Local phase quantization for blur-insensitive image analysis

Rahtu, Esa; Heikkilä, Janne; Ojansivu, Ville; Ahonen, Timo

doi:10.1016/j.imavis.2012.04.001

Cited by 111 publications

(72 citation statements)

References 40 publications

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“…An alternative approach for blur detection, which does not use edges directly, involves analysing the image in the frequency domain (Rahtu et al, 2012). An image can be represented as a 2D function and described by its frequencies (OpenCV Dev Team, 2014).…”

Section: Blur Detection Based On Frequency Analysismentioning

confidence: 99%

Automatic detection of blurred images in UAV image sets

Sieberth

Wackrow

Chandler

2016

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Blur Detection Based On Frequency Analysismentioning

confidence: 99%

Automatic detection of blurred images in UAV image sets

Sieberth

Wackrow

Chandler

2016

ISPRS Journal of Photogrammetry and Remote Sensing

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“…This edge smear due to blur is also used in image frequency analysis methods. When an image has extensively smeared edges in the spatial domain, it is characterised by the disappearance of high frequencies in the frequency domain (Liu et al, 2008;Rahtu et al, 2012). A problem with most blur detection methods is the presumption that the image is blurred and that these methods are developed using mathematically blurred images without any relation to geometrical motion blur or random hand held camera shake (Levin et al, 2009).…”

Section: Related Workmentioning

confidence: 99%

Uav Image Blur – Its Influence and Ways to Correct It

Sieberth

Wackrow

Chandler

2015

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

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ABSTRACT:Unmanned aerial vehicles (UAVs) have become an interesting and active research topic in photogrammetry. Current research is based on image sequences acquired by UAVs which have a high ground resolution and good spectral resolution due to low flight altitudes combined with a high-resolution camera. One of the main problems preventing full automation of data processing of UAV imagery is the unknown degradation effect of blur caused by camera movement during image acquisition.The purpose of this paper is to analyse the influence of blur on photogrammetric image processing, the correction of blur and finally, the use of corrected images for coordinate measurements. It was found that blur influences image processing significantly and even prevents automatic photogrammetric analysis, hence the desire to exclude blurred images from the sequence using a novel filtering technique. If necessary, essential blurred images can be restored using information of overlapping images of the sequence or a blur kernel with the developed edge shifting technique. The corrected images can be then used for target identification, measurements and automated photogrammetric processing.

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“…These approaches detect a region of interest, i.e., the face, then extract features within the region using either appearance-based low-level descriptors [28,35,15] or facial landmark point detectors [4,5]. Recently, Liu et al [22] proposed an alternative approach by automatically learning action unit (AU)-like image features using deep learning (instead of trying to detect AUs directly).…”

Section: Related Workmentioning

confidence: 99%

Learning a sparse codebook of facial and body microexpressions for emotion recognition

Song¹,

Morency²,

Davis³

2013

Proceedings of the 15th ACM on International Conference on Multimodal Interaction

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Obtaining a compact and discriminative representation of facial and body expressions is a difficult problem in emotion recognition. Part of the difficulty is capturing microexpressions, i.e., short, involuntary expressions that last for only a fraction of a second: at a micro-temporal scale, there are so many other subtle face and body movements that do not convey semantically meaningful information. We present a novel approach to this problem by exploiting the sparsity of the frequent micro-temporal motion patterns. Local space-time features are extracted over the face and body region for a very short time period, e.g., few milliseconds. A codebook of microexpressions is learned from the data and used to encode the features in a sparse manner. This allows us to obtain a representation that captures the most salient motion patterns of the face and body at a micro-temporal scale. Experiments performed on the AVEC 2012 dataset show our approach achieving the best published performance on the expectation dimension based solely on visual features. We also report experimental results on audio-visual emotion recognition, comparing early and late data fusion techniques.

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Local phase quantization for blur-insensitive image analysis

Cited by 111 publications

References 40 publications

Automatic detection of blurred images in UAV image sets

Automatic detection of blurred images in UAV image sets

Uav Image Blur – Its Influence and Ways to Correct It

Learning a sparse codebook of facial and body microexpressions for emotion recognition

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