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In a motorized vehicle a number of easily measurable signals with frequency components related to the rotational speed of the engine can be found , e.g. , vibrations , electrical system voltage level , and ambient sound . | {
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These signals could potentially be used to estimate the speed and related states of the vehicle . | {
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Unfortunately , such estimates would typically require the relations -LRB- scale factors -RRB- between the frequency components and the speed for different gears to be known . | {
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Consequently , in this article we look at the problem of estimating these gear scale factors from training data consisting only of speed measurements and measurements of the signal in question . | {
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The estimation problem is formulated as a maximum likelihood estimation problem and heuristics is used to find initial values for a numerical evaluation of the estimator . | {
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Finally , a measurement campaign is conducted and the functionality of the estimation method is verified on real data . | {
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LPC based speech coders operating at bit rates below 3.0 kbits/sec are usually associated with buzzy or metallic artefacts in the synthetic speech . | {
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In this paper a new LPC vocoder is presented which splits the LPC excitation into two frequency bands using a variable cutoff frequency . | {
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In doing so the coder 's performance during both mixed voicing speech and speech containing acoustic noise is greatly improved , producing soft natural sounding speech . | {
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The paper also describes new parameter determination and quantisation techniques vital to the operation of this coder at such low bit rates . | {
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We consider a problem of blind source separation from a set of instantaneous linear mixtures , where the mixing matrix is unknown . | {
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It was discovered recently , that exploiting the sparsity of sources in an appropriate representation according to some signal dictionary , dramatically improves the quality of separation . | {
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In this work we use the property of multi scale transforms , such as wavelet or wavelet packets , to decompose signals into sets of local features with various degrees of sparsity . | {
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The performance of the algorithm is verified on noise-free and noisy data . | {
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Experiments with simulated signals , musical sounds and images demonstrate significant improvement of separation quality over previously reported results . | {
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In this paper , we explore multilingual feature-level data sharing via Deep Neural Network -LRB- DNN -RRB- stacked bottleneck features . | {
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Given a set of available source languages , we apply language identification to pick the language most similar to the target language , for more efficient use of multilingual resources . | {
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Our experiments with IARPA-Babel languages show that bottleneck features trained on the most similar source language perform better than those trained on all available source languages . | {
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Further analysis suggests that only data similar to the target language is useful for multilingual training . | {
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This article introduces a bidirectional grammar generation system called feature structure-directed generation , developed for a dialogue translation system . | {
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The system utilizes typed feature structures to control the top-down derivation in a declarative way . | {
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This generation system also uses disjunctive feature structures to reduce the number of copies of the derivation tree . | {
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The grammar for this generator is designed to properly generate the speaker 's intention in a telephone dialogue . | {
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Automatic image annotation is a newly developed and promising technique to provide semantic image retrieval via text descriptions . | {
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It concerns a process of automatically labeling the image contents with a pre-defined set of keywords which are exploited to represent the image semantics . | {
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A Maximum Entropy Model-based approach to the task of automatic image annotation is proposed in this paper . | {
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In the phase of training , a basic visual vocabulary consisting of blob-tokens to describe the image content is generated at first ; then the statistical relationship is modeled between the blob-tokens and keywords by a Maximum Entropy Model constructed from the training set of labeled images . | {
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In the phase of annotation , for an unlabeled image , the most likely associated keywords are predicted in terms of the blob-token set extracted from the given image . | {
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We carried out experiments on a medium-sized image collection with about 5000 images from Corel Photo CDs . | {
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The experimental results demonstrated that the annotation performance of this method outperforms some traditional annotation methods by about 8 % in mean precision , showing a potential of the Maximum Entropy Model in the task of automatic image annotation . | {
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However most of the works found in the literature have focused on identifying and understanding temporal expressions in newswire texts . | {
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In this paper we report our work on anchoring temporal expressions in a novel genre , emails . | {
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The highly under-specified nature of these expressions fits well with our constraint-based representation of time , Time Calculus for Natural Language -LRB- TCNL -RRB- . | {
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We have developed and evaluated a Temporal Expression Anchoror -LRB- TEA -RRB- , and the result shows that it performs significantly better than the baseline , and compares favorably with some of the closely related work . | {
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We address the problem of populating object category detection datasets with dense , per-object 3D reconstructions , bootstrapped from class labels , ground truth figure-ground segmentations and a small set of keypoint annotations . | {
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Our proposed algorithm first estimates camera viewpoint using rigid structure-from-motion , then reconstructs object shapes by optimizing over visual hull proposals guided by loose within-class shape similarity assumptions . | {
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We show that our method is able to produce convincing per-object 3D reconstructions on one of the most challenging existing object-category detection datasets , PASCAL VOC . | {
