Robust Wide Baseline Stereo from Maximally Stable Extremal Regions

Matas, Jiřı́; Chum, Ondřej; Urban, M.; Pajdla, Tomáš

doi:10.5244/c.16.36

Cited by 1,990 publications

(1,860 citation statements)

References 16 publications

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“…The MSER method is a highly efficient, locally operating blob detection method mainly used in computer vision. The method was first described by Matas (Matas, 2002). It is working on panchromatic images, applying an ordered series of thresholds to the image data, which results in a sequence of binarizations.…”

Section: Methodsologymentioning

confidence: 99%

Automatic Sea Bird Detection From High Resolution Aerial Imagery

Mader¹,

Grenzdörffer²

2016

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

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ABSTRACT:Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted subimages. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.

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Section: Methodsologymentioning

confidence: 99%

Automatic Sea Bird Detection From High Resolution Aerial Imagery

Mader¹,

Grenzdörffer²

2016

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

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“…Using the segmented cell clumps as the initial search area, candidate nuclei with stable connected components were detected by the maximally stable extremal regions algorithm [21]. Nuclei that did not meet a minimum size constraint or had an eccentricity larger than 0.9 were treated as artefacts and not analysed.…”

Section: Cell Clumps and Nuclei Segmentationmentioning

confidence: 99%

A method for quantitative analysis of clump thickness in cervical cytology slides

Fan

Bradley

2016

Micron

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A c c e p t e d M a n u s c r i p tWe developed an algorithm to estimate the thickness of cervical cytology specimens.The proposed algorithm reached an accuracy of 1 micron at 90% of times.The algorithm was used to quantitatively analysis of ten normal Thin-prep cervical cytology slides.It was found that the distribution of cells is skewed towards the cover-slip (top of the slide).It was also proved that considering the thickness of focal points produced focus maps in superior qualities compared to conventional ones. Highlights (for review)Page 2 of 28 A c c e p t e d M a n u s c r i p t AbstractKnowledge of the spatial distribution and thickness of cytology specimens is critical to the development of digital slide acquisition techniques that minimise both scan times and image file size. In this paper, we evaluate a novel method to achieve this goal utilising an exhaustive high-resolution scan, an over-complete wavelet transform across multi-focal planes and a clump segmentation of all cellular material on the slide. The method is demonstrated with a quantitative analysis of ten normal, but difficult to scan Pap stained, Thin-prep, cervical cytology slides. We show that with this method the top and bottom of the specimen can be estimated to an accuracy of 1 micron in 88% and 97% of the fields of view respectively. Overall, cellar material can be over 30 microns thick and the distribution of cells is skewed towards the cover-slip (top of the slide). However, the median clump thickness is 10 microns and only 31% of clumps contain more than three nuclei. Therefore, by finding a focal map of the specimen the number of 1 micron spaced focal planes that are required to be scanned to acquire 95% of the in-focus material can be reduced from 25.4 to 21.4 on average. In addition, we show that by considering the thickness of the specimen, an improved focal map can be produced which further reduces the required number of 1 micron spaced focal planes to 18.6. This has the potential to reduce scan times and raw image data by over 25%.

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“…The basic strategy for detecting single transcripts as spots has been developed by Jiri Matas [32] and Arjun Raj and their colleagues [17]. After emphasizing spot-like signal by a Laplacian of Gaussian filter (Fig.…”

Section: Spot Detection and Correction Of Lens Aberrationsmentioning

confidence: 99%

Computer vision for image-based transcriptomics

Stoeger

Battich

Herrmann

et al. 2015

Methods

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Single-cell transcriptomics has recently emerged as one of the most promising tools for understanding the diversity of the transcriptome among single cells. Image-based transcriptomics is unique compared to other methods as it does not require conversion of RNA to cDNA prior to signal amplification and transcript quantification. Thus, its efficiency in transcript detection is unmatched by other methods. In addition, image-based transcriptomics allows the study of the spatial organization of the transcriptome in single cells at single-molecule, and, when combined with superresolution microscopy, nanometer resolution. However, in order to unlock the full power of image-based transcriptomics, robust computer vision of single molecules and cells is required. Here, we shortly discuss the setup of the experimental pipeline for image-based transcriptomics, and then describe in detail the algorithms that we developed to extract, at high-throughput, robust multivariate feature sets of transcript molecule abundance, localization and patterning in tens of thousands of single cells across the transcriptome. These computer vision algorithms and pipelines can be downloaded from: https://github.com/pelkmanslab/ImageBasedTranscriptomics. AbstractSingle-cell transcriptomics has recently emerged as one of the most promising tools for

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Robust Wide Baseline Stereo from Maximally Stable Extremal Regions

Cited by 1,990 publications

References 16 publications

Automatic Sea Bird Detection From High Resolution Aerial Imagery

Automatic Sea Bird Detection From High Resolution Aerial Imagery

A method for quantitative analysis of clump thickness in cervical cytology slides

Computer vision for image-based transcriptomics

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