From a process development perspective, diamond growth via chemical vapor
deposition has made significant strides. However, challenges persist in
achieving high quality and large-area material production. These difficulties
include controlling conditions to maintain uniform growth rates for the entire
growth surface. As growth progresses, various factors or defect states emerge,
altering the uniform conditions. These changes affect the growth rate and
result in the formation of crystalline defects at the microscale. However,
there is a distinct lack of methods to identify these defect states and their
geometry using images taken during the growth process. This paper details
seminal work on defect segmentation pipeline using in-situ optical images to
identify features that indicate defective states that are visible at the
macroscale. Using a semantic segmentation approach as applied in our previous
work, these defect states and corresponding derivative features are isolated
and classified by their pixel masks. Using an annotation focused
human-in-the-loop software architecture to produce training datasets, with
modules for selective data labeling using active learning, data augmentations,
and model-assisted labeling, our approach achieves effective annotation
accuracy and drastically reduces the time and cost of labeling by orders of
magnitude. On the model development front, we found that deep learning-based
algorithms are the most efficient. They can accurately learn complex
representations from feature-rich datasets. Our best-performing model, based on
the YOLOV3 and DeeplabV3plus architectures, achieved excellent accuracy for
specific features of interest. Specifically, it reached 93.35% accuracy for
center defects, 92.83% for polycrystalline defects, and 91.98% for edge
defects.

通过利用原位光学图像识别可见的宏观缺陷状态特征，本文详细介绍了利用语义分割方法进行缺陷分割的方法，并采用基于 YOLOV3 和 DeeplabV3plus 架构的深度学习算法，在钻石生长的过程中取得了很好的准确性。

AI 引导下的缺陷检测技术模拟单晶金刚石生长

AI-Guided Defect Detection Techniques to Model Single Crystal Diamond  Growth

Accurate land use maps, describing the territory from an anthropic
utilisation point of view, are useful tools for land management and planning.
To produce them, the use of optical images alone remains limited. It is
therefore necessary to make use of several heterogeneous sources, each carrying
complementary or contradictory information due to their imperfections or their
different specifications. This study compares two different approaches i.e. a
pre-classification and a post-classification fusion approach for combining
several sources of spatial data in the context of land use classification. The
approaches are applied on authoritative land use data located in the Gers
department in the southwest of France. Pre-classification fusion, while not
explicitly modeling imperfections, has the best final results, reaching an
overall accuracy of 97% and a macro-mean F1 score of 88%.

准确的土地利用地图对土地管理和规划非常有用，为了产生准确的地图，需要利用多种来源的空间数据，通过预分类融合等多种方法来提高分类准确性。

土地利用分类的两种数据融合方法比较

Comparison of two data fusion approaches for land use classification

Combining synthetic aperture sonar (SAS) imagery with optical images for
underwater object classification has the potential to overcome challenges such
as water clarity, the stability of the optical image analysis platform, and
strong reflections from the seabed for sonar-based classification. In this
work, we propose this type of multi-modal combination to discriminate between
man-made targets and objects such as rocks or litter. We offer a novel
classification algorithm that overcomes the problem of intensity and object
formation differences between the two modalities. To this end, we develop a
novel set of geometrical shape descriptors that takes into account the
geometrical relation between the objects shadow and highlight. Results from
7,052 pairs of SAS and optical images collected during several sea experiments
show improved classification performance compared to the state-of-the-art for
better discrimination between different types of underwater objects. For
reproducibility, we share our database.

使用合成孔径声纳（SAS）图像和光学图像相结合的多模态组合来区分人工目标和岩石等对象，提出了一种创新的分类算法，可以解决两种模态之间强度和对象形成差异的问题，并使用一组新的几何形状描述符来获取影子和高光之间的几何关系，通过对 7,052 对 SAS 和光学图像的实验结果表明其在不同类型的水下目标区分方面相对于现有技术有更好的分类性能，并分享我们的数据库以实现可重复性。

结合 SAS 和光学转 SAS 图像的水下物体分类

Underwater object classification combining SAS and transferred  optical-to-SAS Imagery

More information leads to better decisions and predictions, right? Confirming
this hypothesis, several studies concluded that the simultaneous use of optical
and thermal images leads to better predictions in crowd counting. However, the
way multimodal models extract enriched features from both modalities is not yet
fully understood. Since the use of multimodal data usually increases the
complexity, inference time, and memory requirements of the models, it is
relevant to examine the differences and advantages of multimodal compared to
monomodal models. In this work, all available multimodal datasets for crowd
counting are used to investigate the differences between monomodal and
multimodal models. To do so, we designed a monomodal architecture that
considers the current state of research on monomodal crowd counting. In
addition, several multimodal architectures have been developed using different
multimodal learning strategies. The key components of the monomodal
architecture are also used in the multimodal architectures to be able to answer
whether multimodal models perform better in crowd counting in general.
Surprisingly, no general answer to this question can be derived from the
existing datasets. We found that the existing datasets hold a bias toward
thermal images. This was determined by analyzing the relationship between the
brightness of optical images and crowd count as well as examining the
annotations made for each dataset. Since answering this question is important
for future real-world applications of crowd counting, this paper establishes
criteria for a potential dataset suitable for answering whether multimodal
models perform better in crowd counting in general.

本文探讨了多模型与单模型在人群计数方面的差异，并发现现有数据集存在热成像的偏见，提出了一个潜在数据集的标准，以回答多模型是否在人群计数方面表现更好的问题。