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Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion . | {
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In particular we focus on methods which represent the human motion model as a triangulated graph . | {
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Previous approaches learned models based just on positions and velocities of the body parts while ignoring their appearance . | {
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Moreover , a heuristic approach was commonly used to obtain translation invariance . | {
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In this paper we suggest an improved approach for learning such models and using them for human motion recognition . | {
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The suggested approach combines multiple cues , i.e. , positions , velocities and appearance into both the learning and detection phases . | {
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Furthermore , we introduce global variables in the model , which can represent global properties such as translation , scale or viewpoint . | {
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The model is learned in an unsupervised manner from un-labelled data . | {
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We show that the suggested hybrid proba-bilistic model -LRB- which combines global variables , like translation , with local variables , like relative positions and appearances of body parts -RRB- , leads to : -LRB- i -RRB- faster convergence of learning phase , -LRB- ii -RRB- robustness to occlusions , and , -LRB- iii... | {
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Factor analysis and principal components analysis can be used to model linear relationships between observed variables and linearly map high-dimensional data to a lower-dimensional hidden space . | {
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We describe a nonlinear generalization of factor analysis , called `` product analy-sis '' , that models the observed variables as a linear combination of products of normally distributed hidden variables . | {
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Just as factor analysis can be viewed as unsupervised linear regression on unobserved , normally distributed hidden variables , product analysis can be viewed as unsupervised linear regression on products of unobserved , normally distributed hidden variables . | {
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The mapping between the data and the hidden space is nonlinear , so we use an approximate variational technique for inference and learning . | {
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Since product analysis is a generalization of factor analysis , product analysis always finds a higher data likelihood than factor analysis . | {
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We give results on pattern recognition and illumination-invariant image clustering . | {
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This paper describes a domain independent strategy for the multimedia articulation of answers elicited by a natural language interface to database query applications . | {
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Multimedia answers include videodisc images and heuristically-produced complete sentences in text or text-to-speech form . | {
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Deictic reference and feedback about the discourse are enabled . | {
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The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality . | {
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The demonstrator embodies an interesting combination of hand-built , symbolic resources and stochastic processes . | {
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We describe both the syntax and semantics of a general propositional language of context , and give a Hilbert style proof system for this language . | {
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A propositional logic of context extends classical propositional logic in two ways . | {
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Image matching is a fundamental problem in Computer Vision . | {
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In the context of feature-based matching , SIFT and its variants have long excelled in a wide array of applications . | {
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However , for ultra-wide baselines , as in the case of aerial images captured under large camera rotations , the appearance variation goes beyond the reach of SIFT and RANSAC . | {
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In this paper we propose a data-driven , deep learning-based approach that sidesteps local correspondence by framing the problem as a classification task . | {
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We train our models on a dataset of urban aerial imagery consisting of ` same ' and ` different ' pairs , collected for this purpose , and characterize the problem via a human study with annotations from Amazon Mechanical Turk . | {
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We demonstrate that our models outperform the state-of-the-art on ultra-wide baseline matching and approach human accuracy . | {
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We argue that a more sophisticated and fine-grained annotation in the tree-bank would have very positve effects on stochastic parsers trained on the tree-bank and on grammars induced from the treebank , and it would make the treebank more valuable as a source of data for theoretical linguistic investigations . | {
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The information gained from corpus research and the analyses that are proposed are realized in the framework of SILVA , a parsing and extraction tool for German text corpora . | {
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While paraphrasing is critical both for interpretation and generation of natural language , current systems use manual or semi-automatic methods to collect paraphrases . | {
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We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text . | {
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Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases . | {
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An efficient bit-vector-based CKY-style parser for context-free parsing is presented . | {
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The parser computes a compact parse forest representation of the complete set of possible analyses for large treebank grammars and long input sentences . | {
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{
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The parser uses bit-vector operations to parallelise the basic parsing operations . | {
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In this paper , we propose a partially-blurred-image classification and analysis framework for automatically detecting images containing blurred regions and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring . | {
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... | [] |
We develop several blur features modeled by image color , gradient , and spectrum information , and use feature parameter training to robustly classify blurred images . | {
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{
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Our blur detection is based on image patches , making region-wise training and classification in one image efficient . | {
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{
