-based medial point
description of a biological form (such as a 2D profile of a moving animal)
as a basis for movement computing which delivers computational
schemes to automatically annotate movement and be capable of producing
meaningful qualitative descriptions. We distinguish interior
from exterior shape representation. Interior
medialness is used to characterise deformations from straightness,
corners and necks, while exterior medialness identifies the main
concavities and inlands which are useful to verify parts extent and
reason about articulation and movement. We define an interior dominant
point as a well localised peak value in medialness representation,
while an exterior dominant point is evaluated by identifying a region
of concavity sub-tended by a minimum angular support. Furthermore,
significant convex points are extracted from the object's form to
further characterise the elongation of parts. We propose that our
evaluated feature points are sufficiently representative, as a basis
for shape characterisation, to address many of the goals of movement
computing
Swarm-based identification of animation key points from 2D-medialness maps
In this article we present the use of dispersive flies optimisation (DFO) for swarms of particles active on a medialness map â a 2D field representation of shape informed by perception studies. Optimising swarms activity permits to efficiently identify shape-based keypoints to automatically annotate movement and is capable of producing meaningful qualitative descriptions for animation applications. When taken together as a set, these keypoints represent the full body pose of a character in each processed frame. In addition, such keypoints can be used to embody the notion of the Line of Action (LoA), a well known classic technique from the Disney studios used to capture the overall pose of a character to be fleshed out. Keypoints along a medialness ridge are local peaks which are efficiently localised using DFO driven swarms. DFO is optimised in a way so that it does not need to scan every image pixel and always tend to converge at these peaks. A series of experimental trials on different animation characters in movement sequences confirms the promising performance of the optimiser over a simpler, currently-in-use brute-force approach
Medialness-based Shape Invariant Feature Transformation
This research is about the perception-based medial point description of a natural form (2D static or in movement) as a generic framework for a part-based shape representation, which can then be efficiently used in biological species identification, as well as more general pattern matching and shape movement tasks. We consider recent studies and results in cognitive science that point in similar directions in emphasizing the likely importance of medialness as a core feature used by humans in perceiving shapes in static or dynamic situations. This leads us to define an algorithmic chain composed of the following main steps. The first step is one of fuzzy medialness measurements of 2D segmented objects from intensity images that emphasizes main shape information characteristic of an object's parts, e.g. concavities and folds along a contour. We distinguish interior from exterior shape description. Interior medialness is used to characterise deformations from straightness, corners and necks, while exterior medialness identifies the main concavities and inlands which are useful to verify parts extent and reason about articulation and movement. The second main step consists on defining a feature descriptor, we call ShIFT: Shape Invariant Feature Transform constructed from our proposed medialnessÂbased discrete set, which permits efficient matching tasks when treating very large databases of images containing various types of 2D objects. Our defined shape descriptor ShIFT basically captures elementary shape cues and hence it is able to characterise any 2D shape. In summary, our shape descriptor is strongly footed in results from cognitive psychology while the algorithmic part is influenced by techniques from more traditional computer vision