Optimal separable interpolation of color images with bayer array format

In this paper new separable interpolation schemes for the channels of a single chip color camera are presented. The pixel grid of the camera is called "Bayer" pattern (compare [6] and fig. 1). As different colors are on different grids interpolation of all channels onto one common grid has to be performed. An analysis of the sampling scheme of this camera chip restricts the interpolation methods to filters on inter pixel positions in the middle of the processed color channel grids. Additionally this analysis defines the optimal resolution of the interpolated data which is of high interest concerning data compression. For this kind of interpolation optimal filters are presented that eliminate any possibility of phase errors. Thereby pixel values are supposed to be point samples of an intensity signal convolved by the average pixel area of the chip

A scheme for coherence-enhancing diffusion filtering with optimized rotation invariance

For strongly directed anisotropic processes such as coherence-enhancing diffusion filtering it is crucial to use numerical schemes with highly accurate directional behavior. We show that this is not possible in a satisfactory way when discretizations are limited to 3 x 3 stencils. As a consequence, we investigate a novel algorithm based on 5 x 5 stencils. It utilizes recently discovered differentiation filters with optimized rotation invariance. By juxtaposing it with several common algorithms we demonstrate its superior behavior with respect to the following properties: rotation invariance, avoidance of blurring artifacts (dissipativity), and accuracy. The latter one is evaluated by deriving an analytical solution for coherence-enhancing diffusion filtering of images with circular symmetry. Furthermore, we show that the new scheme is 3 to 4 times more efficient than explicit schemes on 3 x 3 stencils. It does not require to solve linear systems of equations, and it can be easily implemented in any dimension

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128x128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.Comment: 8 pages, 6 figures, 1 table, ICCV CVPPP Workshop 201

Optimale Operatoren in der Digitalen Bildverarbeitung

Eine neuartige Methode zur optimalen Wahl von Filteroperatoren wird vorgestellt. Dabei können sowohl einzelne Filter als auch Filterfamilien mit linearen und nichtlinearen Optimalitätskriterien mittels unterschiedlicher, gewichteter Normen im Wellenzahlraum behandelt werden. Es können Fließkomma- oder Festkommakoeffizienten von Filtern mit beliebigen Trägern optimiert werden. In verschiedenen Applikationsbeispielen werden Filter u.a. nach Isotropie, Rotationsinvarianz oder Betragsgenauigkeit optimiert und die Ergebnisse diskutiert. Zumeist sind Verminderungen der Fehler gegenüber üblichen Parameterwahlen um mehr als eine Größenordnung zu verzeichnen. Untersuchungen der bekannten Strukturtensormethode zeigen, daß durch den Einsatz optimaler Filter Verschiebungsschätzungen in zweierlei Hinsicht verbessert werden. Erstens werden Fehler um ca. zwei Größenordnungen vermindert, zweitens ist eine erhöhte Stabilität gegenüber Rauschen zu verzeichnen. Die gesteigerte Leistungsfähigkeit der Methode wird anhand einer Objektverfolgung demonstriert. Eine neue explizite Diskretisierung für anisotrope Diffusion, die optimale Filter verwendet, wird eingeführt und mit bekannten Schemata verglichen. Sie stellt sich gegenüber einer neuartigen analytischen Lösung kohärenzverstärkender Diffusion als 1.5 bis 2.5 Größenordnungen genauer heraus als das beste Vergleichsverfahren und übertrifft dieses visuell erheblich bei einer Rekonstruktionsaufgabe. Wegen der erhöhten Stabilität des Verfahrens bezüglich großer Zeitschrittweiten, ist es 3-4 mal schneller als andere explizite Schemata

3D Surface Reconstruction of Plant Seeds by Volume Carving: Performance and Accuracies

