23,360 research outputs found
Shadow Optimization from Structured Deep Edge Detection
Local structures of shadow boundaries as well as complex interactions of
image regions remain largely unexploited by previous shadow detection
approaches. In this paper, we present a novel learning-based framework for
shadow region recovery from a single image. We exploit the local structures of
shadow edges by using a structured CNN learning framework. We show that using
the structured label information in the classification can improve the local
consistency of the results and avoid spurious labelling. We further propose and
formulate a shadow/bright measure to model the complex interactions among image
regions. The shadow and bright measures of each patch are computed from the
shadow edges detected in the image. Using the global interaction constraints on
patches, we formulate a least-square optimization problem for shadow recovery
that can be solved efficiently. Our shadow recovery method achieves
state-of-the-art results on the major shadow benchmark databases collected
under various conditions.Comment: 8 pages. CVPR 201
Moving cast shadows detection methods for video surveillance applications
Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (’shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows).Peer Reviewe
Ergonomics of the Operative Field in Paediatric Minimal Access Surgery
Imperial Users onl
A statistical approach for shadow detection using spatio-temporal contexts
Background subtraction is an important step used to segment moving regions in surveillance videos. However, cast shadows are often falsely labeled as foreground objects, which may severely degrade the accuracy of object localization and detection. Effective shadow detection is necessary for accurate foreground segmentation, especially for outdoor scenes. Based on the characteristics of shadows, such as luminance reduction, chromaticity consistency and texture consistency, we introduce a nonparametric framework for modeling surface behavior under cast shadows. To each pixel, we assign a potential shadow value with a confidence weight, indicating the probability that the pixel location is an actual shadow point. Given an observed RGB value for a pixel in a new frame, we use its recent spatio-temporal context to compute an expected shadow RGB value. The similarity between the observed and the expected shadow RGB values determines whether a pixel position is a true shadow. Experimental results show the performance of the proposed method on a suite of standard indoor and outdoor video sequences
Cast shadow modelling and detection
Computer vision applications are often confronted by the need to differentiate between objects and their shadows. A number of shadow detection algorithms have been
proposed in literature, based on physical, geometrical, and other heuristic techniques.
While most of these existing approaches are dependent on the scene environments and
object types, the ones that are not, are classified as superior to others conceptually
and in terms of accuracy. Despite these efforts, the design of a generic, accurate,
simple, and efficient shadow detection algorithm still remains an open problem. In
this thesis, based on a physically-derived hypothesis for shadow identification, novel,
multi-domain shadow detection algorithms are proposed and tested in the spatial and
transform domains.
A novel "Affine Shadow Test Hypothesis" has been proposed, derived, and validated
across multiple environments. Based on that, several new shadow detection algorithms
have been proposed and modelled for short-duration video sequences, where
a background frame is available as a reliable reference, and for long duration video
sequences, where the use of a dedicated background frame is unreliable. Finally, additional
algorithms have been proposed to detect shadows in still images, where the
use of a separate background frame is not possible. In this approach, the author
shows that the proposed algorithms are capable of detecting cast, and self shadows
simultaneously.
All proposed algorithms have been modelled, and tested to detect shadows in the
spatial (pixel) and transform (frequency) domains and are compared against state-of-art approaches, using popular test and novel videos, covering a wide range of
test conditions. It is shown that the proposed algorithms outperform most existing
methods and effectively detect different types of shadows under various lighting and
environmental conditions
A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning
Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to queue estimation, speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a total of 70 research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the preferable way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Networks (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach
Black holes with scalar hair in light of the Event Horizon Telescope
Searching for violations of the no-hair theorem (NHT) is a powerful way to
test gravity, and more generally fundamental physics, particularly with regards
to the existence of additional scalar fields. The first observation of a black
hole (BH) shadow by the Event Horizon Telescope (EHT) has opened a new direct
window onto tests of gravity in the strong-field regime, including probes of
violations of the NHT. We consider two scenarios described by the
Einstein-Maxwell equations of General Relativity and electromagnetism, to which
we add a scalar field. In the first case we consider a minimally-coupled scalar
field with a potential, whereas in the second case the field is
conformally-coupled to curvature. In both scenarios we construct charged BH
solutions, which are found to carry primary scalar hair. We then compute the
shadows cast by these two BHs as a function of their electric charge and scalar
hair parameter. Comparing these shadows to the shadow of M87* recently imaged
by the EHT collaboration, we set constraints on the amount of scalar hair
carried by these two BHs. The conformally-coupled case admits a regime for the
hair parameter, compatible with EHT constraints, describing a so-called mutated
Reissner-Nordstr\"{o}m BH: this solution was recently found to effectively
mimic a wormhole. Our work provides novel constraints on fundamental physics,
and in particular on violations of the no-hair theorem and the existence of
additional scalar fields, from the shadow of M87*.Comment: 33 pages, 6 figures, 1 table, references added, version accepted for
publication in JCA
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