Semantic Image Segmentation Using Visible and Near-Infrared Channels

Salamati, Neda; Larlus, Diane; Süsstrunk, Sabine

doi:10.1007/978-3-642-33868-7_46

Cited by 32 publications

(18 citation statements)

References 22 publications

(30 reference statements)

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“…Namely, the UTM 30LX and URG 04LX which are using the laser emitting the wave of the length of 905nm and 785nm respectively. Both wavelengths are from Near-infrared (NIR) spectrum and are able to reflect the properties of the material surface after passing through certain colour layers [24]. This implies that the NIR band provides material based information, which facilitates terrain discrimination.…”

Section: Methodsmentioning

confidence: 99%

Terrain classification using Laser Range Finder

Walas

Nowicki

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents terrain classification method based on the intensity readings from Laser Range Finder. The classification is performed on the feature vectors obtained using statistical descriptors or Fourier Transform computed for the patches of the intensity map for each terrain sample. As a classifier Support Vector Machines were used. For the set of 12 terrains results of classification are reaching the level of 98% of the correctly recognized terrain samples. The proposed approach has a low computational cost, which is required for its real time applications. The article begins with the description of the experimental setup followed by the presentation of the proposed feature vectors for the registered intensity maps. Next, classification results, using introduced features, are given and compared to other approaches found in literature. At the end concluding remarks are given.978-1-4799-6934-0/14/$31.00

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Section: Methodsmentioning

confidence: 99%

Terrain classification using Laser Range Finder

Walas

Nowicki

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Thus, the only requirement is that both visible and NIR information is available. Combining visible and NIR images has been successfully used to improve various image processing and computer vision tasks, such as skin smoothing [10], high dynamic range image rendering [11], haze removal [12], [13], scene recognition [14], and semantic region labeling [15]. In addition, enabling simultaneous capture of both visible and NIR radiation with a single sensor [16], [17] is currently being researched.…”

Section: Introductionmentioning

confidence: 99%

Automatic and Accurate Shadow Detection Using Near-Infrared Information

Rüfenacht

Fredembach

Süsstrunk

2014

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We present a method to automatically detect shadows in a fast and accurate manner by taking advantage of the inherent sensitivity of digital camera sensors to the near-infrared (NIR) part of the spectrum. Dark objects, which confound many shadow detection algorithms, often have much higher reflectance in the NIR. We can thus build an accurate shadow candidate map based on image pixels that are dark both in the visible and NIR representations. We further refine the shadow map by incorporating ratios of the visible to the NIR image, based on the observation that commonly encountered light sources have very distinct spectra in the NIR band. The results are validated on a new database, which contains visible/NIR images for a large variety of real-world shadow creating illuminant conditions, as well as manually labelled shadow ground truth. Both quantitative and qualitative evaluations show that our method outperforms current state-of-the-art shadow detection algorithms in terms of accuracy and computational efficiency.

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“…In a digital camera, an interference filter, called hot-mirror, usually blocks the NIR. It has recently been shown, however, that retaining, instead of removing, the additional information offered by NIR and combining them with the visible representation of the scene improves the performance of several tasks in computer vision and computational photography, including semantic segmentation [1], skin smoothing [2], image enhancement [3,4], and video conference relighting [5]. All of these applications need RGB (red, green, and blue) and NIR channels of the scene.…”

Section: Introductionmentioning

confidence: 99%

“…Without the hot-mirror filter, a mixture of one color channel and NIR is captured at each spatial position on the sensor 1 . Hence, to have full resolution NIR and RGB images, we first need to separate the NIR and color channels in different pixels of the mosaiced image.…”

Section: Introductionmentioning

confidence: 99%

A novel compressive sensing approach to simultaneously acquire color and near-infrared images on a single sensor

Sadeghipoor

Lu²,

Süsstrunk

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Sensors of most digital cameras are made of silicon that is inherently sensitive to both the visible and near-infrared parts of the electromagnetic spectrum. In this paper, we address the problem of color and NIR joint acquisition. We propose a framework for the joint acquisition that uses only a single silicon sensor and a slightly modified version of the Bayer color-filter array that is already mounted in most color cameras. Implementing such a design for an RGB and NIR joint acquisition system requires minor changes to the hardware of commercial color cameras.One of the important differences between this design and the conventional color camera is the post-processing applied to the captured values to reconstruct full resolution images. By using a CFA similar to Bayer, the sensor records a mixture of NIR and one color channel in each pixel. In this case, separating NIR and color channels in different pixels is equivalent to solving an under-determined system of linear equations. To solve this problem, we propose a novel algorithm that uses the tools developed in the field of compressive sensing. Our method results in high-quality RGB and NIR images (the average PSNR of more than 30 dB for the reconstructed images) and shows a promising path towards RGB and NIR cameras.

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Semantic Image Segmentation Using Visible and Near-Infrared Channels

Cited by 32 publications

References 22 publications

Terrain classification using Laser Range Finder

Terrain classification using Laser Range Finder

Automatic and Accurate Shadow Detection Using Near-Infrared Information

A novel compressive sensing approach to simultaneously acquire color and near-infrared images on a single sensor

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