2014 Canadian Conference on Computer and Robot Vision, 2014
ABSTRACT The identification of pond turtles is important to scientists who monitor local populati... more ABSTRACT The identification of pond turtles is important to scientists who monitor local populations, as it allows them to track the growth and health of subjects over their lifetime. Traditional non-invasive methods for turtle recognition involve the visual inspection of distinctive coloured patterns on their plastron. This visual inspection is time consuming and difficult to scale with a potential growth in the surveyed population. We propose an algorithm for automatic identification of individual turtles based on images of their plastron. Our approach uses a combination of image processing and neural networks. We perform a convexity-concavity analysis of the contours on the plastron. The output of this analysis is combined with additional region-based measurements to compute feature vectors that characterize individual turtles. These features are used to train a neural network. Our goal is to create a neural network which is able to query a database of images of turtles of known identity with an image of an unknown turtle, and which outputs the unknown turtle's identity. The paper provides a thorough experimental evaluation of the proposed approach. Results are promising and point towards future work in the area of standardized image acquisition and image denoising.
2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013
ABSTRACT This paper proposes a novel method for computer vision-based, marker-less analysis of da... more ABSTRACT This paper proposes a novel method for computer vision-based, marker-less analysis of daily human actions for detecting motion irregularities (sway). Sway occurs due to a temporary loss in balance and is an important indicator of decay in motor skills. One should note that the purpose of the proposed approach is not to recognize the performed activity (which is a controlled variable in our experimental design), but to detect irregularities in the performance of this activity. The proposed motion model is based on population Hidden Markov Models. This model has been trained and tested on a custom-designed database involving multiple daily actions. Experimental results demonstrate its robustness with respect to subject and speed variability in training sequences, as well as its ability to capture sway-type motion irregularities.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010
This paper proposes a new method for the automatic contrast enhancement of fiducial markers in lo... more This paper proposes a new method for the automatic contrast enhancement of fiducial markers in low-radiation Electronic Portal Images. It is shown that the proposed approach significantly enhances the contrast of the fiducial markers and produces results where these markers are clearly visible. The main theoretical contribution consists in designing an algorithm that enhances the contrast of small structures in
In this paper, we present a 3D reconstruction approach of a liver tu- mour model from a sequence ... more In this paper, we present a 3D reconstruction approach of a liver tu- mour model from a sequence of 2D MR parallel cross-sections, and the integra- tion of this reconstructed 3D model with a mechanical tissue model. The recon- struction algorithm uses shape-based interpolation and extrapolation. While in- terpolation generates intermediate slices between every pair of adjacent i nput slices,
2014 Canadian Conference on Computer and Robot Vision, 2014
ABSTRACT The identification of pond turtles is important to scientists who monitor local populati... more ABSTRACT The identification of pond turtles is important to scientists who monitor local populations, as it allows them to track the growth and health of subjects over their lifetime. Traditional non-invasive methods for turtle recognition involve the visual inspection of distinctive coloured patterns on their plastron. This visual inspection is time consuming and difficult to scale with a potential growth in the surveyed population. We propose an algorithm for automatic identification of individual turtles based on images of their plastron. Our approach uses a combination of image processing and neural networks. We perform a convexity-concavity analysis of the contours on the plastron. The output of this analysis is combined with additional region-based measurements to compute feature vectors that characterize individual turtles. These features are used to train a neural network. Our goal is to create a neural network which is able to query a database of images of turtles of known identity with an image of an unknown turtle, and which outputs the unknown turtle's identity. The paper provides a thorough experimental evaluation of the proposed approach. Results are promising and point towards future work in the area of standardized image acquisition and image denoising.
2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013
ABSTRACT This paper proposes a novel method for computer vision-based, marker-less analysis of da... more ABSTRACT This paper proposes a novel method for computer vision-based, marker-less analysis of daily human actions for detecting motion irregularities (sway). Sway occurs due to a temporary loss in balance and is an important indicator of decay in motor skills. One should note that the purpose of the proposed approach is not to recognize the performed activity (which is a controlled variable in our experimental design), but to detect irregularities in the performance of this activity. The proposed motion model is based on population Hidden Markov Models. This model has been trained and tested on a custom-designed database involving multiple daily actions. Experimental results demonstrate its robustness with respect to subject and speed variability in training sequences, as well as its ability to capture sway-type motion irregularities.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010
This paper proposes a new method for the automatic contrast enhancement of fiducial markers in lo... more This paper proposes a new method for the automatic contrast enhancement of fiducial markers in low-radiation Electronic Portal Images. It is shown that the proposed approach significantly enhances the contrast of the fiducial markers and produces results where these markers are clearly visible. The main theoretical contribution consists in designing an algorithm that enhances the contrast of small structures in
In this paper, we present a 3D reconstruction approach of a liver tu- mour model from a sequence ... more In this paper, we present a 3D reconstruction approach of a liver tu- mour model from a sequence of 2D MR parallel cross-sections, and the integra- tion of this reconstructed 3D model with a mechanical tissue model. The recon- struction algorithm uses shape-based interpolation and extrapolation. While in- terpolation generates intermediate slices between every pair of adjacent i nput slices,
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Papers by Alexandra Albu