A survey of recent advances in CNN-based single image crowd counting and density estimation

Automated density-based counting of FISH amplification signals for HER2 status assessment

BACKGROUND

Automated image analysis can make quantification of FISH signals in histological sections more efficient and reproducible. Current detection-based methods, however, often fail to accurately quantify densely clustered FISH signals.

METHODS

We propose a novel density-based approach to quantifying FISH signals. Instead of detecting individual signals, this approach quantifies FISH signals in terms of the integral over a density map predicted by Deep Learning. We apply the density-based approach to the task of counting and determining ratios of ERBB2 and CEN17 signals and compare it to common detection-based and area-based approaches.

RESULTS

The ratios determined by our approach were strongly correlated with results obtained by manual annotation of individual FISH signals (Pearson's r = 0.907). In addition, they were highly consistent with cutoff-scores determined by a pathologist (balanced concordance = 0.971). The density-based approach generally outperformed the other approaches. Its superiority was particularly evident in the presence of dense signal clusters.

CONCLUSIONS

The presented approach enables accurate and efficient automated quantification of FISH signals. Since signals in clusters can hardly be detected individually even by human observers, the density-based quantification performs better than detection-based approaches.

The Application of A-CNN in Crowd Counting of Scenic Spots

In places where people are concentrated, such as scenic spots, the statistical accuracy of existing crowd statistics algorithms is not enough. In order to solve this problem, a crowd counting algorithm based on adaptive convolution neural network (A-CNN) is proposed, which is based on video monitoring technology. The process of its pooling is dynamically adjusted according to different feature graphs. Then the pooled weights are adjusted adaptively according to the contents of each pooled domain. Therefore, CNN can extract more accurate features when processing different pooled domains under different iteration times, so as to achieve adaptive effect finally. The experimental results show that the proposed A-CNN algorithm has improved the recognition accuracy.

TasselNet: counting maize tassels in the wild via local counts regression network

BACKGROUND

Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations.

RESULTS

This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting (MTC) dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large margins and achieves the overall best counting performance, with a mean absolute error of 6.6 and a mean squared error of 9.6 averaged over 8 test sequences.

CONCLUSIONS

TasselNet can achieve robust in-field counting of maize tassels with a relatively high degree of accuracy. Our experimental evaluations also suggest several good practices for practitioners working on maize-tassel-like counting problems. It is worth noting that, though the counting errors have been greatly reduced by TasselNet, in-field counting of maize tassels remains an open and unsolved problem.