5 research outputs found
Representations for Cognitive Vision : a Review of Appearance-Based, Spatio-Temporal, and Graph-Based Approaches
The emerging discipline of cognitive vision requires a proper representation of visual information including spatial and temporal relationships, scenes, events, semantics and context. This review article summarizes existing representational schemes in computer vision which might be useful for cognitive vision, a and discusses promising future research directions. The various approaches are categorized according to appearance-based, spatio-temporal, and graph-based representations for cognitive vision. While the representation of objects has been covered extensively in computer vision research, both from a reconstruction as well as from a recognition point of view, cognitive vision will also require new ideas how to represent scenes. We introduce new concepts for scene representations and discuss how these might be efficiently implemented in future cognitive vision systems
Curve-Based Shape Matching Methods and Applications
One of the main cues we use in our everyday life when interacting with the environment is shape.
For example, we use shape information to recognise a chair, grasp a cup, perceive traffic signs and
solve jigsaw puzzles. We also use shape when dealing with more sophisticated tasks, such as the
medical diagnosis of radiographs or the restoration of archaeological artifacts. While the perception
of shape and its use is a natural ability of human beings, endowing machines with such skills is
not straightforward. However, the exploitation of shape cues is important for the development of
competent computer methods that will automatically perform tasks such as those just mentioned.
With this aim, the present work proposes computer methods which use shape to tackle two important
tasks, namely packing and object recognition.
The packing problem arises in a variety of applications in industry, where the placement of a set
of two-dimensional shapes on a surface such that no shapes overlap and the uncovered surface area
is minimised is important. Given that this problem is NP-complete, we propose a heuristic method
which searches for a solution of good quality, though not necessarily the optimal one, within a reasonable
computation time. The proposed method adopts a pictorial representation and employs a greedy
algorithm which uses a shape matching module in order to dynamically select the order and the pose
of the parts to be placed based on the “gaps” appearing in the layout during the execution.
This thesis further investigates shape matching in the context of object recognition and first considers
the case where the target object and the input scene are represented by their silhouettes. Two distinct
methods are proposed; the first method follows a local string matching approach, while the second
one adopts a global optimisation approach using dynamic programming. Their use of silhouettes,
however, rules out the consideration of any internal contours that might appear in the input scene,
and in order to address this limitation, we later propose a graph-based scheme that performs shape
matching incorporating information from both internal and external contours. Finally, we lift the assumption
made that input data are available in the form of closed curves, and present a method which
can robustly perform object recognition using curve fragments (edges) as input evidence. Experiments
conducted with synthetic and real images, involving rigid and deformable objects, show the
robustness of the proposed methods with respect to geometrical transformations, heavy clutter and
substantial occlusion
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Contour and texture for visual recognition of object categories
The recognition of categories of objects in images has become a central
topic in computer vision. Automatic visual recognition systems
are rapidly becoming central to applications such as image search,
robotics, vehicle safety systems, and image editing. This work addresses
three sub-problems of recognition: image classification, object
detection, and semantic segmentation. The task of classification
is to determine whether an object of a particular category is present
or not. Object detection aims to localize any objects of the category.
Semantic segmentation is a more complete image understanding,
whereby an image is partitioned into coherent regions that are assigned
meaningful class labels. This thesis proposes novel discriminative
learning approaches to these problems.
Our primary contributions are threefold. Firstly, we demonstrate
that the contours (the outline and interior edges) of an object are,
alone, sufficient for accurate visual recognition. Secondly, we propose
two powerful new feature types: (i) a learned codebook of contour
fragments matched with an improved oriented chamfer distance,
and (ii) a set of texture-based features that simultaneously exploit
local appearance, approximate shape, and appearance context.
The efficacy of these new features types is evaluated on a wide variety
of datasets. Thirdly, we show how, in combination, these two
largely orthogonal feature types can substantially improve recognition
performance above that achieved by either alone