Object identification and segmentation application requires extraction of object in foreground from the background. In this paper the Bhattacharya distance based probabilistic approach is utilized with an active contour model (ACM) to segment an object from the background. In the proposed approach, the Bhattacharya histogram is calculated on non-linear structure tensor space. Based on the histogram, new formulation of active contour model is proposed to segment images. The results are tested on both color and gray images from the Berkeley image database. The experimental results show that the proposed model is applicable to both color and gray images as well as both texture images and natural images. Again in comparing to the Bhattacharya based ACM in ICA space, the proposed model is able to segment multiple object too.
Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment" This repository contains the necessary codes to reproduce results in the paper: Baals, L. J., Liu, Y., Osterrieder, J., & Hadji-Misheva, B. (2025). A Syste…
developing a nature-inspired design framework for self-regulating urban parks: a digital twin-based model for intelligent landscape management parisa azimi1, sepideh habibpour mehraban2 1- M.Sc in Enviromental Design2- M.Sc in Enviromental Design Abstract Urban parks have faced i…
We develop a federal-level decision-support framework for managing a long-run transition to an economy in which artificial intelligence and inexpensive general-purpose robots can perform a substantial share of cognitive and physical tasks. The framework does not forecast the tech…
The Mixture of Experts (MoE) architecture has demonstrated high efficiency in scalinglarge neural network models. However, deploying sparse MoE models on computationalnodes with strict resource constraints entails critical latencies, high memory consumption,and computational redu…
Release v0.1.1 · oubino/locpix_points / oubino/locpix_points at v0.1.1 This software is for classification of point-cloud data based on the features and spatial arrangement of clusters within the data. It uses graph-based neural networks, taking the point coordinates and their as…