In this study, a hybrid active contour model driven by pre-fitting energy with an adaptive
edge indicator function and an adaptive sign function is proposed. The key idea of …

In the last decade, researchers have focused more on deep convolutional neural networks
(CNNs) than other machine learning algorithms for object detection, localization …

The objects in natural images are often texturally inhomogeneous and prone to be falsely
segmented into different parts by conventional methods. To overcome the difficulties caused …

Active contour model (ACM) is considered as a feasible tool to handle image segmentation
problems via an unsupervised learning approach. This paper compensates the …

This paper proposes a method that uses a deep neural network (DNN) to detect small traffic
lights (TLs) in images captured by cameras mounted in vehicles. The proposed TL detector …

Long-range ground targets are difficult to detect in a noisy cluttered environment using either
synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can …

Breast cancer is the main cause of death among young women in developing countries. The
human body temperature carries critical medical information related to the overall body …

Most Active Contour Models (ACMs) deal with the image segmentation problem as a
functional optimization problem, as they work on dividing an image into several regions by …

Traditional sheep counting methods are labour-intensive, time-consuming, and potentially
disruptive to sheep behaviour. Unmanned aerial vehicles (UAVs) and machine learning …

This paper introduces a novel approach using video monitoring to automatically count
pollen sacs on honey bees returning to a beehive. This allows beekeepers to have an …