9,954 research outputs found
3-D Face Analysis and Identification Based on Statistical Shape Modelling
This paper presents an effective method of statistical shape representation for automatic face analysis and identification in 3-D. The method combines statistical shape modelling techniques and the non-rigid deformation matching scheme. This work is distinguished by three key contributions. The first is the introduction of a new 3-D shape registration method using hierarchical landmark detection and multilevel B-spline warping technique, which allows accurate dense correspondence search for statistical model construction. The second is the shape representation approach, based on Laplacian Eigenmap, which provides a nonlinear submanifold that links underlying structure of facial data. The third contribution is a hybrid method for matching the statistical model and test dataset which controls the levels of the model’s deformation at different matching stages and so increases chance of the successful matching. The proposed method is tested on the public database, BU-3DFE. Results indicate that it can achieve extremely high verification rates in a series of tests, thus providing real-world practicality
Ensemble of Hankel Matrices for Face Emotion Recognition
In this paper, a face emotion is considered as the result of the composition
of multiple concurrent signals, each corresponding to the movements of a
specific facial muscle. These concurrent signals are represented by means of a
set of multi-scale appearance features that might be correlated with one or
more concurrent signals. The extraction of these appearance features from a
sequence of face images yields to a set of time series. This paper proposes to
use the dynamics regulating each appearance feature time series to recognize
among different face emotions. To this purpose, an ensemble of Hankel matrices
corresponding to the extracted time series is used for emotion classification
within a framework that combines nearest neighbor and a majority vote schema.
Experimental results on a public available dataset shows that the adopted
representation is promising and yields state-of-the-art accuracy in emotion
classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text
overlap with arXiv:1506.0500
PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras
We present the first purely event-based, energy-efficient approach for object
detection and categorization using an event camera. Compared to traditional
frame-based cameras, choosing event cameras results in high temporal resolution
(order of microseconds), low power consumption (few hundred mW) and wide
dynamic range (120 dB) as attractive properties. However, event-based object
recognition systems are far behind their frame-based counterparts in terms of
accuracy. To this end, this paper presents an event-based feature extraction
method devised by accumulating local activity across the image frame and then
applying principal component analysis (PCA) to the normalized neighborhood
region. Subsequently, we propose a backtracking-free k-d tree mechanism for
efficient feature matching by taking advantage of the low-dimensionality of the
feature representation. Additionally, the proposed k-d tree mechanism allows
for feature selection to obtain a lower-dimensional dictionary representation
when hardware resources are limited to implement dimensionality reduction.
Consequently, the proposed system can be realized on a field-programmable gate
array (FPGA) device leading to high performance over resource ratio. The
proposed system is tested on real-world event-based datasets for object
categorization, showing superior classification performance and relevance to
state-of-the-art algorithms. Additionally, we verified the object detection
method and real-time FPGA performance in lab settings under non-controlled
illumination conditions with limited training data and ground truth
annotations.Comment: Accepted in ACCV 2018 Workshops, to appea
ProSLAM: Graph SLAM from a Programmer's Perspective
In this paper we present ProSLAM, a lightweight stereo visual SLAM system
designed with simplicity in mind. Our work stems from the experience gathered
by the authors while teaching SLAM to students and aims at providing a highly
modular system that can be easily implemented and understood. Rather than
focusing on the well known mathematical aspects of Stereo Visual SLAM, in this
work we highlight the data structures and the algorithmic aspects that one
needs to tackle during the design of such a system. We implemented ProSLAM
using the C++ programming language in combination with a minimal set of well
known used external libraries. In addition to an open source implementation, we
provide several code snippets that address the core aspects of our approach
directly in this paper. The results of a thorough validation performed on
standard benchmark datasets show that our approach achieves accuracy comparable
to state of the art methods, while requiring substantially less computational
resources.Comment: 8 pages, 8 figure
Automating image analysis by annotating landmarks with deep neural networks
Image and video analysis is often a crucial step in the study of animal behavior and kinematics. Often these analyses require that the position of one or more animal landmarks are annotated (marked) in numerous images. The process of annotating landmarks can require a significant amount of time and tedious labor, which motivates the need for algorithms that can automatically annotate landmarks. In the community of scientists that use image and video analysis to study the 3D flight of animals, there has been a trend of developing more automated approaches for annotating landmarks, yet they fall short of being generally applicable. Inspired by the success of Deep Neural Networks (DNNs) on many problems in the field of computer vision, we investigate how suitable DNNs are for accurate and automatic annotation of landmarks in video datasets representative of those collected by scientists studying animals.
Our work shows, through extensive experimentation on videos of hawkmoths, that DNNs are suitable for automatic and accurate landmark localization. In particular, we show that one of our proposed DNNs is more accurate than the current best algorithm for automatic localization of landmarks on hawkmoth videos. Moreover, we demonstrate how these annotations can be used to quantitatively analyze the 3D flight of a hawkmoth. To facilitate the use of DNNs by scientists from many different fields, we provide a self contained explanation of what DNNs are, how they work, and how to apply them to other datasets using the freely available library Caffe and supplemental code that we provide.https://arxiv.org/abs/1702.00583Published versio
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