Recent advances in harris hawks optimization: A comparative study and applications
The Harris hawk optimizer is a recent population-based metaheuristics algorithm that
simulates the hunting behavior of hawks. This swarm-based optimizer performs the …
simulates the hunting behavior of hawks. This swarm-based optimizer performs the …
[PDF][PDF] A survey: Image segmentation techniques
MW Khan - International Journal of Future Computer and …, 2014 - researchgate.net
Segmentation is considered as one of the main steps in image processing. It divides a digital
image into multiple regions in order to analyze them. It is also used to distinguish different …
image into multiple regions in order to analyze them. It is also used to distinguish different …
Reversible data hiding with contrast enhancement and tamper localization for medical images
Digital transmission of medical images often involves hiding crucial information in some
parts of the images which should be later extracted to authenticate ownership and identity. In …
parts of the images which should be later extracted to authenticate ownership and identity. In …
Brain tumour segmentation using S-Net and SA-Net
Image segmentation is an application area of computer vision and digital image processing
that partitions a digital image into multiple image regions or segments. This process involves …
that partitions a digital image into multiple image regions or segments. This process involves …
Active contour driven by multi-scale local binary fitting and Kullback-Leibler divergence for image segmentation
Image segmentation is an important processing in many applications such as image
retrieval and computer vision. One of the most successful models for image segmentation is …
retrieval and computer vision. One of the most successful models for image segmentation is …
[PDF][PDF] An efficient image segmentation method based on linear discriminant analysis and K-means algorithm with automatically splitting and merging clusters
Image segmentation plays a significant role in image processing, pattern recognition and as
well as in computer vision. It aims to classify the meaningful objects residing in the image …
well as in computer vision. It aims to classify the meaningful objects residing in the image …
Sparse graph connectivity for image segmentation
It has been demonstrated that the segmentation performance is highly dependent on both
subspace preservation and graph connectivity. In the literature, the full connectivity method …
subspace preservation and graph connectivity. In the literature, the full connectivity method …
[PDF][PDF] Segmentation and measurement of medical image quality using K-means clustering algorithm
AMA Karrar, J Sun - American Journal of Neural Networks and …, 2019 - academia.edu
In this paper we have segmented an image by using a k-clustering algorithm, using the
Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have …
Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have …
Image Segmentation Techniques: A Survey
S Puri, S Singh - International Journal of Engineering and Applied …, 2021 - ijeap.org
Segmenting an image utilizing diverse strategies is the primary technique of Image
Processing. The technique is broadly utilized in clinical image handling, face …
Processing. The technique is broadly utilized in clinical image handling, face …
[PDF][PDF] A SURVEY ON BRAIN TUMOR IDENTIFICATION THROUGH MEDICAL IMAGES.
M Saini, S Saini, P Tripathi, KK Saini… - International Journal of …, 2017 - academia.edu
This paper presents a survey on the detection of brain tumor. Brain Tumor can be described
as a cluster of abnormal cells which grows inside the brain by uncontrolled growth in tissues …
as a cluster of abnormal cells which grows inside the brain by uncontrolled growth in tissues …