147 research outputs found
Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras
Cameras are the primary sensor in automated driving systems. They provide
high information density and are optimal for detecting road infrastructure cues
laid out for human vision. Surround-view camera systems typically comprise of
four fisheye cameras with 190{\deg}+ field of view covering the entire
360{\deg} around the vehicle focused on near-field sensing. They are the
principal sensors for low-speed, high accuracy, and close-range sensing
applications, such as automated parking, traffic jam assistance, and low-speed
emergency braking. In this work, we provide a detailed survey of such vision
systems, setting up the survey in the context of an architecture that can be
decomposed into four modular components namely Recognition, Reconstruction,
Relocalization, and Reorganization. We jointly call this the 4R Architecture.
We discuss how each component accomplishes a specific aspect and provide a
positional argument that they can be synergized to form a complete perception
system for low-speed automation. We support this argument by presenting results
from previous works and by presenting architecture proposals for such a system.
Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.Comment: Accepted for publication at IEEE Transactions on Intelligent
Transportation System
Fast and Efficient Scene Categorization for Autonomous Driving using VAEs
Scene categorization is a useful precursor task that provides prior knowledge
for many advanced computer vision tasks with a broad range of applications in
content-based image indexing and retrieval systems. Despite the success of data
driven approaches in the field of computer vision such as object detection,
semantic segmentation, etc., their application in learning high-level features
for scene recognition has not achieved the same level of success. We propose to
generate a fast and efficient intermediate interpretable generalized global
descriptor that captures coarse features from the image and use a
classification head to map the descriptors to 3 scene categories: Rural, Urban
and Suburban. We train a Variational Autoencoder in an unsupervised manner and
map images to a constrained multi-dimensional latent space and use the latent
vectors as compact embeddings that serve as global descriptors for images. The
experimental results evidence that the VAE latent vectors capture coarse
information from the image, supporting their usage as global descriptors. The
proposed global descriptor is very compact with an embedding length of 128,
significantly faster to compute, and is robust to seasonal and illuminational
changes, while capturing sufficient scene information required for scene
categorization.Comment: Published in the 24th Irish Machine Vision and Image Processing
Conference (IMVIP 2022
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging
Hyperspectral imaging has the potential to improve intraoperative decision
making if tissue characterisation is performed in real-time and with
high-resolution. Hyperspectral snapshot mosaic sensors offer a promising
approach due to their fast acquisition speed and compact size. However, a
demosaicking algorithm is required to fully recover the spatial and spectral
information of the snapshot images. Most state-of-the-art demosaicking
algorithms require ground-truth training data with paired snapshot and
high-resolution hyperspectral images, but such imagery pairs with the exact
same scene are physically impossible to acquire in intraoperative settings. In
this work, we present a fully unsupervised hyperspectral image demosaicking
algorithm which only requires exemplar snapshot images for training purposes.
We regard hyperspectral demosaicking as an ill-posed linear inverse problem
which we solve using a deep neural network. We take advantage of the spectral
correlation occurring in natural scenes to design a novel inter spectral band
regularisation term based on spatial gradient consistency. By combining our
proposed term with standard regularisation techniques and exploiting a standard
data fidelity term, we obtain an unsupervised loss function for training deep
neural networks, which allows us to achieve real-time hyperspectral image
demosaicking. Quantitative results on hyperspetral image datasets show that our
unsupervised demosaicking approach can achieve similar performance to its
supervised counter-part, and significantly outperform linear demosaicking. A
qualitative user study on real snapshot hyperspectral surgical images confirms
the results from the quantitative analysis. Our results suggest that the
proposed unsupervised algorithm can achieve promising hyperspectral
demosaicking in real-time thus advancing the suitability of the modality for
intraoperative use
Revisiting Modality Imbalance In Multimodal Pedestrian Detection
Multimodal learning, particularly for pedestrian detection, has recently
received emphasis due to its capability to function equally well in several
critical autonomous driving scenarios such as low-light, night-time, and
adverse weather conditions. However, in most cases, the training distribution
largely emphasizes the contribution of one specific input that makes the
network biased towards one modality. Hence, the generalization of such models
becomes a significant problem where the non-dominant input modality during
training could be contributing more to the course of inference. Here, we
introduce a novel training setup with regularizer in the multimodal
architecture to resolve the problem of this disparity between the modalities.
Specifically, our regularizer term helps to make the feature fusion method more
robust by considering both the feature extractors equivalently important during
the training to extract the multimodal distribution which is referred to as
removing the imbalance problem. Furthermore, our decoupling concept of output
stream helps the detection task by sharing the spatial sensitive information
mutually. Extensive experiments of the proposed method on KAIST and UTokyo
datasets shows improvement of the respective state-of-the-art performance.Comment: 5 pages, 3 figure, 4 table
Tumour inflammatory infiltrate predicts survival following curative resection for node-negative colorectal cancer
<b>Background</b>: A pronounced tumour inflammatory infiltrate is known to confer a good outcome in colorectal cancer. Klintrup and colleagues reported a structured assessment of the inflammatory reaction at the invasive margin scoring low grade or high grade. The aim of the present study was to examine the prognostic value of tumour inflammatory infiltrate in node-negative colorectal cancer.
<b>Methods</b>: Two hundred patients had undergone surgery for node-negative colorectal cancer between 1997 and 2004. Specimens were scored with Jass’ and Klintrup’s criteria for peritumoural infiltrate. Pathological data were taken from the reports at that time.
<b>Results</b>: Low-grade inflammatory infiltrate assessed using Klintrup’s criteria was an independent prognostic factor in node-negative disease. In patients with a low-risk Petersen Index (n = 179), low-grade infiltrate carried a threefold increased risk of cancer death. Low-grade infiltrate was related to increasing T stage and an infiltrating margin.
<b>Conclusion</b>: Assessment of inflammatory infiltrate using Klintrup’s criteria provides independent prognostic information on node-negative colorectal cancer. A high-grade local inflammatory response may represent effective host immune responses impeding tumour growth
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