Bart Jansen

3D human pose recognition for home monitoring of elderly

A toolbox for the automatic monitoring of elderly in a nursing home or in the natural home environment is proposed. Rather than monitoring vital signs or other biomedical parameters, the toolbox is focussed on the monitoring of activity patterns and changes therein. Activity information is derived from visual information using image processing algorithms. The visual information is acquired using 3D camera technology. Besides a traditional visual image, 3D cameras also provide highly accurate depth information. The 3D position of the subject is derived and serves as the primary information source for the different components in the toolbox.

A Locally Adaptive System for the Fusion of Objective Quality Measures

Objective measures to automatically predict the perceptual quality of images or videos can reduce the time and cost requirements of end-to-end quality monitoring. For reliable quality predictions, these objective quality measures need to respond consistently with the behavior of the human visual system (HVS). In practice, many important HVS mechanisms are too complex to be modeled directly. Instead, they can be mimicked by machine learning systems, trained on subjective quality assessment databases, and applied on predefined objective quality measures for specific content or distortion classes. On the downside, machine learning systems are often difficult to interpret and may even contradict the input objective quality measures, leading to unreliable quality predictions. To address this problem, we developed an interpretable machine learning system for objective quality assessment, namely the locally adaptive fusion (LAF). This paper describes the LAF system and compares its performance with traditional machine learning. As it turns out, the LAF system is more consistent with the input measures and can better handle heteroscedastic training data.

Semi-automatic calibration of 3D camera images — Monitoring activities made easy

A major practical problem when implementing home monitoring using 3D camera technology is the calibration process which serves to relate the camera coordinate system to a coordinate system interpretable by the caregiver. In this paper, we propose a calibration algorithm which can relate both coordinate systems without performing actual measurements of the position of reference points in the room. The calibration tool is constructed such that it can be operated by any caregiver and hence eases the deployment of the home monitoring appliances. Our method (semi)automatically detects a plane corresponding to the ground floor in the three dimensional images. Random points from that plane are used to calculate rotation angles in a least squares manner. Experiments are performed to investigate how the obtained results depend on the parameters of the algorithm.

Detection of activity pattern changes among elderly with 3D camera technology

This paper provides an overview of the medical scales which are currently in practice at the geriatrics department of the hospital for assessing independence and mobility of elderly patients. Several shortcomings and issues related to the scales are identified. It is shown how a 3D camera system could be used for the automatic assessment of several items of the scales. this automated assessment is overcoming many of the issues with the existing methods. An analysis of the automatically identified activity features of a typical patient is used to compare the data derived from our system with data obtained with accelerometer readings.

Classification of microcalcifications using micro-CT

Microcalcifications are tiny spots of calcium deposit that often occur in female breasts. Microcalcifications are common in healthy woman, but they often are an early sign of breast cancer. On a mammogram; the current standard of care for breast screening; calcifications appear as tiny white dots. They may occur scattered throughout the breast or grouped in clusters. Radiologists determine the suspiciousness based upon several factors, including position, frequency, grouping, evolution compared to prior studies and shape. In this paper, we study micro-CT images of biopsy samples containing microcalcifications. The scanner delivers 3D images with a voxel size of 8.66 μm, i.e. ca. 8 times the spatial resolution of a contemporary digital mammogram. We propose an automated binary classification method of the samples, based upon shape analysis of the microcalcifications. The study is performed on a set of 50 benign and 50 malign samples preserved in paraffin. The ground truth of the classification is based upon anapathological investigation of the paraffin blocks. The results show a sensitivity, i.e. the percentage of correctly classified malign samples, of up to 98% with a specificity of 40%.

An approach for combining multiple descriptors for image classification

Recently, efficient image descriptors have shown promise for image classification tasks. Moreover, methods based on the combination of multiple image features provide better performance compared to methods based on a single feature. This work presents a simple and efficient approach for combining multiple image descriptors. We first employ a Naive-Bayes Nearest-Neighbor scheme to evaluate four widely used descriptors. For all features, “Image-to-Class” distances are directly computed without descriptor quantization. Since distances measured by different metrics can be of different nature and they may not be on the same numerical scale, a normalization step is essential to transform these distances into a common domain prior to combining them. Our experiments conducted on a challenging database indicate that z-score normalization followed by a simple sum of distances fusion technique can significantly improve the performance compared to applications in which individual features are used. It was also observed that our experimental results on the Caltech 101 dataset outperform other previous results.