ITR: Pattern Recognition for Ecological Science and Environmental Monitoring

生态科学和环境监测需要廉价的方法来获得昆虫的种群数量。 现有的方法需要人类专家进行鉴定，这对于支持大规模的科学和环境监测研究来说成本太高。 该项目正在开发一种机械/计算机设备，可以自动分离，拍照和分类昆虫，以产生昆虫种群计数。 目前正在研制这一装置，并就两个重要的环境问题进行测试：（通过识别和计数溪流基质中的石蛾幼虫）和（B）森林甲虫生物多样性的测量和表征（通过识别和计算土壤中型动物）。开发这样一种装置对模式识别提出了两个挑战：（a）开发能够识别柔性三维物体的方法，（B）开发能够简单地通过再训练应用于新昆虫组的通用方法。 为了应对这些挑战，该项目正在探索两种新的模式识别方法：（a）特征字典（从图像中发现和提取大量视觉特征的算法），以及（B）基于关系外观的方法（将对象分解为各个部分并了解各个部分在各种视角和照明条件下的外观的算法）。 一种低成本、高效率的监测溪流中昆虫种群的方法将彻底改变水质监测。 一种用于对中型土壤动物进行分类和计数的自动化装置将使获得种群计数所需的时间缩短1 000倍。 这将使广泛的生物学家能够在基础研究和环境监测中广泛采用生物多样性指标分析。

Ecological science and environmental monitoring need inexpensive methods for obtaining population counts of insects. Existing methods require identification by human experts, which is too costly to support large-scale scientific and environmental monitoring studies. This project is developing a mechanical/computer device that can automatically separate, photograph, and classify insects to produce insect population counts. This device is being developed and tested on two important environmental problems: (a) water quality monitoring in streams (by recognizing and counting stonefly larvae in stream substrates) and (b) measurement and characterization of forest beetle biodiversity (by recognizing and counting soil mesofauna).Developing such a device raises two challenges for pattern recognition: (a) develop methods that can recognize flexible 3-dimensional objects, and (b) develop general-purpose methods that can be applied to new sets of insects simply by retraining. To meet these challenges, the project is exploring two new approaches to pattern recognition: (a) feature dictionaries (algorithms that find and extract a large number of visual features from images), and (b) relational appearance-based methods (algorithms that decompose the object into its parts and learn what each part looks like under a wide variety of viewing angles and lighting conditions).This project has many potential impacts. A low-cost, efficient method for monitoring insect populations in streams would revolutionize water quality monitoring. An automated device for classifying and counting soil mesofauna would reduce the amount of time required to obtain population counts 1000-fold. This would allow a broad spectrum of biologists to employ biodiversity indicator analysis on a wide scale for basic research and environmental monitoring.