26,672 research outputs found
SIFTing the relevant from the irrelevant: Automatically detecting objects in training images
Many state-of-the-art object recognition systems rely on identifying the location of objects in images, in order to better learn its visual attributes. In this paper, we propose four simple yet powerful hybrid ROI detection methods (combining both local and global features), based on frequently occurring keypoints. We show that our methods demonstrate competitive performance in two different types of datasets, the Caltech101 dataset and the GRAZ-02 dataset, where the pairs of keypoint bounding box method achieved the best accuracies overall
MKtree: generation and simulations
The problem to represent very complex systems has been studied by
several authors, obtaining solutions based on different data
structures. In this paper, a K dimensional tree
(Multirresolution Kdtree, MKtree) is introduced. The MKtree represents
a hierarchical subdivision of the scene objects that guarantees a
minimum space overlap between node regions. MKtrees are useful for
collision detection and for time-critical rendering in very large
environments requiring external memory storage. Examples in ship
design applications are described.Postprint (published version
Hierarchical bounding structures for efficient virial computations: Towards a realistic molecular description of cholesterics
We detail the application of bounding volume hierarchies to accelerate
second-virial evaluations for arbitrary complex particles interacting through
hard and soft finite-range potentials. This procedure, based on the
construction of neighbour lists through the combined use of recursive
atom-decomposition techniques and binary overlap search schemes, is shown to
scale sub-logarithmically with particle resolution in the case of molecular
systems with high aspect ratios. Its implementation within an efficient
numerical and theoretical framework based on classical density functional
theory enables us to investigate the cholesteric self-assembly of a wide range
of experimentally-relevant particle models. We illustrate the method through
the determination of the cholesteric behaviour of hard, structurally-resolved
twisted cuboids, and report quantitative evidence of the long-predicted phase
handedness inversion with increasing particle thread angles near the
phenomenological threshold value of . Our results further highlight
the complex relationship between microscopic structure and helical twisting
power in such model systems, which may be attributed to subtle geometric
variations of their chiral excluded-volume manifold
A computational model of the referential semantics of projective prepositions
In this paper we present a framework for interpreting locative expressions containing the prepositions in front of and behind. These prepositions have different semantics in the viewer-centred and intrinsic frames of reference (Vandeloise, 1991). We define a model of their semantics in each frame of reference. The basis of these models is a novel parameterized continuum function that creates a 3-D spatial template. In the intrinsic frame of reference the origin used by the continuum function is assumed to be known a priori and object occlusion does not impact on the applicability rating of a point in the spatial template. In the viewer-centred frame the location of the spatial template’s origin is dependent on the user’s perception of the landmark at the time of the utterance and object
occlusion is integrated into the model. Where there is an ambiguity with respect to the intended frame of reference, we define an algorithm for merging the spatial templates from the competing frames of reference, based on psycholinguistic observations in (Carlson-Radvansky, 1997)
Rotation-invariant features for multi-oriented text detection in natural images.
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes
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