10,075 research outputs found
Growing Regression Forests by Classification: Applications to Object Pose Estimation
In this work, we propose a novel node splitting method for regression trees
and incorporate it into the regression forest framework. Unlike traditional
binary splitting, where the splitting rule is selected from a predefined set of
binary splitting rules via trial-and-error, the proposed node splitting method
first finds clusters of the training data which at least locally minimize the
empirical loss without considering the input space. Then splitting rules which
preserve the found clusters as much as possible are determined by casting the
problem into a classification problem. Consequently, our new node splitting
method enjoys more freedom in choosing the splitting rules, resulting in more
efficient tree structures. In addition to the Euclidean target space, we
present a variant which can naturally deal with a circular target space by the
proper use of circular statistics. We apply the regression forest employing our
node splitting to head pose estimation (Euclidean target space) and car
direction estimation (circular target space) and demonstrate that the proposed
method significantly outperforms state-of-the-art methods (38.5% and 22.5%
error reduction respectively).Comment: Paper accepted by ECCV 201
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models
Given large amount of real photos for training, Convolutional neural network
shows excellent performance on object recognition tasks. However, the process
of collecting data is so tedious and the background are also limited which
makes it hard to establish a perfect database. In this paper, our generative
model trained with synthetic images rendered from 3D models reduces the
workload of data collection and limitation of conditions. Our structure is
composed of two sub-networks: semantic foreground object reconstruction network
based on Bayesian inference and classification network based on multi-triplet
cost function for avoiding over-fitting problem on monotone surface and fully
utilizing pose information by establishing sphere-like distribution of
descriptors in each category which is helpful for recognition on regular photos
according to poses, lighting condition, background and category information of
rendered images. Firstly, our conjugate structure called generative model with
metric learning utilizing additional foreground object channels generated from
Bayesian rendering as the joint of two sub-networks. Multi-triplet cost
function based on poses for object recognition are used for metric learning
which makes it possible training a category classifier purely based on
synthetic data. Secondly, we design a coordinate training strategy with the
help of adaptive noises acting as corruption on input images to help both
sub-networks benefit from each other and avoid inharmonious parameter tuning
due to different convergence speed of two sub-networks. Our structure achieves
the state of the art accuracy of over 50\% on ShapeNet database with data
migration obstacle from synthetic images to real photos. This pipeline makes it
applicable to do recognition on real images only based on 3D models.Comment: 14 page
On the 3D point cloud for human-pose estimation
This thesis aims at investigating methodologies for estimating a human pose from a 3D point cloud that is captured by a static depth sensor. Human-pose estimation (HPE) is important for a range of applications, such as human-robot interaction, healthcare, surveillance, and so forth. Yet, HPE is challenging because of the uncertainty in sensor measurements and the complexity of human poses. In this research, we focus on addressing challenges related to two crucial components in the estimation process, namely, human-pose feature extraction and human-pose modeling.
In feature extraction, the main challenge involves reducing feature ambiguity. We propose a 3D-point-cloud feature called viewpoint and shape feature histogram (VISH) to reduce feature ambiguity by capturing geometric properties of the 3D point cloud of a human. The feature extraction consists of three steps: 3D-point-cloud pre-processing, hierarchical structuring, and feature extraction. In the pre-processing step, 3D points corresponding to a human are extracted and outliers from the environment are removed to retain the 3D points of interest. This step is important because it allows us to reduce the number of 3D points by keeping only those points that correspond to the human body for further processing. In the hierarchical structuring, the pre-processed 3D point cloud is partitioned and replicated into a tree structure as nodes. Viewpoint feature histogram (VFH) and shape features are extracted from each node in the tree to provide a descriptor to represent each node. As the features are obtained based on histograms, coarse-level details are highlighted in large regions and fine-level details are highlighted in small regions. Therefore, the features from the point cloud in the tree can capture coarse level to fine level information to reduce feature ambiguity.
