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Abstract: The morphological characteristics of retinal blood vessels play an essential role in the computer-assisted diagnosis of fundus-related diseases. In this paper, a retinal vessel segmentation ...
Abstract: Bin-picking is a significant task in industrial scenes, with a challenging problem of segmentation. However, industrial scenes usually involve textureless objects with clutter and occlusion, ...
Abstract: Multimodal remote sensing data substantially enhance semantic segmentation accuracy by providing complementary information across sensing modalities. However, fully exploiting and ...
Abstract: With the big data era, precision marketing in e-commerce has become ever more important in order to target prospective customers with high relevance. The present research focuses on applying ...
Abstract: K-Means clustering and Density-Based Spatial Clustering of Applications with Noise, both unsupervised clustering approaches have many applications. This paper performs a comparative analysis ...
I’M PEYTON FURTADO WYFF NEWS FOUR. PEYTON. THANK YOU. AND ONE ISSUE THAT THE NEXT GOVERNOR WILL HAVE TO CONTEND WITH IS THE FUTURE OF DATA CENTERS IN SOUTH CAROLINA. THIS COMES AS PROJECT SPARROW IS ...
COLUMBUS, Ga. (WTVM) - Columbus city leaders are in talks to bring a $5 billion data center to the area in what would be one of the largest technological investments in the city’s history. The ...
Abstract: The long-tail problem presents a significant challenge to the advancement of semantic segmentation in ultrahigh-resolution (UHR) satellite imagery. While previous efforts in UHR semantic ...
Abstract: Semantic segmentation is critical in remote sensing applications such as urban planning, disaster management, and environmental monitoring. However, segmenting complex satellite images ...
A team of researchers in Japan has developed an artificial intelligence tool called YORU that can identify specific animal behaviors in real time and immediately interact with the animals’ brain ...
Abstract: Traditional spatiotemporal data analysis often relies on predictive models that overlook causal relationships, making it difficult to identify true drivers and formulate effective ...
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