We describe a method for 3D reconstruction of plant seed surfaces, focusing on small seeds with diameters as small as 200 μm. The method considers robotized systems allowing single seed handling in order to rotate a single seed in front of a camera. Even though such systems feature high position repeatability, at sub-millimeter object scales, camera pose variations have to be compensated. We do this by robustly estimating the tool center point from each acquired image. 3D reconstruction can then be performed by a simple shape-from-silhouette approach. In experiments we investigate runtimes, theoretically achievable accuracy, experimentally achieved accuracy, and show as a proof of principle that the proposed method is well sufficient for 3D seed phenotyping purposes

The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool

Background Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure to characterize these structural properties is the leaf angle distribution, which in turn requires knowledge on the 3-dimensional single leaf surface. Despite a large number of 3-d sensors and methods only a few systems are applicable for fast and routine measurements in plants and natural canopies. A suitable approach is stereo imaging, which combines depth and color information that allows for easy segmentation of green leaf material and the extraction of plant traits, such as leaf angle distribution. Results We developed a software package, which provides tools for the quantification of leaf surface properties within natural canopies via 3-d reconstruction from stereo images. Our approach includes a semi-automatic selection process of single leaves and different modes of surface characterization via polygon smoothing or surface model fitting. Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations. We include a case study to demonstrate the functionality of our software. 48 images of small sugar beet populations (4 varieties) have been analyzed on the base of their leaf angle distribution in order to investigate seasonal, genotypic and fertilization effects on leaf angle distributions. We could show that leaf angle distributions change during the course of the season with all varieties having a comparable development. Additionally, different varieties had different leaf angle orientation that could be separated in principle component analysis. In contrast nitrogen treatment had no effect on leaf angles. Conclusions We show that a stereo imaging setup together with the appropriate image processing tools is capable of retrieving the geometric leaf surface properties of plants and canopies. Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management

Editorial: Computer vision in plant phenotyping and agriculture

[Abstract not available.

Machine Learning for Plant Phenotyping Needs Image Processing

We found the article by Singh et al. [1] extremely interesting because it introduces and showcases the utility of machine learning for high-throughput data-driven plant phenotyping. With this letter we aim to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what is suggested in [1], both in analyzing phenotyping data (e.g., to measure growth) and when providing effective feature extraction to be used by machine learning. Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2]

Классификация, сравнение и анализ систем подачи газового топлива для питания дизельных двигателей

In this paper we present a new method to implement a robust estimator: B-spline channel smoothing. We show that linear smoothing of channels is equivalent to a robust estimator, where we make use of the channel representation based upon quadratic B-splines. The linear decoding from B-spline channels allows to derive a robust error norm which is very similar to Tukey's biweight error norm. Using channel smoothing instead of iterative robust estimator implementations like non-linear diffusion, bilateral filtering, and mean-shift approaches is advantageous since channel smoothing is faster, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on non-linear spaces, such as orientation space. As an application, we implemented orientation smoothing and compared it to the other three approaches

The significance of image compression in plant phenotyping applications

We are currently witnessing an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected using complex automated procedures and are then post-processed to extract phenotyping-related information. In this article, we show that the image compression used in such procedures may compromise phenotyping results and this needs to be taken into account. We use three illuminating proof-of-concept experiments that demonstrate that compression (especially in the most common lossy JPEG form) affects measurements of plant traits and the errors introduced can be high. We also systematically explore how compression affects measurement fidelity, quantified as effects on image quality, as well as errors in extracted plant visual traits. To do so, we evaluate a variety of image-based phenotyping scenarios, including size and colour of shoots, leaf and root growth. To show that even visual impressions can be used to assess compression effects, we use root system images as examples. Overall, we find that compression has a considerable effect on several types of analyses (albeit visual or quantitative) and that proper care is necessary to ensure that this choice does not affect biological findings. In order to avoid or at least minimise introduced measurement errors, for each scenario, we derive recommendations and provide guidelines on how to identify suitable compression options in practice. We also find that certain compression choices can offer beneficial returns in terms of reducing the amount of data storage without compromising phenotyping results. This may enable even higher throughput experiments in the future