7,917 research outputs found
Automated Particle Identification through Regression Analysis of Size, Shape and Colour
Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a
range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for
parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based
diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the
field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved
by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the
diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during
the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood
sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by
a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After
subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a
certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false).
As such the computer program should be able to ”predict” with reasonable level of confidence if a given particle
belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three
continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a
logistic regression equation as they proved to have a relatively high predictive value on their own
Posture recognition based fall detection system for monitoring an elderly person in a smart home environment
We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment
Foreground Detection in Camouflaged Scenes
Foreground detection has been widely studied for decades due to its
importance in many practical applications. Most of the existing methods assume
foreground and background show visually distinct characteristics and thus the
foreground can be detected once a good background model is obtained. However,
there are many situations where this is not the case. Of particular interest in
video surveillance is the camouflage case. For example, an active attacker
camouflages by intentionally wearing clothes that are visually similar to the
background. In such cases, even given a decent background model, it is not
trivial to detect foreground objects. This paper proposes a texture guided
weighted voting (TGWV) method which can efficiently detect foreground objects
in camouflaged scenes. The proposed method employs the stationary wavelet
transform to decompose the image into frequency bands. We show that the small
and hardly noticeable differences between foreground and background in the
image domain can be effectively captured in certain wavelet frequency bands. To
make the final foreground decision, a weighted voting scheme is developed based
on intensity and texture of all the wavelet bands with weights carefully
designed. Experimental results demonstrate that the proposed method achieves
superior performance compared to the current state-of-the-art results.Comment: IEEE International Conference on Image Processing, 201
CVABS: Moving Object Segmentation with Common Vector Approach for Videos
Background modelling is a fundamental step for several real-time computer
vision applications that requires security systems and monitoring. An accurate
background model helps detecting activity of moving objects in the video. In
this work, we have developed a new subspace based background modelling
algorithm using the concept of Common Vector Approach with Gram-Schmidt
orthogonalization. Once the background model that involves the common
characteristic of different views corresponding to the same scene is acquired,
a smart foreground detection and background updating procedure is applied based
on dynamic control parameters. A variety of experiments is conducted on
different problem types related to dynamic backgrounds. Several types of
metrics are utilized as objective measures and the obtained visual results are
judged subjectively. It was observed that the proposed method stands
successfully for all problem types reported on CDNet2014 dataset by updating
the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl
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