研究可以为奇怪的伙伴关系做出。Edgar Lobaton.是NC州立大学的电气和计算机工程研究员，专门从事机器人，传感器和计算机视觉。汤姆Marchitto.是研究史前海洋的铜博尔德的地质科学研究员，ritayan mitrawas a post-doc at NC State and is currently a research affiliate at CU Boulder. Divided by geography and discipline, they are nevertheless partners in an attempt to solve a tricky engineering challenge and advance our understanding of Earth’s oceans.
At the heart of their collaboration are foraminifera, or forams – organisms that have been abundant in marine environments for more than a million centuries. If you haven’t noticed them, that’s probably because they are very small; most are only a fraction of a millimeter wide, meaning you could fit about a hundred of them next to each other in a single inch.
这些福特是保护者 - 既不是植物也不是动物 - 但它们为自己创造了小炮弹。当他们死的时候，他们会留下那些贝壳。
通过研究这些贝壳或其化石，科学家可以为那些福特居住的海洋学习大量的大陆。例如，不同类型的福特种类在不同种类的海洋环境中茁壮成长，化学测量可以告诉科学家了解来自的科学家ocean’s chemistry to its temperature when the shell was being formed. This is the sort of work Marchitto’s lab is interested in.
But there’s a problem: all of those little shells are, well,小的。
训练有素的学生或工作人员必须花很长时间盯着显微镜才能识别和分类这些微观壳和化石。那就是在哪里罗布登的实验室进来。
洛比塔，Marchitto和Mitra正在共同努力，找到一种方法来自动化福特分类过程 - 节省时间，努力和（希望）加快解读这些线索的过程，以便我们的星球历史。
该团队最近收到了补助金from the National Science Foundation to pursue the work, and摘要对研究人员对他们的项目有一些问题。
摘要：RITAYAN如何三次最终伴随着合作伙伴？
ritayan mitra：可能，我要为这个奇怪的伙伴关系'责备'！去年年初我正在访问我的妻子，当时是UCLA的博士。她的实验室在古生美食过程中工作，我在显微镜下看到了她的实验室辛劳，长时间从海洋泥浆样品中挑选炮弹。作为一个兴趣对跨学科问题的地球科学家，我感到好奇。
从海洋沉积物挑选葫芦的过程形成了主要海洋发现的基础，但它仍然是极为费力和重复的。随着任何重复过程可以（和应该）是自动化的，我有兴趣了解是否有人尝试过这种自动化。文献调查显示了对这种自动化的一些初步研究，但其中大多数人使用神经网络看了几百种物种的分类。这种巨大数据集的神经网络的培训已经证明是极其困难的，因此，研究成功有限。然而，大多数海洋学研究往往只需要少量的比赛物种，我意识到，如果我们可以自动识别有限数量的物种，随后他们从泥浆样品中挑选（从未尝试过），然后，本身将是减少此类研究背后的时间和努力的巨大一步。
这种方法将绕过使用神经网络的瓶颈进行en Masse物种识别。相反，利用先进的计算机视觉和模式识别技术来识别一些重要物种的有限形态特征，这将是诀窍。如果成功，我们也可以扩展到包括其他物种。在这一点上，我知道我手里有一个有趣的项目的种子。
我详细研究了手动福拉姆拣选过程，并提出了可以构成这样的自动机的各种部件的粗略设计。然而，我的机器人知识只有到目前为止，我知道我必须与专家联系，讨论这样一个项目的可行性。最重要的是，我不确定在这种规模上的微操矩是否可能。那时我在北卡罗来纳州立大学，我与埃德加联系，把他的想法与他联系。
他很热情，他讨论了一些他已经参与过MicroScales的机器人操纵的一些项目。他讨论的项目吹过我的思想，我知道我只与合适的人联系。我们决定一起提出建议。不久之后，我与地球科学界的几个实验室联系，我认为对这种自动化的应用方有兴趣。当我搬到科罗拉多州的博尔德时，作为一个研究联盟，我与汤姆一起讨论了这个想法，他们蓬勃发展的古生社区实验室。他对我们正在发展的兴趣表现出敏锐的兴趣。随着汤姆的参与，我们得到了我们所需的提案的纪律处的专业知识和背景。
摘要：为什么这些福特斯有趣？我们能从他们那里学到什么？
汤姆Marchitto：我们人类是处于一个意外,global-scale climate ‘experiment’ due to our emission of greenhouse gases. To predict the outcomes of that experiment we need a better understanding of how Earth’s climate behaves when its energy balance is altered. Fortunately there are myriad examples in the geologic past of times when the climate responded to some perturbation, be it greenhouse gases, changes in Earth’s orbit, solar luminosity or massive volcanism. Our records of these past events usually come from cores driven into ice sheets, or sediments at the bottoms of lakes and oceans. Since the oceans cover about 70 percent of Earth’s surface and play such an enormous role in its climate, the ocean record is a very important piece of the puzzle. We use forams because they are ubiquitous and because the chemistry of their shells records the physical and chemical characteristics of the waters that they grew in. These tiny critters bear witness to such past properties as temperature, salinity, acidity and nutrient concentrations. In turn we use those properties to reconstruct ocean circulation and heat transport during past climate events.
