A novel approach for color image segmentation using iterative partitioning mean shift clustering algorithm

Naik, Pavankumar; Gopal, T. Venu

doi:10.1109/iccsp.2015.7322768

Cited by 5 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A novel approach, called the Iterative Partitioning-Mean Shift (IP-MS) was introduced by Naik and colleagues [ 41 ], where the number of centroids chosen for each cluster and the number of iterations are key parameters for image segmentation. The image was pre-processed by reducing the noise, transforming the RGB image to a LAB color space, and normalizing the pixel values.…”

Section: Related Work On Computer Vision and Image Clusteringmentioning

confidence: 99%

An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach

Arjoune

Peri

Sugunaraj

et al. 2021

Sensors

View full text Add to dashboard Cite

Heat loss quantification (HLQ) is an essential step in improving a building’s thermal performance and optimizing its energy usage. While this problem is well-studied in the literature, most of the existing studies are either qualitative or minimally driven quantitative studies that rely on localized building envelope points and are, thus, not suitable for automated solutions in energy audit applications. This research work is an attempt to fill this gap of knowledge by utilizing intensive thermal data (on the order of 100,000 plus images) and constitutes a relatively new area of analysis in energy audit applications. Specifically, we demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an unmanned aerial system (UAS). To quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value). The proposed model was applied to eleven academic campuses across the state of North Dakota. The preliminary findings indicate that Mask R-CNN outperformed other instance segmentation models with an mIOU of 73% for facades, 55% for windows, 67% for roofs, 24% for doors, and 11% for HVACs. Two clustering methods, namely K-means and threshold-based clustering (TBC), were deployed to estimate surface temperatures with TBC providing consistent estimates across all times of the day over K-means. Our analysis demonstrated that thermal efficiency not only depended on the accurate acquisition of thermal images but also relied on other factors, such as the building geometry and seasonal weather parameters, such as the outside/inside building temperatures, wind, time of day, and indoor heating/cooling conditions. Finally, the resultant U-values of various building envelopes were compared with recommendations from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards.

show abstract

Section: Related Work On Computer Vision and Image Clusteringmentioning

confidence: 99%