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Artificial intelligence is nowadays used for cell detection and classification in optical microscopy during post‐acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real‐time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high‐dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non‐linear‐classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time‐consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature‐group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4% accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5‐fold cross‐validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM‐based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general‐purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy. [ABSTRACT FROM AUTHOR]

Image-based approaches to plant phenotyping are gaining momentum providing fertile ground for several interesting vision tasks where fine-grained categorization is necessary, such as leaf segmentation among a variety of cultivars, and cultivar (or mutant) identification. However, benchmark data focusing on typical imaging situations and vision tasks are still lacking, making it difficult to compare existing methodologies. This paper describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants. We briefly describe plant material, imaging setup and procedures for different experiments: one with various cultivars of Arabidopsis and one with tobacco undergoing different treatments. We proceed to define a set of computer vision and classification tasks and provide accompanying datasets and annotations based on our raw data. We describe the annotation process performed by experts and discuss appropriate evaluation criteria. We also offer exemplary use cases and results on some tasks obtained with parts of these data. We hope with the release of this rigorous dataset collection to invigorate the development of algorithms in the context of plant phenotyping but also provide new interesting datasets for the general computer vision community to experiment on. Data are publicly available at http://www.plant-phenotyping.org/datasets . [ABSTRACT FROM AUTHOR]

Abstract: Online learning has shown to be successful in tracking-by-detection of previously unknown objects. However, most approaches are limited to a bounding-box representation with fixed aspect ratio and cannot handle highly non-rigid and articulated objects. Moreover, they provide only a limited foreground/background separation, which in turn, increases the amount of noise introduced during online self-training. To overcome the limitations of a rigid bounding box, we present a novel tracking-by-detection approach based on the generalized Hough-transform. We extend the idea of Hough Forests to the online domain and couple the voting-based detection and back-projection with a rough GrabCut segmentation. Because of the increased granularity of the object description the amount of noisy training samples during online learning is reduced significantly which prevents drifting of the tracker. To show the benefits of our approach, we demonstrate it for a variety of previously unknown objects even under heavy non-rigid transformations, partial occlusions, scale changes, and rotations. Moreover, we compare our tracker to state-of-the-art methods (bounding-box-based as well as part-based) and show robust and accurate tracking results on various challenging sequences. [Copyright &y& Elsevier]