摘要：How and where do you collect samples of forams? And how many forams are in a typical sample?
Marchitto：海洋沉积物核心使用海洋研究船只在世界各地。收集非常短的核心是相当低的技术，基本上涉及顶部重量的PVC管;虽然很长的核心需要海底钻孔。几乎每个核心都包含福特。来自其中一个核心的典型5立方厘米样品可能包含成千上万的壳。
摘要：使用常规方法处理牙稗样品需要多长时间？
Marchitto：This depends on the objective. If we need, say, 30 individuals of an abundant species, picking them might take tens of minutes. If the species is rare, as is often the case for benthic (bottom-dwelling) forams, it might take hours to find enough.
摘要：What’s your overall goal for this particular project?
Edgar Lobaton：我们的总体目标是开发一种能够识别不同种福特种类的便携式视觉系统。如果我们在此过程中成功，我们计划将此系统用作机器人平台的构建块，该机器人将能够自动识别，选择和排序Forams。视觉系统将包含受控定向照明，以捕获将用于执行所需物种识别的外观以及3-D形信息。
摘要：Does this project present particularly interesting engineering challenges?
洛比托：One of the key challenges for this problem is the design of features – which are essentially properties extracted from the images and shape models – that are useful for identifying forams. These features will be obtained based on the knowledge of experts (such as our collaborators), but we will also learn some features automatically from the data. That is, we will have to search through the datasets of images that we will be creating and find the piece of information that makes it possible to discriminate between the different species of forams. Another challenge is the integration of the information coming from images taken using various directional lighting sources. We are aiming to fuse all that information by building a 3-D representation of the forams.
摘要：您是否有任何想法如何获得技术以区分不同类型的福尔鸟类？这个过程可能看起来像什么？
Mitra：The overarching goal of this project is to a) automate identification of different types of forams and b) engage a robotic manipulation to pick the relevant species. At present, we are working on the identification part which involves pattern recognition. Each species has a unique set of structural feature and under proper lighting and orientation we can identify these species under the microscope (magnification 20X to 40X). The idea is to use a camera fixed to a microscope to take pictures of a sample containing several species and then use pattern recognition techniques to identify the target species by recognizing their uniquely identifiable structural features. Initially, we will only look at the most relevant (in terms of oceanographic abundance and scholarly interest) 5-6 species.
洛比托：从算法的角度来看，系统将需要人类用户首先培训系统。专家必须提供标记物种样本。然后，系统将利用专家指定的功能，并自动学习如何区分不同物种。一旦观察到足够的样品，系统就可以告诉我们它属于哪些物种并测量它是多么自信。
摘要：使用自动方法对Forams进行排序需要多长时间的目标是什么？
洛比托：根据我们的讨论，对这些物种的识别和采摘时间有很多可变性，从几秒钟到可能是一个小时。因此，在这一点上，我们对这个问题的答案感到舒服。这是我们将能够在以后更准确地量化的事情。机器人系统的一个优点之一（即使它只需要只要采摘）就是这种系统就能在不停止的情况下连续且一致地执行任务。
摘要：您在此处的工作是否适用于其他挑战或问题？
洛比托：From the engineering side, the techniques that we will develop will include the robust segmentation and classification. These approaches can be used for a number of applications including medical imaging (e.g., identification of cancerous tissues), and autonomous driving (e.g., identification of pedestrians and obstacles on the road).
摘要：这个项目的时间范围是多少？
洛比托：Our current award officially starts on Aug. 1, 2016 and lasts until July 31, 2018. During that time, we are planning to develop and validate our autonomous visual recognition system. After that, we are hoping to pursue further funding for developing the autonomous robotic system for picking and sorting of forams.
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