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We propose a probabilistic keyboard based on syllable HMMs, as well as an adaptation for users and operating styles to achieve high accuracy on the software keyboard on mobile devices. The syllable HMMs balances high accuracy by introducing syllabic constraints and word flexibility by not depending on a dictionary. Experimental results showed that a user-dependent probabilistic model reduced the error...
Variations in inter-patient prostate shape, and size and imaging artifacts in magnetic resonance images (MRI) hinders automatic accurate prostate segmentation. In this paper we propose a graph cut based energy minimization of the posterior probabilities obtained in a supervised learning schema for automatic 3D segmentation of the prostate in MRI. A probabilistic classification of the prostate voxels...
In this paper we propose a probabilistic method for fusing depth maps in real time for wide-baseline situation. We treat the depth map fusion as a problem of probability density function (pdf) estimation. The original point cloud, instead of the reprojected depth map, is used to estimate the pdf, and a mathematical expectation computation method is proposed to reduce the complexity of the method....
A document image matching approach making use of probabilistic graphical models is proposed. The document image is first represented by a tree with the nodes in the tree corresponding to the regions in the image and the edges indicating the parent-child relationships between them, transforming the problem to tree matching. A graphical model, i.e. pairwise Markov Random Field is defined on the tree,...
In this paper, an automatic image annotation (AIA) method using Gaussian mixture model (GMM) is discussed. Supervised multiclass labeling (SML), which is a notable AIA method using GMM, has a problem of low annotation performances of labels that have a few training samples because of over fitting. In the present study, we propose to introduce a cross entropy based constraint into SML. According to...
In this paper we present a method of sampling from a probabilistic generative model for a set of graphs. Our method is based on the assumption that the nodes and edges of graphs arise under independent Bernoulli distributions. We sample graphs from the generative model according to the node and edge occurrence probabilities. We explain the construction of our generative model and then compute the...
We present an ensemble recognition method for graphic symbols that could be interfered by intersecting objects from the context. The symbol is first represented as a set of shape points, each of which is described by a shape context pyramid capturing the local shape characteristics of multi-scale regions surrounding the shape point. A Hough forest ensemble classifier is then employed to learn the...
We propose a generic framework to handle missing weak classifiers at prediction time in a boosted cascade. The contribution is a probabilistic formulation of the cascade structure that considers the uncertainty introduced by missing weak classifiers. This new formulation involves two problems: 1) the approximation of posterior probabilities on each level and 2) the computation of thresholds on these...
In this paper we develop a practical method for estimating shape and reflectance using only three polarised images. Using polarised light and retro-reflection settings during image acquisition, we separate the diffuse and specular reflectance components using Blind Source Separation without the accurate knowledge of the polariser angle information. Next, we compare the capacities of five chosen reflectance...
This paper presents a probabilistic approach for logo detection and localization in natural scene images. Two probability distributions are computed, one considering the features extracted from the key points located inside a region and the second refers to shape geometry defined by the key points. The barycentric co-ordinates are considered to define the shape statistics. The performance of the proposed...
In this paper, a unified view of the problem of class-selection with Bayesian classifiers is presented. Selecting a subset of classes instead of singleton allows 1) to reduce the error rate and 2) to propose a reduced set to another classifier or an expert. This second step provides additional information, and therefore increases the quality of the result. The proposed framework, based on the evaluation...
Non-negative matrix factorization [5](NMF) is a well known tool for unsupervised machine learning. It can be viewed as a generalization of the K-means clustering, Expectation Maximization based clustering and aspect modeling by Probabilistic Latent Semantic Analysis (PLSA). Specifically PLSA is related to NMF with KL-divergence objective function. Further it is shown that K-means clustering is a special...
We present a novel probabilistic model for parsing shapes into several distinguishable parts for accurate shape recognition. This shape parsing is based on robust geometric features that permit high recognition accuracy. Although modelling shapes is an inherently uncertain process, our approach is lenient, in that the desired parse of a shape only needs to be within its k most probable parses. Using...
Representing a video by a set of key frames is useful for efficient video browsing and retrieving. But key frame extraction keeps a challenge in the computer vision field. In this paper, we propose a joint framework to integrate both shot boundary detection and key frame extraction, wherein three probabilistic components are taken into account, i.e. the prior of the key frames, the conditional probability...
Automatic Call Recognition is vital for environmental monitoring. Patten recognition has been applied in automatic species recognition for years. However, few studies have applied formal syntactic methods to species call structure analysis. This paper introduces a novel method to adopt timed and probabilistic automata in automatic species recognition based upon acoustic components as the primitives...
In the emerging field of adaptive biometrics, systems aim to adapt enrolled templates to variations in samples observed during operations. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due to limitations associated with existing adaptation schemes. This paper proposes a dual-staged template adaptation scheme that allows...
During the last two decades, a series of subspace methods have succeeded in achieving a satisfactory performance for face recognition tasks, but have always failed when partial occlusions occur. This paper combines the subspace techniques with probabilistic models, and aims at achieving invariance to occlusions. The concept underlying the proposed method is that two faces with the same identity, even...
Data has multi-view representations from various feature spaces in real world. Multi-view clustering algorithms allow leveraging information from multiple views of the data and this may substantially improve the clustering result obtained by using a single view. In this paper, we propose a novel algorithm called Collaborative PLSA (C-PLSA) for multi-view clustering, which works on the assumption that...
This paper proposes a novel probabilistic approach to utilize clip attributes as hidden knowledge for event recognition. Event recognition in surveillance videos is very challenging due to its large intra-class variations and relative low image resolution. The clip attributes, that are available only during training, provide auxiliary hidden information about the variation of the event appearance...
This paper presents a modified Kanade-Lucas-Tomasi (KLT) tracking framework for multiple objects tracking applications. First, the framework includes a global pixel-level probabilistic model and an adaptive RGB template model to modify traditional KLT tracker more robust to track multiple objects and partial occlusions. Meanwhile, a Merge and Split algorithm is introduced in the proposed framework...
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