153 research outputs found
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
Stereo matching is one of the longest-standing problems in computer vision
with close to 40 years of studies and research. Throughout the years the
paradigm has shifted from local, pixel-level decision to various forms of
discrete and continuous optimization to data-driven, learning-based methods.
Recently, the rise of machine learning and the rapid proliferation of deep
learning enhanced stereo matching with new exciting trends and applications
unthinkable until a few years ago. Interestingly, the relationship between
these two worlds is two-way. While machine, and especially deep, learning
advanced the state-of-the-art in stereo matching, stereo itself enabled new
ground-breaking methodologies such as self-supervised monocular depth
estimation based on deep networks. In this paper, we review recent research in
the field of learning-based depth estimation from single and binocular images
highlighting the synergies, the successes achieved so far and the open
challenges the community is going to face in the immediate future.Comment: Accepted to TPAMI. Paper version of our CVPR 2019 tutorial:
"Learning-based depth estimation from stereo and monocular images: successes,
limitations and future challenges"
(https://sites.google.com/view/cvpr-2019-depth-from-image/home
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix
Fitness applications are commonly used to monitor activities within the gym,
but they often fail to automatically track indoor activities inside the gym.
This study proposes a model that utilizes pose estimation combined with a novel
data augmentation method, i.e., rotation matrix. We aim to enhance the
classification accuracy of activity recognition based on pose estimation data.
Through our experiments, we experiment with different classification algorithms
along with image augmentation approaches. Our findings demonstrate that the SVM
with SGD optimization, using data augmentation with the Rotation Matrix, yields
the most accurate results, achieving a 96% accuracy rate in classifying five
physical activities. Conversely, without implementing the data augmentation
techniques, the baseline accuracy remains at a modest 64%.Comment: 24 page
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