CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.

For automatic processing of point clouds their segmentation is one of the most important processes. The methods based on curvature and other higher level derivatives often lead to over segmentation, which later needs a lot of manual editing. We present a method for segmentation of point clouds using smoothness constraint, which finds smoothly connected areas in point clouds. It uses only local surface normals and point connectivity which can be enforced using either k-nearest or fixed distance neighbours. The presented method requires a small number of intuitive parameters, which provide a tradeoff between underand over-segmentation. The application of the presented algorithm on industrial point clouds shows its effectiveness compared to curvature based approaches.

https://www.isprs.org/proceedings/XXXVI/part5/paper/RABB_639.pdf

Segmentation of point clouds using smoothness constraints

. Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.

/pdf/a-review-ofpoint-clouds-segmentation-and-classification-5dsnxn6l9d.pdf

A review ofpoint clouds segmentation and classification algorithms

Ripe with possibilities offered by deep-learning techniques and useful in applications related to remote sensing, computer vision, and robotics, 3D point cloud semantic segmentation (PCSS) and point cloud segmentation (PCS) are attracting increasing interest. This article summarizes available data sets and relevant studies on recent developments in PCSS and PCS.

/pdf/linking-points-with-labels-in-3d-a-review-of-point-cloud-1venutor5l.pdf

Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation

Segmentation of mobile laser point clouds of urban scenes into objects is an important step for post-processing (e.g., interpretation) of point clouds. Point clouds of urban scenes contain numerous objects with significant size variability, complex and incomplete structures, and holes or variable point densities, raising great challenges for the segmentation of mobile laser point clouds. This paper addresses these challenges by proposing a shape-based segmentation method. The proposed method first calculates the optimal neighborhood size of each point to derive the geometric features associated with it, and then classifies the point clouds according to geometric features using support vector machines (SVMs). Second, a set of rules are defined to segment the classified point clouds, and a similarity criterion for segments is proposed to overcome over-segmentation. Finally, the segmentation output is merged based on topological connectivity into a meaningful geometrical abstraction. The proposed method has been tested on point clouds of two urban scenes obtained by different mobile laser scanners. The results show that the proposed method segments large-scale mobile laser point clouds with good accuracy and computationally effective time cost, and that it segments pole-like objects particularly well.

A shape-based segmentation method for mobile laser scanning point clouds

In this paper, we present the parallel edge-region-based segmentation algorithm targeted at reconfigurable MultiRing network. The algorithm is based on detection of edges in the image. The 3-D image is sliced to create equidepth contours (EDCs). Three types of critical points, corresponding to three types of edges: fold edges, semistep edges, and boundary edges, are extracted in parallel from various EDCs. A subset of edge pixels is extracted first using these critical points. The edges are grown in parallel from these pixels through application of edge masks. The parallel algorithm is targeted on the MultiRing network. Various broadcasting mechanisms for utilizing the MultiRing for various stages of the algorithm are discussed. The paper also discusses how the segmentation algorithm is mapped on the MultiRing topology.

Parallel Edge-Region-Based Segmentation Algorithm Targeted at Reconfigurable MultiRing Network

Preface -- Introduction to Deep Learning -- Basic Deep Learning Models -- Training Basic Deep Learning Models -- Optimising Deep Learning Models -- Application of Deep Learning in Classification -- Application of Deep Learning in Segmentation -- Application of Deep Learning in Face Recognition -- Application of Deep Learning in Fingerprint Recognition -- Author's Index.

Advances in Deep Learning

Purpose – The most commonly used approaches for cluster validation are based on indices but the majority of the existing cluster validity indices do not work well on data sets of different complexities. The purpose of this paper is to propose a new cluster validity index (ARSD index) that works well on all types of data sets. Design/methodology/approach – The authors introduce a new compactness measure that depicts the typical behaviour of a cluster where more points are located around the centre and lesser points towards the outer edge of the cluster. A novel penalty function is proposed for determining the distinctness measure of clusters. Random linear search-algorithm is employed to evaluate and compare the performance of the five commonly known validity indices and the proposed validity index. The values of the six indices are computed for all nc ranging from (nc min, nc max) to obtain the optimal number of clusters present in a data set. The data sets used in the experiments include shaped, Gaussian...

A new cluster validity index using maximum cluster spread based compactness measure

Elucidating the patterns hidden in gene expression data offers an opportunity for identifying co-expressed genes and biologically relevant grouping of genes. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the microarray data. A first step toward addressing this challenge is the use of clustering techniques. Validation of results obtained from a clustering algorithm is an important part of the clustering process. In this paper, we propose a new cluster validity index (ARPoints index) for the purpose of cluster validation. A new approach to determine the compactness measure and distinctness measure of clusters is presented. We revisit commonly known indices and conduct a thorough comparison of these indices with the proposed index and provide a summary of performance evaluation of different indices. Experimental results show that the proposed index performs better than the commonly known cluster validity indices.

A novel point density based validity index for clustering gene expression datasets

Autonomous mobile robot path planning is considered as a complex task in unknown, highly uncertain and dynamic environments. The presence of diverse and uncertain objects present in the environment makes the autonomous navigation of a mobile robot a difficult and expensive task. Artificial potential field (APF) provides a good and easy solution to the path planning problem resulting in a collision free navigation of a mobile robot from source to destination. One of the inherent problems in APF based path planning is the Local Minima problem. This paper provides a fuzzy based approach to path planning in addition to steering a robot out of local minima configuration with a comparative analysis of some non-fuzzy approaches.

M. Arif Wani

Papers

Parallel Edge-Region-Based Segmentation Algorithm Targeted at Reconfigurable MultiRing Network

Advances in Deep Learning

A new cluster validity index using maximum cluster spread based compactness measure

A novel point density based validity index for clustering gene expression datasets

A fuzzy based local minima avoidance path planning in autonomous robots