English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Understanding of images via features like edges plays a vital role in many image processing applications. However, obtaining an optimum edge detector that performs well in every possible imaging condition is still an open challenge to researchers. In this paper, a quantitative analysis of some significant state of art edge detection techniques such as Canny’s, Prewitt, Sobel, Laplacian of Gaussian, fuzzy based edge detection, wavelet based edge detector with hybrid edge detection technique is proposed based on the correspondence between their outcomes. The hybrid edge detection method utilizes fuzzy logic partitioning along with wavelet transformation to maintain a proper balance in the false detections i.e., false positives and false negatives rates and provides better tracking of edge information. Various subjective as well as objective quality measures are provided for quantitative analysis of edge detectors. The experimental results confirm that compared to other techniques the hybrid edge detection technique outperform in terms of edge detection accuracy exclusively when the images are corrupted by noises.

A Quantitative Performance Analysis of Edge Detectors with Hybrid Edge Detector