171 research outputs found
A Kind of Affine Weighted Moment Invariants
A new kind of geometric invariants is proposed in this paper, which is called
affine weighted moment invariant (AWMI). By combination of local affine
differential invariants and a framework of global integral, they can more
effectively extract features of images and help to increase the number of
low-order invariants and to decrease the calculating cost. The experimental
results show that AWMIs have good stability and distinguishability and achieve
better results in image retrieval than traditional moment invariants. An
extension to 3D is straightforward
Collaborative Computation in Self-Organizing Particle Systems
Many forms of programmable matter have been proposed for various tasks. We
use an abstract model of self-organizing particle systems for programmable
matter which could be used for a variety of applications, including smart paint
and coating materials for engineering or programmable cells for medical uses.
Previous research using this model has focused on shape formation and other
spatial configuration problems (e.g., coating and compression). In this work we
study foundational computational tasks that exceed the capabilities of the
individual constant size memory of a particle, such as implementing a counter
and matrix-vector multiplication. These tasks represent new ways to use these
self-organizing systems, which, in conjunction with previous shape and
configuration work, make the systems useful for a wider variety of tasks. They
can also leverage the distributed and dynamic nature of the self-organizing
system to be more efficient and adaptable than on traditional linear computing
hardware. Finally, we demonstrate applications of similar types of computations
with self-organizing systems to image processing, with implementations of image
color transformation and edge detection algorithms
On the surplus value of semantic video analysis beyond the key frame
Typical semantic video analysis methods aim for classification of camera shots based on extracted features from a single key frame only. In this paper, we sketch a video analysis scenario and evaluate the benefit of analysis beyond the key frame for semantic concept detection performance. We developed detectors for a lexicon of 26 concepts, and evaluated their performance on 120 hours of video data. Results show that, on average, detection performance can increase with almost 40 % when the analysis method takes more visual content into account. 1
Adaptive Seeding for Gaussian Mixture Models
We present new initialization methods for the expectation-maximization
algorithm for multivariate Gaussian mixture models. Our methods are adaptions
of the well-known -means++ initialization and the Gonzalez algorithm.
Thereby we aim to close the gap between simple random, e.g. uniform, and
complex methods, that crucially depend on the right choice of hyperparameters.
Our extensive experiments indicate the usefulness of our methods compared to
common techniques and methods, which e.g. apply the original -means++ and
Gonzalez directly, with respect to artificial as well as real-world data sets.Comment: This is a preprint of a paper that has been accepted for publication
in the Proceedings of the 20th Pacific Asia Conference on Knowledge Discovery
and Data Mining (PAKDD) 2016. The final publication is available at
link.springer.com (http://link.springer.com/chapter/10.1007/978-3-319-31750-2
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A Family of Maximum Margin Criterion for Adaptive Learning
In recent years, pattern analysis plays an important role in data mining and
recognition, and many variants have been proposed to handle complicated
scenarios. In the literature, it has been quite familiar with high
dimensionality of data samples, but either such characteristics or large data
have become usual sense in real-world applications. In this work, an improved
maximum margin criterion (MMC) method is introduced firstly. With the new
definition of MMC, several variants of MMC, including random MMC, layered MMC,
2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the
MMC network is developed to learn deep features of images in light of simple
deep networks. Experimental results on a diversity of data sets demonstrate the
discriminant ability of proposed MMC methods are compenent to be adopted in
complicated application scenarios.Comment: 14 page
Constant force muscle stretching induces greater acute deformations and changes in passive mechanical properties compared to constant length stretching
Stretching is applied to lengthen shortened muscles in pathological conditions such as joint contractures. We investigated (i) the acute effects of different types of stretching, i.e. constant length (CL) and constant force (CF) stretching, on acute deformations and changes in passive mechanical properties of medial gastrocnemius muscle (MG) and (ii) the association of acute muscle–tendon deformations or changes in mechanical properties with the impulse or maximal strain of stretching. Forty-eight hindlimbs from 13 male and 12 female Wistar rats (13 weeks old, respectively 424.6 ± 35.5 and 261.8 ± 15.6 g) were divided into six groups (n = 8 each). The MG was initially stretched to a length at which the force was 75%, 95%, or 115% of the force corresponding to estimated maximal dorsiflexion and held at either CF or CL for 30 min. Before and after the stretching protocol, the MG peak force and peak stiffness were assessed by lengthening the passive muscle to the length corresponding to maximal ankle dorsiflexion. Also, the muscle belly length and tendon length were measured. CF stretching affected peak force, peak stiffness, muscle belly length, and tendon length more than CL stretching (p < 0.01). Impulse was associated only with the decrease in peak force, while maximal strain was associated with the decrease in peak force, peak stiffness, and the increase in muscle belly length. We conclude that CF stretching results in greater acute deformations and changes in mechanical properties than CL stretching, which appears to be dependent predominantly on the differences in imposed maximal strain.</p
Validating the detection of everyday concepts in visual lifelogs
The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user's day-to-day activities. It can capture up to 3,000 images per day, equating to almost 1 million images per year. It is used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer's life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the novel domain of visual lifelogs. A concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept's presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were then evaluated on a subset of 95,907 images, to determine the precision for detection of each semantic concept and to draw some interesting inferences on the lifestyles of those 5 users. We additionally present future applications of concept detection within the domain of lifelogging. © 2008 Springer Berlin Heidelberg
An Image Statistics–Based Model for Fixation Prediction
The problem of predicting where people look at, or equivalently salient region detection, has been related to the statistics of several types of low-level image features. Among these features, contrast and edge information seem to have the highest correlation with the fixation locations. The contrast distribution of natural images can be adequately characterized using a two-parameter Weibull distribution. This distribution catches the structure of local contrast and edge frequency in a highly meaningful way. We exploit these observations and investigate whether the parameters of the Weibull distribution constitute a simple model for predicting where people fixate when viewing natural images. Using a set of images with associated eye movements, we assess the joint distribution of the Weibull parameters at fixated and non-fixated regions. Then, we build a simple classifier based on the log-likelihood ratio between these two joint distributions. Our results show that as few as two values per image region are already enough to achieve a performance comparable with the state-of-the-art in bottom-up saliency prediction
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