3,148 research outputs found
Empirical mode decomposition-based facial pose estimation inside video sequences
We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions
Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device
There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient aid. This article presents an evaluation of compressive sensing techniques in an accelerometer-based intelligent fall detection system modelled on a wearable Shimmer biomedical embedded computing device with Matlab. The presented fall detection system utilises a database of fall and activities of daily living signals evaluated with discrete wavelet transforms and principal component analysis to obtain binary tree classifiers for fall evaluation. 14 test subjects undertook various fall and activities of daily living experiments with a Shimmer device to generate data for principal component analysis-based fall classifiers and evaluate the proposed fall analysis system. The presented system obtains highly accurate fall detection results, demonstrating significant advantages in comparison with the thresholding method presented. Additionally, the presented approach offers advantageous fall diagnostic information. Furthermore, transmitted data accounts for over 80% battery current usage of the Shimmer device, hence it is critical the acceleration data is reduced to increase transmission efficiency and in-turn improve battery usage performance. Various Matching pursuit-based compressive sensing techniques have been utilised to significantly reduce acceleration information required for transmission.Scopu
Fitting 3D Morphable Models using Local Features
In this paper, we propose a novel fitting method that uses local image
features to fit a 3D Morphable Model to 2D images. To overcome the obstacle of
optimising a cost function that contains a non-differentiable feature
extraction operator, we use a learning-based cascaded regression method that
learns the gradient direction from data. The method allows to simultaneously
solve for shape and pose parameters. Our method is thoroughly evaluated on
Morphable Model generated data and first results on real data are presented.
Compared to traditional fitting methods, which use simple raw features like
pixel colour or edge maps, local features have been shown to be much more
robust against variations in imaging conditions. Our approach is unique in that
we are the first to use local features to fit a Morphable Model.
Because of the speed of our method, it is applicable for realtime
applications. Our cascaded regression framework is available as an open source
library (https://github.com/patrikhuber).Comment: Submitted to ICIP 2015; 4 pages, 4 figure
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Estimating heart rate and rhythm via 3D motion tracking in depth video
Low-cost depth sensors, such as Microsoft Kinect, have potential for non-intrusive, non-contact health monitoring that is robust to ambient lighting conditions. However, captured depth images typically suer from low bit-depth and high acquisition noise, and hence processing them to estimate biometrics is dicult. In this paper, we propose to capture depth video of a human subject using Kinect 2.0 to estimate his/her heart rate and rhythm (regularity); as blood is pumped from the heart to circulate through the head, tiny oscillatory head motion due to Newtonian mechanics can be detected for periodicity analysis. Specifically, we first restore a captured depth video via a joint bit-depth enhancement / denoising procedure, using a graph-signal smoothness prior for regularization. Second, we track an automatically detected head region throughout the depth video to deduce 3D motion vectors. The detected vectors are fed back to the depth restoration module in a loop to ensure that the motion information in two modules are consistent, improving performance of both restoration and motion tracking in the process. Third, the computed 3D motion vectors are projected onto its principal component for 1D signal analysis, composed of trend removal, band-pass filtering, and wavelet-based motion denoising. Finally, the heart rate is estimated via Welch power spectrum analysis, and the heart rhythm is computed via peak detection. Experimental results show accurate estimation of the heart rate and rhythm using our proposed algorithm as compared to rate and rhythm estimated by a portable oximeter
- …