949 research outputs found
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
How do computers and intelligent agents view the world around them? Feature
extraction and representation constitutes one the basic building blocks towards
answering this question. Traditionally, this has been done with carefully
engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is
no ``one size fits all'' approach that satisfies all requirements. In recent
years, the rising popularity of deep learning has resulted in a myriad of
end-to-end solutions to many computer vision problems. These approaches, while
successful, tend to lack scalability and can't easily exploit information
learned by other systems. Instead, we propose SAND features, a dedicated deep
learning solution to feature extraction capable of providing hierarchical
context information. This is achieved by employing sparse relative labels
indicating relationships of similarity/dissimilarity between image locations.
The nature of these labels results in an almost infinite set of dissimilar
examples to choose from. We demonstrate how the selection of negative examples
during training can be used to modify the feature space and vary it's
properties. To demonstrate the generality of this approach, we apply the
proposed features to a multitude of tasks, each requiring different properties.
This includes disparity estimation, semantic segmentation, self-localisation
and SLAM. In all cases, we show how incorporating SAND features results in
better or comparable results to the baseline, whilst requiring little to no
additional training. Code can be found at:
https://github.com/jspenmar/SAND_featuresComment: CVPR201
Geometric Mining: Scaling Geometric Hashing to Large Datasets
It is known that relative feature location is important in representing objects, but assumptions that make learning tractable often simplify how structure is encoded e.g. spatial pooling or star models. For example, techniques such as spatial pyramid matching (SPM), in-conjunction with machine learning techniques perform well [13]. However, there are limitations to such spatial encoding schemes which discard important information about the layout of features. In contrast, we propose to use the object itself to choose the basis of the features in an object centric approach. In doing so we return to the early work of geometric hashing [18] but demonstrate how such approaches can be scaled-up to modern day object detection challenges in terms of both the number of examples and their variability. We apply a two stage process, initially filtering background features to localise the objects and then hashing the remaining pairwise features in an affine invariant model. During learning, we identify class-wise key feature predictors. We validate our detection and classification of objects on the PASCAL VOC'07 and' 11 [6] and CarDb [21] datasets and compare with state of the art detectors and classifiers. Importantly we demonstrate how structure in features can be efficiently identified and how its inclusion can increase performance. This feature centric learning technique allows us to localise objects even without object annotation during training and the resultant segmentation provides accurate state of the art object localization, without the need for annotations
Multi-touchless: Real-time fingertip detection and tracking using geodesic maxima
Since the advent of multitouch screens users have been able to interact using fingertip gestures in a two dimensional plane. With the development of depth cameras, such as the Kinect, attempts have been made to reproduce the detection of gestures for three dimensional interaction. Many of these use contour analysis to find the fingertips, however the success of such approaches is limited due to sensor noise and rapid movements. This paper discusses an approach to identify fingertips during rapid movement at varying depths allowing multitouch without contact with a screen. To achieve this, we use a weighted graph that is built using the depth information of the hand to determine the geodesic maxima of the surface. Fingertips are then selected from these maxima using a simplified model of the hand and correspondence found over successive frames. Our experiments show real-time performance for multiple users providing tracking at 30fps for up to 4 hands and we compare our results with stateof- the-art methods, providing accuracy an order of magnitude better than existing approaches. © 2013 IEEE
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Implementing a perioperative efficiency initiative for orthopedic surgery instrumentation at an academic center: A comparative before-and-after study.
Optimizing surgical instrumentation may contribute to value-based care, particularly in commonly performed procedures. We report our experience in implementing a perioperative efficiency program in 2 types of orthopedic surgery (primary total-knee arthroplasty, TKA, and total-hip arthroplasty, THA).A comparative before-and-after study with 2 participating surgeons, each performing both THA and TKA, was conducted. Our objective was to evaluate the effect of surgical tray optimization on operating and processing time, cost, and waste associated with preparation, delivery, and staging of sterile surgical instruments. The study was designed as a prospective quality improvement initiative with pre- and postimplementation operational measures and a provider satisfaction survey.A total of 96 procedures (38 preimplementation and 58 postimplementation) were assessed using time-stamped performance endpoints. The number and weight of trays and instruments processed were reduced substantially after the optimization intervention, particularly for TKA. Setup time was reduced by 23% (6 minutes, P = .01) for TKA procedures but did not differ for THA. The number of survey respondents was small, but satisfaction was high overall among personnel involved in implementation.Optimizing instrumentation trays for orthopedic procedures yielded reduction in processing time and cost. Future research should evaluate patient outcomes and incremental/additive impact on institutional quality measures
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning
In the current monocular depth research, the dominant approach is to employ
unsupervised training on large datasets, driven by warped photometric
consistency. Such approaches lack robustness and are unable to generalize to
challenging domains such as nighttime scenes or adverse weather conditions
where assumptions about photometric consistency break down.
We propose DeFeat-Net (Depth & Feature network), an approach to
simultaneously learn a cross-domain dense feature representation, alongside a
robust depth-estimation framework based on warped feature consistency. The
resulting feature representation is learned in an unsupervised manner with no
explicit ground-truth correspondences required.
We show that within a single domain, our technique is comparable to both the
current state of the art in monocular depth estimation and supervised feature
representation learning. However, by simultaneously learning features, depth
and motion, our technique is able to generalize to challenging domains,
allowing DeFeat-Net to outperform the current state-of-the-art with around 10%
reduction in all error measures on more challenging sequences such as nighttime
driving
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies
Deep learning has become an increasingly common technique for various control
problems, such as robotic arm manipulation, robot navigation, and autonomous
vehicles. However, the downside of using deep neural networks to learn control
policies is their opaque nature and the difficulties of validating their
safety. As the networks used to obtain state-of-the-art results become
increasingly deep and complex, the rules they have learned and how they operate
become more challenging to understand. This presents an issue, since in
safety-critical applications the safety of the control policy must be ensured
to a high confidence level. In this paper, we propose an automated black box
testing framework based on adversarial reinforcement learning. The technique
uses an adversarial agent, whose goal is to degrade the performance of the
target model under test. We test the approach on an autonomous vehicle problem,
by training an adversarial reinforcement learning agent, which aims to cause a
deep neural network-driven autonomous vehicle to collide. Two neural networks
trained for autonomous driving are compared, and the results from the testing
are used to compare the robustness of their learned control policies. We show
that the proposed framework is able to find weaknesses in both control policies
that were not evident during online testing and therefore, demonstrate a
significant benefit over manual testing methods.Comment: 2020 IEEE International Conference on Robotics and Automation (ICRA
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
The use of neural networks and reinforcement learning has become increasingly
popular in autonomous vehicle control. However, the opaqueness of the resulting
control policies presents a significant barrier to deploying neural
network-based control in autonomous vehicles. In this paper, we present a
reinforcement learning based approach to autonomous vehicle longitudinal
control, where the rule-based safety cages provide enhanced safety for the
vehicle as well as weak supervision to the reinforcement learning agent. By
guiding the agent to meaningful states and actions, this weak supervision
improves the convergence during training and enhances the safety of the final
trained policy. This rule-based supervisory controller has the further
advantage of being fully interpretable, thereby enabling traditional validation
and verification approaches to ensure the safety of the vehicle. We compare
models with and without safety cages, as well as models with optimal and
constrained model parameters, and show that the weak supervision consistently
improves the safety of exploration, speed of convergence, and model
performance. Additionally, we show that when the model parameters are
constrained or sub-optimal, the safety cages can enable a model to learn a safe
driving policy even when the model could not be trained to drive through
reinforcement learning alone.Comment: Published in Sensor
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