A physical model-based approach to detecting sky in photographic images

Luo, Jiebo; Etz, S.P.

doi:10.1109/83.988954

Cited by 56 publications

(7 citation statements)

References 7 publications

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“…In another approach, [5], a neural network is used to assign a score in [0..1], to each pixel. Pixels that belong to the sky must have high score.…”

Section: Region Based Methodsmentioning

confidence: 99%

A Parametric Algorithm for Skyline Extraction

Ayadi

Suta

Scuturici

et al. 2016

Advanced Concepts for Intelligent Vision Systems

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International audienceThis paper is dedicated to the problem of automatic skyline extraction in digital images. The study is motivated by the needs, expressed by urbanists, to describe in terms of geometrical features, the global shape created by man-made buildings in urban areas. Skyline extraction has been widely studied for navigation of Unmanned Aerial Vehicles (drones) or for geolocalization, both in natural and urban contexts. In most of these studies, the skyline is defined by the limit between sky and ground objects, and can thus be resumed to the sky segmentation problem in images. In our context, we need a more generic definition of skyline, which makes its extraction more complex and even variable. The skyline can be extracted for different depths, depending on the interest of the user (far horizon, intermediate buildings, near constructions , ...), and thus requires a human interaction. The main steps of our method are as follows: we use a Canny filter to extract edges and allow the user to interact with filter's parameters. With a high sensitivity , all the edges will be detected, whereas with lower values, only most contrasted contours will be kept by the filter. From the obtained edge map, an upper envelope is extracted, which is a disconnected approximation of the skyline. A graph is then constructed and a shortest path algorithm is used to link discontinuities. Our approach has been tested on several public domain urban and natural databases, and have proven to give better results that previously published methods

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“…In another approach, [5], a neural network is used to assign a score in [0..1], to each pixel. Pixels that belong to the sky must have high score.…”

Section: Region Based Methodsmentioning

confidence: 99%

A Parametric Algorithm for Skyline Extraction

Ayadi

Suta

Scuturici

et al. 2016

Advanced Concepts for Intelligent Vision Systems

View full text Add to dashboard Cite

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“…Some techniques have been proposed for statistical image modeling in the past [3][4][5][6][7][8][9][10][11][12][13][14]. The most prevalent framework for statistical image modeling is to use the Markov random field model [3][4][5][6][7][8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%

“…The major limitation of a Markov random field is that its complexity increases rapidly when a larger neighborhood of image pixels is incorporated to model the local joint statistics of the image pixels. Gaussian Markov random fields have also been used for texture image modeling and segmentation [12][13][14]. Srivastava et al have also proposed a universal analytical framework for statistical image modeling by considering non-Gaussian property of image statistics, i.e., high kurtosis, heavy tails, and sharp central cusps [9].…”

Section: Related Workmentioning

confidence: 99%

“…Following the same spirit of sentence parsing in speech recognition and natural language processing, Tu et al have integrated generative (top-down) methods and discriminative (bottom-up) methods for parsing images into constituent visual patterns [32]. Luo et al have also proposed an interesting approach by integrating statistical image modeling for sky detection [14], and good detection results are reported. Zhu et al have generalized some generative and discriminative techniques for statistical image modeling and applied them to conceptualize large amounts of visual patterns in natural images [13].…”

Section: Related Workmentioning

confidence: 99%

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Statistical modeling for automatic image indexing and retrieval

2016

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In this paper, a statistical modeling algorithm is developed to achieve automatic detection of object classes and image concepts via partial similarity matching. For a given image, its statistical image model is automatically learned by using a finite mixture model to approximate the distribution of its image pixels in the 10-dimensional feature space. Such statistical image modeling process can also achieve automatic image segmentation implicitly. To achieve more precise matching between the mixture components and the local distributions of the relevant image pixels, an adaptive EM algorithm is developed to simultaneously select the model structure (i.e., the optimal number of mixture components) and estimate the model parameters (i.e., locations and statistical properties of the mixture components) according to the local distributions of the relevant image pixels. For a given image concept or object class of interest, its statistical concept model is automatically learned from the statistical image models for the labeled training images. Finally, similarity matching for automatic detection of object classes and image concepts is treated as a partial model matching problem, i.e., matching between the statistical image model for a given test image and all the statistical concept models for the object classes and image concepts of interest. Our experimental results have demonstrated that our statistical modeling algorithm can achieve very competitive results on both automatic image segmentation and classification.

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“…In the past decades, some researchers attempted to utilize horizon line extraction methods to solve this problem [ 3 , 4 , 5 , 6 ]. However, they are not suitable for varied water scenarios, such as the situation of interception or disaster relief in maritime environments, or the situation of water quality monitoring or waste management inland.…”

Section: Introductionmentioning

confidence: 99%

Towards Smaller and Stronger: An Edge-Aware Lightweight Segmentation Approach for Unmanned Surface Vehicles in Water Scenarios

Han

Zhao

Luo

2023

Sensors

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The accurate detection and segmentation of accessible surface regions in water scenarios is one of the indispensable capabilities of surface unmanned vehicle systems. ‘Most existing methods focus on accuracy and ignore the lightweight and real-time demands. Therefore, they are not suitable for embedded devices, which have been wildly applied in practical applications.‘ An edge-aware lightweight water scenario segmentation method (ELNet), which establishes a lighter yet better network with lower computation, is proposed. ELNet utilizes two-stream learning and edge-prior information. Except for the context stream, a spatial stream is expanded to learn spatial details in low-level layers with no extra computation cost in the inference stage. Meanwhile, edge-prior information is introduced to the two streams, which expands the perspectives of pixel-level visual modeling. The experimental results are 45.21% in FPS, 98.5% in detection robustness, 75.1% in F-score on MODS benchmark, 97.82% in precision, and 93.96% in F-score on USV Inland dataset. It demonstrates that ELNet uses fewer parameters to achieve comparable accuracy and better real-time performance.

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A physical model-based approach to detecting sky in photographic images

Cited by 56 publications

References 7 publications

A Parametric Algorithm for Skyline Extraction

A Parametric Algorithm for Skyline Extraction

Statistical modeling for automatic image indexing and retrieval

Towards Smaller and Stronger: An Edge-Aware Lightweight Segmentation Approach for Unmanned Surface Vehicles in Water Scenarios

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