26 research outputs found
Oriented Edge Forests for Boundary Detection
We present a simple, efficient model for learning boundary detection based on
a random forest classifier. Our approach combines (1) efficient clustering of
training examples based on simple partitioning of the space of local edge
orientations and (2) scale-dependent calibration of individual tree output
probabilities prior to multiscale combination. The resulting model outperforms
published results on the challenging BSDS500 boundary detection benchmark.
Further, on large datasets our model requires substantially less memory for
training and speeds up training time by a factor of 10 over the structured
forest model.Comment: updated to include contents of CVPR version + new figure showing
example segmentation result
Point-wise mutual information-based video segmentation with high temporal consistency
In this paper, we tackle the problem of temporally consistent boundary
detection and hierarchical segmentation in videos. While finding the best
high-level reasoning of region assignments in videos is the focus of much
recent research, temporal consistency in boundary detection has so far only
rarely been tackled. We argue that temporally consistent boundaries are a key
component to temporally consistent region assignment. The proposed method is
based on the point-wise mutual information (PMI) of spatio-temporal voxels.
Temporal consistency is established by an evaluation of PMI-based point
affinities in the spectral domain over space and time. Thus, the proposed
method is independent of any optical flow computation or previously learned
motion models. The proposed low-level video segmentation method outperforms the
learning-based state of the art in terms of standard region metrics
Oriented Response Networks
Deep Convolution Neural Networks (DCNNs) are capable of learning
unprecedentedly effective image representations. However, their ability in
handling significant local and global image rotations remains limited. In this
paper, we propose Active Rotating Filters (ARFs) that actively rotate during
convolution and produce feature maps with location and orientation explicitly
encoded. An ARF acts as a virtual filter bank containing the filter itself and
its multiple unmaterialised rotated versions. During back-propagation, an ARF
is collectively updated using errors from all its rotated versions. DCNNs using
ARFs, referred to as Oriented Response Networks (ORNs), can produce
within-class rotation-invariant deep features while maintaining inter-class
discrimination for classification tasks. The oriented response produced by ORNs
can also be used for image and object orientation estimation tasks. Over
multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we
consistently observe that replacing regular filters with the proposed ARFs
leads to significant reduction in the number of network parameters and
improvement in classification performance. We report the best results on
several commonly used benchmarks.Comment: Accepted in CVPR 2017. Source code available at http://yzhou.work/OR
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Patch-based Corner Detection for Cervical Vertebrae in X-ray Images
Corners hold vital information about size, shape and morphology of a vertebra in an x-ray image, and recent literature [1, 2] has shown promising performance for detecting vertebral corners using a Hough forest-based architecture. To provide spatial context, this method generates a set of 12 patches around a vertebra and uses a machine learning approach to predict corners of a vertebral body through a voting process. In this paper, we extend this framework in terms of patch generation and prediction methods. During patch generation, the square region of interest has been replaced with data-driven rectangular and trapezoidal region of interest which better aligns the patches to the vertebral body geometry, resulting in more discriminative feature vectors. The corner estimation or the prediction stage has been improved by utilising more efficient voting process using a single kernel density estimation. In addition, advanced and more complex feature vectors are introduced. We also present a thorough evaluation of the framework with different patch generation methods, forest training mechanisms and prediction methods. In order to compare the performance of this framework with a more general method, a novel multi-scale Harris corner detector-based approach is introduced that incorporates a spatial prior through a naive Bayes method. All these methods have been tested on a dataset of 90 X-ray images and achieved an average corner localization error of 2.01 mm, representing a 33% improvement in localisation accuracy compared to the previous state-of-the-art method [2]