Classical Edge Detection Algorithm Research Articles

Mathematical model and nanoindentation properties of the claws of Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae).

Scanning electron microscopy (SEM) was used to observe the macroscopic, microscopic, and cross‐sectional structures of the claws of Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae), and a mathematical model of a claw was used to investigate the structure–function relationships. To improve the quality of the SEM images, a non‐local means (NLM) algorithm and an improved NLM algorithm were applied. After comparison and analysis of five classical edge‐detection algorithms, the boundaries of the structural features of the claw were extracted based on a B‐spline wavelet algorithm, and the results showed that the variable curvature of the beetle claw enhances its adhesion force and improves its strength. Adhesion models of the claw were established, and the mechanical properties of its biomaterials were measured using nanoindentation. Considering that the presence of water can affect the hardness and Young's modulus, both ‘dry’ and ‘wet’ samples were examined. For the dry samples, the hardness and Young's modulus were 0.197 ± 0.074 GPa and 1.105 ± 0.197 GPa, respectively, whereas the respective values for the wet samples were both lower at 0.071 ± 0.030 GPa and 0.693 ± 0.163 GPa. This study provides data that can inform the design of climbing robots.

Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. The algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. The algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm.

Mathematical Model and microCT-Based Kinematic Analysis of the Rostrum Mouthparts in Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae).

To uncover the chewing mechanism of Cyrtotrachelus buqueti Guer, a mathematical model was created and a kinematic analysis of its rostrum mouthparts was conducted for, to our knowledge, the first time. To reduce noise and improve the quality of scanning electron micrographs of the weevil's mouthparts, nonlocal means and integral nonlocal means algorithms were proposed. Additionally, based on a comparison and analysis of five classical edge detection algorithms, a multiscale edge detection algorithm based on the B-spline wavelet was used to obtain the boundaries of structural features. The least squares method was used to analyze the data of the mouthparts to fit the mathematical model and fitted curves were obtained using Gaussian equations. The results show that curvature and concave-convex variations of the weevil's mouthparts can highlight fluctuations in friction effects when it chews bamboo shoots, which is helpful in preventing debris from bamboo shoots or other debris from sticking to the mouthpart surfaces. Moreover, this paper highlights the utility of micro-computed tomography (microCT) for three-dimensional (3D) reconstruction and a flowchart is suggested. The reconstructed slices were 9.0 μm thick and an accurate 3D rendered model was obtained from a series of microCT slices. Finally, a real model of the rostrum mouthparts was analyzed using finite-element analysis. The results provide a biological template for the design of a novel bionic drilling mechanism.

A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms

Traditionally, the last step of edge detection algorithms, which is called scaling-evaluation, produces the final output classifying each pixel as edge or nonedge. This last step is usually done based on local evaluation methods. The local evaluation makes this classification based on measures obtained for every pixel. By contrast, in this work, we propose a global evaluation approach based on the idea of edge list to produce a solution that suits more with the human perception. In particular, we propose a new evaluation method that can be combined with any classical edge detection algorithm in an easy way to produce a novel edge detection algorithm. The new global evaluation method is divided in four steps: in first place we build the edge lists, that we have called edge segments. In second place we extract the characteristics associated to each segment: length, intensity, location, and so on. In the third step we learn the characteristics that make a segment good enough to become an edge. At the fourth step, we apply the classification task. In this work we have built the ground truth of edge list necessary for the supervised classification. Finally, we test the effectiveness of this algorithm against other classical algorithms based on local evaluation approach.

Active Canny: edge detection and recovery with open active contour models

The authors introduce an edge detection and recovery framework based on open active contour models (snakelets) to mitigate the problem of noisy or broken edges produced by classical edge detection algorithms, like Canny. The idea is to utilise the local continuity and smoothness cues provided by strong edges and grow them to recover the missing edges. This way, the strong edges are used to recover weak or missing edges by considering the local edge structures, instead of blindly linking edge pixels based on a threshold. The authors initialise short snakelets on the gradient magnitudes or binary edges automatically and then deform and grow them under the influence of gradient vector flow. The output snakelets are able to recover most of the breaks or weak edges and provide a smooth edge representation of the image; they can also be used for higher-level analysis, like contour segmentation.

Multi-Dimension and Structure Element Edge-Detection Based on Mathematical Morphology

A novel multi-dimension and structure element edge-detection based on mathematical morphology is presented to resolve blur problem of classical morphology when detecting an edge to reduce the noise but hard to preserve the details and edge information of the original image effectively. First,pretreatment of the image are completed by close-open operation to eliminate noise; second,do close operation to smooth image,in the end,using the operation of morphological gradient for smooting image,the ideal image edge under the environment of existing noise is obtained,and it is applied to detect the edges of welding pore images. The experimental results show that it is compared with classical Sobel operators,Canny operators and traditional edge detection algorithm, the proposed algorithm has the following distinguished advantages:accuracy of edges detected, a clear outline of the image, and can preserve more image details as well, and insensitive to noise.

Edge Detection for Noise Image by Wavelet Transform and Mathematical Morphology

It is much more complex and difficult for edge detection of noise image compared to edge detection of normal image，the analysis and study of edge detection of noise image has universal significance and practical value. Wavelet transform possesses good time-frequency localization characteristic and multi-scale analytical ability, mathematical morphology is a new subject based on set theory, which is very suitable for analyzing and describing geometrical feature of signal. Combining the advantages of wavelet transform and mathematical morphology, the paper proposes an edge detection algorithm, which mainly focused on noise image. For edge detection based on mathematical morphology, constructs an anti-noise operator of edge detection by improving existing operators and employs different direction linear structure elements; edge detection based on mathematical morphology can reserve details of edge effectively, ensure the continuity and integrity of edge detected. Experimental results show the proposed algorithm can suppress the interference of different density and different types of noise more effectively in comparison with several classical edge detection algorithm, thus improving the detection accuracy and robustness for different images.

Adaptive Weighted Morphology Detection Algorithm of Plane Object in Docking Guidance System

In this paper, we presented an image segmentation algorithm based on adaptive weighted mathematical morphology edge detectors. The performance of the proposed algorithm has been demonstrated on the Lena image. The input of the proposed algorithm is a grey level image. The image was first processed by the mathematical morphological closing and dilation residue edge detector to enhance the edge features and sketch out the contour of the image, respectively. Then the adaptive weight SE operation was applied to the edge-extracted image to fuse edge gaps and hill up holds. Experimental results show it can not only primely extract detail edge, but also superbly preserve integer effect comparative to classical edge detection algorithm.

Computational analysis and learning for a biologically motivated model of boundary detection

In this work we address the problem of boundary detection by combining ideas and approaches from biological and computational vision. Initially, we propose a simple and efficient architecture that is inspired from models of biological vision. Subsequently, we interpret and learn the system using computer vision techniques: First, we present analogies between the system components and computer vision techniques and interpret the network as minimizing a cost functional, thereby establishing a link with variational techniques. Second, based on Mean Field Theory the equations describing the network behavior are interpreted statistically. Third, we build on this interpretation to develop an algorithm to learn the network weights from manually segmented natural images. Using a systematic evaluation on the Berkeley benchmark we show that when using the learned connection weights our network outperforms classical edge detection algorithms.