88,322 research outputs found
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
Good results on image classification and retrieval using support vector
machines (SVM) with local binary patterns (LBPs) as features have been
extensively reported in the literature where an entire image is retrieved or
classified. In contrast, in medical imaging, not all parts of the image may be
equally significant or relevant to the image retrieval application at hand. For
instance, in lung x-ray image, the lung region may contain a tumour, hence
being highly significant whereas the surrounding area does not contain
significant information from medical diagnosis perspective. In this paper, we
propose to detect salient regions of images during training and fold the data
to reduce the effect of irrelevant regions. As a result, smaller image areas
will be used for LBP features calculation and consequently classification by
SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the
performance of the proposed approach. The results demonstrate the benefits of
saliency-based folding approach that delivers comparable classification
accuracies with state-of-the-art but exhibits lower computational cost and
storage requirements, factors highly important for big data analytics.Comment: To appear in proceedings of The 14th International Conference on
Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA,
201
Context-aware CNNs for person head detection
Person detection is a key problem for many computer vision tasks. While face
detection has reached maturity, detecting people under a full variation of
camera view-points, human poses, lighting conditions and occlusions is still a
difficult challenge. In this work we focus on detecting human heads in natural
scenes. Starting from the recent local R-CNN object detector, we extend it with
two types of contextual cues. First, we leverage person-scene relations and
propose a Global CNN model trained to predict positions and scales of heads
directly from the full image. Second, we explicitly model pairwise relations
among objects and train a Pairwise CNN model using a structured-output
surrogate loss. The Local, Global and Pairwise models are combined into a joint
CNN framework. To train and test our full model, we introduce a large dataset
composed of 369,846 human heads annotated in 224,740 movie frames. We evaluate
our method and demonstrate improvements of person head detection against
several recent baselines in three datasets. We also show improvements of the
detection speed provided by our model.Comment: To appear in International Conference on Computer Vision (ICCV), 201
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Objective: Surgical data science is evolving into a research field that aims
to observe everything occurring within and around the treatment process to
provide situation-aware data-driven assistance. In the context of endoscopic
video analysis, the accurate classification of organs in the field of view of
the camera proffers a technical challenge. Herein, we propose a new approach to
anatomical structure classification and image tagging that features an
intrinsic measure of confidence to estimate its own performance with high
reliability and which can be applied to both RGB and multispectral imaging (MI)
data. Methods: Organ recognition is performed using a superpixel classification
strategy based on textural and reflectance information. Classification
confidence is estimated by analyzing the dispersion of class probabilities.
Assessment of the proposed technology is performed through a comprehensive in
vivo study with seven pigs. Results: When applied to image tagging, mean
accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB)
and 96% (MI) with the confidence measure. Conclusion: Results showed that the
confidence measure had a significant influence on the classification accuracy,
and MI data are better suited for anatomical structure labeling than RGB data.
Significance: This work significantly enhances the state of art in automatic
labeling of endoscopic videos by introducing the use of the confidence metric,
and by being the first study to use MI data for in vivo laparoscopic tissue
classification. The data of our experiments will be released as the first in
vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table
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