In human-pose modeling, the main challenges involve reducing the dimensionality of human-pose space and designing appropriate factors that represent the underlying probability distributions for estimating human poses. To reduce the dimensionality, we propose a non-parametric action-mixture model (AMM). It represents high-dimensional human-pose space using low-dimensional manifolds in searching human poses. In each manifold, a probability distribution is estimated based on feature similarity. The distributions in the manifolds are then redistributed according to the stationary distribution of a Markov chain that models the frequency of human actions. After the redistribution, the manifolds are combined according to a probability distribution determined by action classification. Experiments were conducted using VISH features as input to the AMM. The results showed that the overall error and standard deviation of the AMM were reduced by about 7.9% and 7.1%, respectively, compared with a model without action classification.
To design appropriate factors, we consider the AMM as a Bayesian network and propose a mapping that converts the Bayesian network to a neural network called NN-AMM. The proposed mapping consists of two steps: structure identification and parameter learning. In structure identification, we have developed a bottom-up approach to build a neural network while preserving the Bayesian-network structure. In parameter learning, we have created a part-based approach to learn synaptic weights by decomposing a neural network into parts. Based on the concept of distributed representation, the NN-AMM is further modified into a scalable neural network called NND-AMM. A neural-network-based system is then built by using VISH features to represent 3D-point-cloud input and the NND-AMM to estimate 3D human poses. The results showed that the proposed mapping can be utilized to design AMM factors automatically. The NND-AMM can provide more accurate human-pose estimates with fewer hidden neurons than both the AMM and NN-AMM can. Both the NN-AMM and NND-AMM can adapt to different types of input, showing the advantage of using neural networks to design factors
An Expressive Deep Model for Human Action Parsing from A Single Image
This paper aims at one newly raising task in vision and multimedia research:
recognizing human actions from still images. Its main challenges lie in the
large variations in human poses and appearances, as well as the lack of
temporal motion information. Addressing these problems, we propose to develop
an expressive deep model to naturally integrate human layout and surrounding
contexts for higher level action understanding from still images. In
particular, a Deep Belief Net is trained to fuse information from different
noisy sources such as body part detection and object detection. To bridge the
semantic gap, we used manually labeled data to greatly improve the
effectiveness and efficiency of the pre-training and fine-tuning stages of the
DBN training. The resulting framework is shown to be robust to sometimes
unreliable inputs (e.g., imprecise detections of human parts and objects), and
outperforms the state-of-the-art approaches.Comment: 6 pages, 8 figures, ICME 201
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
Data clustering has received a lot of attention and numerous methods,
algorithms and software packages are available. Among these techniques,
parametric finite-mixture models play a central role due to their interesting
mathematical properties and to the existence of maximum-likelihood estimators
based on expectation-maximization (EM). In this paper we propose a new mixture
model that associates a weight with each observed point. We introduce the
weighted-data Gaussian mixture and we derive two EM algorithms. The first one
considers a fixed weight for each observation. The second one treats each
weight as a random variable following a gamma distribution. We propose a model
selection method based on a minimum message length criterion, provide a weight
initialization strategy, and validate the proposed algorithms by comparing them
with several state of the art parametric and non-parametric clustering
techniques. We also demonstrate the effectiveness and robustness of the
proposed clustering technique in the presence of heterogeneous data, namely
audio-visual scene analysis.Comment: 14 pages, 4 figures, 4 table
RUR53: an Unmanned Ground Vehicle for Navigation, Recognition and Manipulation
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously
navigate through, identify, and reach areas of interest; and there recognize,
localize, and manipulate work tools to perform complex manipulation tasks. The
proposed contribution includes a modular software architecture where each
module solves specific sub-tasks and that can be easily enlarged to satisfy new
requirements. Included indoor and outdoor tests demonstrate the capability of
the proposed system to autonomously detect a target object (a panel) and
precisely dock in front of it while avoiding obstacles. They show it can
autonomously recognize and manipulate target work tools (i.e., wrenches and
valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve
stem). A specific case study is described where the proposed modular
architecture lets easy switch to a semi-teleoperated mode. The paper
exhaustively describes description of both the hardware and software setup of
RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International
Robotics Challenge, and the lessons we learned when participating at this
competition, where we ranked third in the Gran Challenge in collaboration with
the Czech Technical University in Prague, the University of Pennsylvania, and
the University of Lincoln (UK).Comment: This article has been accepted for publication in Advanced Robotics,
published by Taylor & Franci
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