Phase Congruency Parameter Optimization for Enhanced Detection of Image Features for both Natural and Medical Applications
Following the presentation and proof of the hypothesis that image features are particularly perceived at points where the Fourier components are maximally in phase, the concept of phase congruency (PC) is introduced. Subsequently, a two-dimensional multi-scale phase congruency (2D-MSPC) is developed, which has been an important tool for detecting and evaluation of image features. However, the 2D-MSPC requires many parameters to be appropriately tuned for optimal image features detection. In this paper, we defined a criterion for parameter optimization of the 2D-MSPC, which is a function of its maximum and minimum moments. We formulated the problem in various optimal and suboptimal frameworks, and discussed the conditions and features of the suboptimal solutions. The effectiveness of the proposed method was verified through several examples, ranging from natural objects to medical images from patients with a neurological disease, multiple sclerosis.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
PCNet: A Structure Similarity Enhancement Method for Multispectral and Multimodal Image Registration
Multispectral and multimodal images are of important usage in the field of multi-source visual information fusion. Due to the alternation or movement of image devices, the acquired multispectral and multimodal images are…
Image RegistrationAdaptive Active Contour Model for Brain Tumor Segmentation
For accurately diagnosing the severity of brain tumors in MRI images, Glioma segmentation is a significant step. The Glioma segmentation is due to noise and weak edges of organs in medical images. The geodesic active con…
Brain Tumor SegmentationEdge DetectionImage Segmentationmodel+2Sub-pixel matching method for low-resolution thermal stereo images
In the context of a localization and tracking application, we developed a stereo vision system based on cheap low-resolution 80x60 pixels thermal cameras. We proposed a threefold sub-pixel stereo matching framework (call…
Stereo MatchingA Total Variation Denoising Method Based on Median Filter and Phase Consistency
The total variation method is widely used in image noise suppression. However, this method is easy to cause the loss of image details, and it is also sensitive to parameters such as iteration time. In this work, the tota…
DenoisingDirection Concentration Learning: Enhancing Congruency in Machine Learning
One of the well-known challenges in computer vision tasks is the visual diversity of images, which could result in an agreement or disagreement between the learned knowledge and the visual content exhibited by the curren…
BIG-bench Machine LearningContinual LearningDiversityImage Classification+1