Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding

Brust, Clemens-Alexander; Sickert, Sven; Simon, Marcel; Rodner, Erik; Denzler, Joachim

doi:10.5220/0005355105100517

Cited by 80 publications

(66 citation statements)

References 22 publications

(51 reference statements)

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“…For eTRIMS dataset, we perform a 5-fold cross-validation as in (Yang and Förstner, 2011) mentioned by dividing 40 images into a training set and 20 images into a test set randomly. For LabelMeFacade dataset, we use the pre-separated training and testing as the same as (Fröhlich et al, 2010, Brust et al, 2015 mentioned. We compare our results with against (Jampani et al, 2015) and (Brust et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

“…Recently, convolutional patch network, which is one type of convolutional neural networks, is presented by (Brust et al, 2015). Since both the eTRIMS (Korč and Förstner, 2009) and LabelMeFacade (Fröhlich et al, 2010, Brust et al, 2015 image databases are relative small, this limit the classification and labeling ability of the convolutional patch networks.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…However these methods are not pixelwise labeling and not suitable for irregular man-made scene images, such The eTRIMS (Korč and Förstner, 2009) and LabelMeFacade (Fröhlich et al, 2010, Brust et al, 2015 datasets. Recently, convolutional patch network, which is one type of convolutional neural networks, is presented by (Brust et al, 2015). Since both the eTRIMS (Korč and Förstner, 2009) and LabelMeFacade (Fröhlich et al, 2010, Brust et al, 2015 image databases are relative small, this limit the classification and labeling ability of the convolutional patch networks.…”

Section: Related Workmentioning

confidence: 99%

“…Man-made scenes exhibit strong contextual and structural information in the form of spatial interactions among components, which may include buildings, cars, doors, pavements, roads, windows or vegetation. The eTRIMS (Korč and Förstner, 2009) and LabelMeFacade (Fröhlich et al, 2010, Brust et al, 2015 image databases are two popular dataset for man-made scene semantic segmentation, which have irregular facades and do not follow strong architectural principles. In this paper, we will explore semantic segmentation of this kind of man-made scenes using fully connected Conditional random fields (CRFs).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient Semantic Segmentation of Man-Made Scenes Using Fully-Connected Conditional Random Field

Li¹,

Yang²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Semantic Segmentation of Man-Made Scenes Using Fully-Connected Conditional Random Field

Li¹,

Yang²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Machine learning for next‐generation intelligent transportation systems: A survey

Yuan

Rocha

Rothenberg

et al. 2021

Trans Emerging Tel Tech

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Intelligent transportation systems, or ITS for short, includes a variety of services and applications such as road traffic management, traveler information systems, public transit system management, and autonomous vehicles, to name a few. ITS are expected to be an integral part of urban planning and future smart cities, contributing to improved road and traffic safety, transportation and transit efficiency, as well as to increased energy efficiency and reduced environmental pollution. On the other hand, ITS pose a variety of challenges due to its scalability and diverse quality‐of‐service needs, as well as the massive amounts of data it will generate. In this survey, we explore the use of machine learning (ML), which has recently gained significant traction, to enable ITS. We provide a thorough survey of the current state‐of‐the‐art of how ML technology has been applied to a broad range of ITS applications and services, such as cooperative driving and road hazard warning, and identify future directions for how ITS can further use and benefit from ML technology.

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Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

Bargoti

Underwood

2017

Journal of Field Robotics

384

205

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Ground vehicles equipped with monocular vision systems are a valuable source of high resolution image data for precision agriculture applications in orchards. This paper presents an image processing framework for fruit detection and counting using orchard image data. A general purpose image segmentation approach is used, including two feature learning algorithms; multi-scale Multi-Layered Perceptrons (MLP) and Convolutional Neural Networks (CNN). These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data. The pixel-wise fruit segmentation output is processed using the Watershed Segmentation (WS) and Circular Hough Transform (CHT) algorithms to detect and count individual fruits. Experiments were conducted in a commercial apple orchard near Melbourne, Australia. The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network. We extend this work with CNNs, bringing agrovision closer to the state-of-the-art in computer vision, where although metadata had negligible influence, the best pixel-wise F1-score of 0.791 was achieved. The WS algorithm produced the best apple detection and counting results, with a detection F1-score of 0.858. As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post-harvest fruit counts that were obtained from a grading and counting machine. The count estimates using CNN and WS resulted in the best performance for this dataset, with a squared correlation coefficient of r 2 = 0.826.

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Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding

Cited by 80 publications

References 22 publications

Efficient Semantic Segmentation of Man-Made Scenes Using Fully-Connected Conditional Random Field

Efficient Semantic Segmentation of Man-Made Scenes Using Fully-Connected Conditional Random Field

Machine learning for next‐generation intelligent transportation systems: A survey

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

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