Subsurface source boundary identification is a major step in the interpretation of potential field anomalies in geophysical exploration. We investigated the behavior of wavelet space entropy over the boundaries of 2D potential field source edges. We tested the robustness of the method for complex source geometries with distinct source parameters of prismatic bodies. We further validated the behavior with two datasets by delineating the edges of (i) the magnetic anomalies due to the popular Bishop model and (ii) the gravity anomalies of the Delhi fold belt region, India. The results showed prominent signatures for the geological boundaries. Our findings indicate sharp changes in the wavelet space entropy values corresponding to the source edges. The effectiveness of wavelet space entropy was compared with the established edge detection techniques. The findings can help with a variety of geophysical source characterization problems.

/pdf/wavelet-entropy-a-new-tool-for-edge-detection-of-potential-2bbnh9db.pdf

Wavelet Entropy: A New Tool for Edge Detection of Potential Field Data

By exploring the nonlocal self-similarity of images, weighted nuclear norm minimization (WNNM) based image denoising algorithm has achieved competitive performance. Multi-scale patches-based image denoising using weighted nuclear norm minimization (MS-WNNM) is one of the follow-up researches, which further improves WNNM by considering cross-scale self-similarity in block-matching. However, the better performance is achieved at the price of spending more time in a larger search space. In this paper, we propose an edge map-guided strategy to accelerate MS-WNNM based image denoising. Inspired by the fact that patches belonging to the structure part and patches belonging to the texture part follow different repetitive patterns, we propose a prior that the patches containing and surrounding the edges carry most of the structural and textural information of the image. The proposed prior is explained from an information-theoretic perspective. Based on this prior, an edge map-guided acceleration strategy is designed, where redundant patches are trimmed off and the informative patches are retained. Block-matching in the pruned search space, which still has patch diversity, can significantly reduce time without much loss of quality. As a result, comparable denoising can be achieved in less runtime. The experimental results demonstrate the quality and efficiency of the proposed method. 

An edge map-guided acceleration strategy for multi-scale weighted nuclear norm minimization-based image denoising

Edge detection is a fundamental process, and therefore there are still demands to improve its efficiency and computational complexity. This study proposes a knowledge-based edge detection method to meet this requirement by introducing a set of knowledge-based rules. The methodology to derive the rules is based on the observed continuity properties and the neighborhood characteristics of the edge pixels, which are expressed as simple arithmetical operations to improve computational complexity. The results show that the method has an advantage over the gradient-based methods in terms of performance and computational load. It is appropriately four times faster than Canny method and shows superior performance compared to the gradient-based methods in general. Furthermore, the proposed method provides robustness to effectively identify edges at the corners. Due to its light computational requirement and inherent parallelization properties, the method would be also suitable for hardware implementation on field-programmable gate arrays (FPGA).

/pdf/edge-detection-method-driven-by-knowledge-based-neighborhood-3iof9c18.pdf

Edge Detection Method Driven by Knowledge-Based Neighborhood Rules

The Milky Way is used by nocturnal flying and walking insects for maintaining heading while navigating. In this study, we have explored the feasibility of the method for machine vision systems on autonomous vehicles by measuring the visual features and characteristics of the Milky Way. We also consider the conditions under which the Milky Way is used by insects and the sensory systems that support their detection of the Milky Way. Using a combination of simulated and real Milky Way imagery, we demonstrate that appropriate computer vision methods are capable of reliably and accurately extracting the orientation of the Milky Way under an unobstructed night sky. The technique presented achieves angular accuracy of better then ±2° under moderate light pollution conditions but also demonstrates that higher light pollution levels will adversely effect orientation estimates by systems depending on the Milky Way for navigation.

/pdf/a-computer-vision-milky-way-compass-qmnd7b6t.pdf

A Computer Vision Milky Way Compass

Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.

Thomas Scully

Papers

A Low Redundancy Wavelet Entropy Edge Detection Algorithm.