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Extensive experiments show that our method works satisfactorily on challenging image data , which establishes a technical foundation for solving several computer vision problems , such as motion analysis and image restoration , using the blur information . | {
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},
{
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We have recently reported on two new word-sense disambiguation systems , one trained on bilingual material -LRB- the Canadian Hansards -RRB- and the other trained on monolingual material -LRB- Roget 's Thesaurus and Grolier 's Encyclopedia -RRB- . | {
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"text": "word-sense disambiguation systems",
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},
{
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In addition , it could also be used to help evaluate disambiguation algorithms that did not make use of the discourse constraint . | {
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"text": "disambiguation algorithms",
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We study and compare two novel embedding methods for segmenting feature points of piece-wise planar structures from two -LRB- uncalibrated -RRB- perspective images . | {
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"text": "embedding methods",
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"tail": {
"text": "segmenting feature points of piece-wise planar structures",
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]... | [] |
We show that a set of different homographies can be embedded in different ways to a higher-dimensional real or complex space , so that each homography corresponds to either a complex bilinear form or a real quadratic form . | {
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"tail": {
"text": "homographies",
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},
{
"head": ... | [] |
We give a closed-form segmentation solution for each case by utilizing these properties based on subspace-segmentation methods . | {
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"head": {
"text": "subspace-segmentation methods",
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"tail": {
"text": "closed-form segmentation solution",
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These theoretical results show that one can intrinsically segment a piece-wise planar scene from 2-D images without explicitly performing any 3-D reconstruction . | {
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"head": {
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}
} | [] |
Background maintenance is a frequent element of video surveillance systems . | {
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"head": {
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"tail": {
"text": "video surveillance systems",
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]
}
} | [] |
We develop Wallflower , a three-component system for background maintenance : the pixel-level component performs Wiener filtering to make probabilistic predictions of the expected background ; the region-level component fills in homogeneous regions of foreground objects ; and the frame-level component detects sudden , ... | {
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"tail": {
"text": "background maintenance",
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},
{
"head": {
... | [] |
We compare our system with 8 other background subtraction algorithms . | {
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"text": "system",
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"tail": {
"text": "background subtraction algorithms",
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}
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}
} | [] |
Wallflower is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur . | {
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"head": {
"text": "Wallflower",
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"tail": {
"text": "algorithms",
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]
}
} | [] |
Finally , we analyze the experimental results and propose normative principles for background maintenance . | {
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} | [] |
Is it possible to use out-of-domain acoustic training data to improve a speech recognizer 's performance on a speciic , independent application ? | {
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"used for": [
{
"head": {
"text": "out-of-domain acoustic training data",
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"tail": {
"text": "speech recognizer",
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} | [] |
In our experiments , we use Wallstreet Journal -LRB- WSJ -RRB- data to train a recognizer , which is adapted and evaluated in the Phonebook domain . | {
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"used for": [
{
"head": {
"text": "Wallstreet Journal -LRB- WSJ -RRB- data",
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First , starting from the WSJ-trained recognizer , how much adaptation data -LRB- taken from the Phonebook training corpus -RRB- is necessary to achieve a reasonable recognition performance in spite of the high degree of mismatch ? | {
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"tail": {
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},
{
"head": {
"text": ... | [] |
Second , is it possible to improve the recognition performance of a Phonebook-trained baseline acoustic model by using additional out-of-domain training data ? | {
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{
"head": {
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"text": "recognition",
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},
{
"head": {
... | [] |
This paper proposes an approach to full parsing suitable for Information Extraction from texts . | {
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"tail": {
"text": "full parsing",
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},
{
"head": {
"text": "full parsing",... | [] |
It was implemented in the IE module of FACILE , a EU project for multilingual text classification and IE . | {
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"head": {
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It then presents an implemented graphic interpretation system that takes into account a variety of communicative signals , and an evaluation study showing that evidence obtained from shallow processing of the graphic 's caption has a significant impact on the system 's success . | {
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},
{
"head": {
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Graphical models such as Bayesian Networks -LRB- BNs -RRB- are being increasingly applied to various computer vision problems . | {
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One bottleneck in using BN is that learning the BN model parameters often requires a large amount of reliable and representative training data , which proves to be difficult to acquire for many computer vision tasks . | {
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"head": {
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},
{
"head": {
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On the other hand , there is often available qualitative prior knowledge about the model . | {
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} | [] |
Such knowledge comes either from domain experts based on their experience or from various physical or geometric constraints that govern the objects we try to model . | {
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{
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Unlike the quantitative prior , the qualitative prior is often ignored due to the difficulty of incorporating them into the model learning process . | {
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