302 research outputs found
Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition
This paper presents a robust and dynamic face recognition technique based on
the extraction and matching of devised probabilistic graphs drawn on SIFT
features related to independent face areas. The face matching strategy is based
on matching individual salient facial graph characterized by SIFT features as
connected to facial landmarks such as the eyes and the mouth. In order to
reduce the face matching errors, the Dempster-Shafer decision theory is applied
to fuse the individual matching scores obtained from each pair of salient
facial features. The proposed algorithm is evaluated with the ORL and the IITK
face databases. The experimental results demonstrate the effectiveness and
potential of the proposed face recognition technique also in case of partially
occluded faces.Comment: 8 pages, 2 figure
Massively Parallel Video Networks
We introduce a class of causal video understanding models that aims to
improve efficiency of video processing by maximising throughput, minimising
latency, and reducing the number of clock cycles. Leveraging operation
pipelining and multi-rate clocks, these models perform a minimal amount of
computation (e.g. as few as four convolutional layers) for each frame per
timestep to produce an output. The models are still very deep, with dozens of
such operations being performed but in a pipelined fashion that enables
depth-parallel computation. We illustrate the proposed principles by applying
them to existing image architectures and analyse their behaviour on two video
tasks: action recognition and human keypoint localisation. The results show
that a significant degree of parallelism, and implicitly speedup, can be
achieved with little loss in performance.Comment: Fixed typos in densenet model definition in appendi
An experimental multiprocessor system for distributed parallel computations.
The availability of low-cost microprocessor chips with efficient instruction sets for specific numerical tasks (signal processors) has been exploited for building a versatile multiprocessor system, consisting of a host minicomputer augmented by a number of joint processors. The host provides a multiuser-multitasking environment and manages system resources and task scheduling. User applications can call upon one or more joint processors for parallel execution of adequately partitioned, computationally intensive numeric operations. Each joint processor has sufficient local memory for storing procedures and data and has access to regions in host memory for shared data. Kernel processes in the host and in the joint processors provide the necessary mechanism for initialization and synchronization of the distributed parallel execution of procedures
Self-Supervised Relative Depth Learning for Urban Scene Understanding
As an agent moves through the world, the apparent motion of scene elements is
(usually) inversely proportional to their depth. It is natural for a learning
agent to associate image patterns with the magnitude of their displacement over
time: as the agent moves, faraway mountains don't move much; nearby trees move
a lot. This natural relationship between the appearance of objects and their
motion is a rich source of information about the world. In this work, we start
by training a deep network, using fully automatic supervision, to predict
relative scene depth from single images. The relative depth training images are
automatically derived from simple videos of cars moving through a scene, using
recent motion segmentation techniques, and no human-provided labels. This proxy
task of predicting relative depth from a single image induces features in the
network that result in large improvements in a set of downstream tasks
including semantic segmentation, joint road segmentation and car detection, and
monocular (absolute) depth estimation, over a network trained from scratch. The
improvement on the semantic segmentation task is greater than those produced by
any other automatically supervised methods. Moreover, for monocular depth
estimation, our unsupervised pre-training method even outperforms supervised
pre-training with ImageNet. In addition, we demonstrate benefits from learning
to predict (unsupervised) relative depth in the specific videos associated with
various downstream tasks. We adapt to the specific scenes in those tasks in an
unsupervised manner to improve performance. In summary, for semantic
segmentation, we present state-of-the-art results among methods that do not use
supervised pre-training, and we even exceed the performance of supervised
ImageNet pre-trained models for monocular depth estimation, achieving results
that are comparable with state-of-the-art methods
Temporal coherence-based self-supervised learning for laparoscopic workflow analysis
In order to provide the right type of assistance at the right time,
computer-assisted surgery systems need context awareness. To achieve this,
methods for surgical workflow analysis are crucial. Currently, convolutional
neural networks provide the best performance for video-based workflow analysis
tasks. For training such networks, large amounts of annotated data are
necessary. However, collecting a sufficient amount of data is often costly,
time-consuming, and not always feasible. In this paper, we address this problem
by presenting and comparing different approaches for self-supervised
pretraining of neural networks on unlabeled laparoscopic videos using temporal
coherence. We evaluate our pretrained networks on Cholec80, a publicly
available dataset for surgical phase segmentation, on which a maximum F1 score
of 84.6 was reached. Furthermore, we were able to achieve an increase of the F1
score of up to 10 points when compared to a non-pretrained neural network.Comment: Accepted at the Workshop on Context-Aware Operating Theaters (OR
2.0), a MICCAI satellite even
Towards Pose-Invariant 2D Face Classification for Surveillance
A key problem for "face in the crowd" recognition from existing surveillance cameras in public spaces (such as mass transit centres) is the issue of pose mismatches between probe and gallery faces. In addition to accuracy, scalability is also important, necessarily limiting the complexity of face classification algorithms. In this paper we evaluate recent approaches to the recognition of faces at relatively large pose angles from a gallery of frontal images and propose novel adaptations as well as modifications. Specifically, we compare and contrast the accuracy, robustness and speed of an Active Appearance Model (AAM) based method (where realistic frontal faces are synthesized from non-frontal probe faces) against bag-of-features methods (which are local feature approaches based on block Discrete Cosine Transforms and Gaussian Mixture Models). We show a novel approach where the AAM based technique is sped up by directly obtaining pose-robust features, allowing the omission of the computationally expensive and artefact producing image synthesis step. Additionally, we adapt a histogram-based bag-of-features technique to face classification and contrast its properties to a previously proposed direct bag-of-features method. We also show that the two bag-of-features approaches can be considerably sped up, without a loss in classification accuracy, via an approximation of the exponential function. Experiments on the FERET and PIE databases suggest that the bag-of-features techniques generally attain better performance, with significantly lower computational loads. The histogram-based bag-of-features technique is capable of achieving an average recognition accuracy of 89% for pose angles of around 25 degrees
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