Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking
Recently, single-stage embedding based deep learning algorithms gain increasing attention
in cell segmentation and tracking. Compared with the traditional “segment-then-associate” …
in cell segmentation and tracking. Compared with the traditional “segment-then-associate” …
GPU-accelerated Faster Mean Shift with euclidean distance metrics
Handling clustering problems are important in data statistics, pattern recognition and image
processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used to …
processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used to …
Fast image dehazing using guided joint bilateral filter
C **ao, J Gan - The Visual Computer, 2012 - Springer
In this paper, we propose a new fast dehazing method from single image based on filtering.
The basic idea is to compute an accurate atmosphere veil that is not only smoother, but also …
The basic idea is to compute an accurate atmosphere veil that is not only smoother, but also …
Adaptive core fusion-based density peak clustering for complex data with arbitrary shapes and densities
A challenging issue of clustering in real-word application is to detect clusters with arbitrary
shapes and densities in complex data. Many conventional clustering algorithms are capable …
shapes and densities in complex data. Many conventional clustering algorithms are capable …
Tigris: Architecture and algorithms for 3d perception in point clouds
Machine perception applications are increasingly moving toward manipulating and
processing 3D point cloud. This paper focuses on point cloud registration, a key primitive of …
processing 3D point cloud. This paper focuses on point cloud registration, a key primitive of …
Fast shadow removal using adaptive multi‐scale illumination transfer
In this paper, we present a new method for removing shadows from images. First, shadows
are detected by interactive brushing assisted with a Gaussian Mixture Model. Secondly, the …
are detected by interactive brushing assisted with a Gaussian Mixture Model. Secondly, the …
MeanShift++: Extremely fast mode-seeking with applications to segmentation and object tracking
J Jang, H Jiang - Proceedings of the IEEE/CVF Conference …, 2021 - openaccess.thecvf.com
MeanShift is a popular mode-seeking clustering algorithm used in a wide range of
applications in machine learning. However, it is known to be prohibitively slow, with …
applications in machine learning. However, it is known to be prohibitively slow, with …
QuickFPS: Architecture and algorithm co-design for farthest point sampling in large-scale point clouds
Point clouds have been employed extensively in machine perception applications. Farthest
point sampling (FPS) is a critical kernel for point cloud processing. With the rapid growth of …
point sampling (FPS) is a critical kernel for point cloud processing. With the rapid growth of …
Segmentation for high-resolution optical remote sensing imagery using improved quadtree and region adjacency graph technique
G Fu, H Zhao, C Li, L Shi - Remote sensing, 2013 - mdpi.com
An approach based on the improved quadtree structure and region adjacency graph for the
segmentation of a high-resolution remote sensing image is proposed in this paper. In order …
segmentation of a high-resolution remote sensing image is proposed in this paper. In order …
Entropy-weighted medoid shift: An automated clustering algorithm for high-dimensional data
Unveiling the intrinsic structure within high-dimensional data presents a significant
challenge, particularly when clusters manifest themselves in lower-dimensional subspaces …
challenge, particularly when clusters manifest themselves in lower-dimensional subspaces …