Counting Objects by Diffused Index: geometry-free and training-free approach

• URL: http://arxiv.org/abs/2110.08365v1
• Date: Fri, 15 Oct 2021 20:53:37 GMT
• Title: Counting Objects by Diffused Index: geometry-free and training-free approach
• Authors: Mengyi Tang (1), Maryam Yashtini (2), and Sung Ha Kang (1) ((1) Georgia Institute of Technology, (2) Georgetown University )
• Abstract summary: We propose diffusion-based, geometry-free, and learning-free methodologies to count the number of objects in images. The main idea is to represent each object by a unique index value regardless of its intensity or size. We present counting results in various applications such as biological cells, agriculture, concert crowd, and transportation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Counting objects is a fundamental but challenging problem. In this paper, we propose diffusion-based, geometry-free, and learning-free methodologies to count the number of objects in images. The main idea is to represent each object by a unique index value regardless of its intensity or size, and to simply count the number of index values. First, we place different vectors, refer to as seed vectors, uniformly throughout the mask image. The mask image has boundary information of the objects to be counted. Secondly, the seeds are diffused using an edge-weighted harmonic variational optimization model within each object. We propose an efficient algorithm based on an operator splitting approach and alternating direction minimization method, and theoretical analysis of this algorithm is given. An optimal solution of the model is obtained when the distributed seeds are completely diffused such that there is a unique intensity within each object, which we refer to as an index. For computational efficiency, we stop the diffusion process before a full convergence, and propose to cluster these diffused index values. We refer to this approach as Counting Objects by Diffused Index (CODI). We explore scalar and multi-dimensional seed vectors. For Scalar seeds, we use Gaussian fitting in histogram to count, while for vector seeds, we exploit a high-dimensional clustering method for the final step of counting via clustering. The proposed method is flexible even if the boundary of the object is not clear nor fully enclosed. We present counting results in various applications such as biological cells, agriculture, concert crowd, and transportation. Some comparisons with existing methods are presented.

Related papers

• Linear time Evidence Accumulation Clustering with KMeans [0.0]
  This work describes a trick which mimic the behavior of average linkage clustering. We found a way of computing efficiently the density of a partitioning, reducing the cost from a quadratic to linear complexity. The k-means results are comparable to the best state of the art in terms of NMI while keeping the computational cost low.
  arXiv  Detail & Related papers  (2023-11-15T14:12:59Z)
• Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO) [1.8749305679160362]
  We propose a new method to construct a binary or a probabilistic consensus segmentation based on the Fr'echet means of carefully chosen distances. We show that it leads to binary consensus masks of intermediate size between Majority Voting and STAPLE and to different posterior probabilities than Mask Averaging and STAPLE methods.
  arXiv  Detail & Related papers  (2023-09-14T23:28:58Z)
• PaVa: a novel Path-based Valley-seeking clustering algorithm [13.264374632165776]
  We propose a novel Path-based Valley-seeking clustering algorithm for arbitrarily shaped clusters. Three vital techniques are used in this algorithm. The results indicate that the Path-based Valley-seeking algorithm is accurate and efficient.
  arXiv  Detail & Related papers  (2023-06-13T02:29:34Z)
• A Computational Theory and Semi-Supervised Algorithm for Clustering [0.0]
  A semi-supervised clustering algorithm is presented. The kernel of the clustering method is Mohammad's anomaly detection algorithm. Results are presented on synthetic and realworld data sets.
  arXiv  Detail & Related papers  (2023-06-12T09:15:58Z)
• Rethinking k-means from manifold learning perspective [122.38667613245151]
  We present a new clustering algorithm which directly detects clusters of data without mean estimation. Specifically, we construct distance matrix between data points by Butterworth filter. To well exploit the complementary information embedded in different views, we leverage the tensor Schatten p-norm regularization.
  arXiv  Detail & Related papers  (2023-05-12T03:01:41Z)
• GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing [78.92205914422925]
  We propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST) We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority" Experimental results on several data sets demonstrate the power of the algorithm.
  arXiv  Detail & Related papers  (2023-03-02T09:04:35Z)
• PointInst3D: Segmenting 3D Instances by Points [136.7261709896713]
  We propose a fully-convolutional 3D point cloud instance segmentation method that works in a per-point prediction fashion. We find the key to its success is assigning a suitable target to each sampled point. Our approach achieves promising results on both ScanNet and S3DIS benchmarks.
  arXiv  Detail & Related papers  (2022-04-25T02:41:46Z)
• SOLO: A Simple Framework for Instance Segmentation [84.00519148562606]
  "instance categories" assigns categories to each pixel within an instance according to the instance's location. "SOLO" is a simple, direct, and fast framework for instance segmentation with strong performance. Our approach achieves state-of-the-art results for instance segmentation in terms of both speed and accuracy.
  arXiv  Detail & Related papers  (2021-06-30T09:56:54Z)
• Estimating leverage scores via rank revealing methods and randomization [50.591267188664666]
  We study algorithms for estimating the statistical leverage scores of rectangular dense or sparse matrices of arbitrary rank. Our approach is based on combining rank revealing methods with compositions of dense and sparse randomized dimensionality reduction transforms.
  arXiv  Detail & Related papers  (2021-05-23T19:21:55Z)
• Finding Geometric Models by Clustering in the Consensus Space [61.65661010039768]
  We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects.
  arXiv  Detail & Related papers  (2021-03-25T14:35:07Z)
• Clustering with Penalty for Joint Occurrence of Objects: Computational Aspects [0.0]
  The method of Hol'y, Sokol and vCern'y clusters objects based on their incidence in a large number of given sets. The idea is to minimize the occurrence of multiple objects from the same cluster in the same set. In the current paper, we study computational aspects of the method.
  arXiv  Detail & Related papers  (2021-02-02T10:39:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.