Design and Implementation of Image Edge Detection Algorithm on FPGA

Image processing is an important task in data processing systems for applications such as medical sectors, remote sensing, and microscopy tomography. Edge recognition is a sort of image division method that is used to simplify the image records so as to reduce the amount of data to be processed. Edges are considered the most important in image processing because they are used to characterize the boundaries of an image. The performance of the Canny edge recognition algorithm remarkably surpasses the present edge recognition technology in various computer visualization methods. The main drawback of using Canny edge boundary is that it consumes lot of period due to its complex computation. In order to tackle this problem a hybrid edge recognition method is proposed in block stage to locate edges with no loss. It employs the Sobel operator estimate method to calculate the value and direction of the gradient by substituting complex processes by hardware cost savings, traditional non-maximum suppression adaptive thresholding block organization, and conventional hysteresis thresholding. Pipeline was presented to lessen latency. The planned strategy is simulated using Xilinx ISE Design Suite14.2 running on a Xilinx Spartan-6 FPGA board. The synthesized architecture uses less hardware to detect edges and operates at maximum frequency of 935 MHz.

Edge Location and Identification Method for Electronic Components based on Improved Canny Algorithm

The position identification of SMD electronic components mainly uses Canny edge detection algorithm to detect the edges of specific elements, benefited from its computational simplicity. The traditional Canny algorithm lacks the adaptability in gradient calculation and double thresholds selection, which may affect the location and identification accuracy of specific elements in electronic components. In this paper, an improved canny edge detection algorithm is proposed. The gradient magnitude is calculated in four directions, i.e., horizontal, vertical, and diagonal. Both the high and low thresholds can be adaptively determined based on the grayscale distribution information, to increase the adaptability of edge identification. The experimental results show that the proposed method can better locate the true edges of specific elements in electronic components with a reasonable processing speed, compared with the traditional Canny algorithm, and has been successfully applied on practical real-time vision inspection on SMD electronic components.

Edge Detection in Natural Scenes Inspired by the Speed Drawing Challenge

Edge detection is a major step in several computer vision applications. Edges define the shape of objects to be used in a recognition system, for example. In this work, we introduce an approach to edge detection inspired by a challenge for artists: the Speed Drawing Challenge. In this challenge, a person is asked to draw the same figure in different times (as 10[Formula: see text]min, 1[Formula: see text]min and 10[Formula: see text]s); at each time, different levels of details are drawn by the artist. In a short time stamp, just the major elements remain. This work proposes a new approach for producing images with different amounts of edges representing different levels of relevance. Our method uses superpixel to suppress image details, followed by Globalized Probability of Boundary (gPb) and Canny edge detection algorithms to create an image containing different number of edges. After that, an edge analysis step detects whose edges are the most relevant for the scene. The results are presented for the BSDS500 dataset and they are compared to other edge and contour detection algorithms by quantitative and qualitative means with very satisfactory results.

A Measurement of Visual Complexity for Heterogeneity in the Built Environment Based on Fractal Dimension and Its Application in Two Gardens

In this study, a fractal dimension-based method has been developed to compute the visual complexity of the heterogeneity in the built environment. The built environment is a very complex combination, structurally consisting of both natural and artificial elements. Its fractal dimension computation is often disturbed by the homogenous visual redundancy, which is textured but needs less attention to process, so that it leads to a pseudo-evaluation of visual complexity in the built environment. Based on human visual perception, the study developed a method: fractal dimension of heterogeneity in the built environment, which includes Potts segmentation and Canny edge detection as image preprocessing procedure and fractal dimension as computation procedure. This proposed method effectively extracts perceptually meaningful edge structures in the visual image and computes its visual complexity which is consistent with human visual characteristics. In addition, an evaluation system combining the proposed method and the traditional method has been established to classify and assess the visual complexity of the scenario more comprehensively. Two different gardens had been computed and analyzed to demonstrate that the proposed method and the evaluation system provide a robust and accurate way to measure the visual complexity in the built environment.

Sensitive Ant Algorithm for Edge Detection in Medical Images

Nowadays, reliable medical diagnostics from computed tomography (CT) and X-rays can be obtained by using a large number of image edge detection methods. One technique with a high potential to improve the edge detection of images is ant colony optimization (ACO). In order to increase both the quality and the stability of image edge detection, a vector called pheromone sensitivity level, PSL, was used within ACO. Each ant in the algorithm has one assigned element from PSL, representing the ant’s sensibility to the artificial pheromone. A matrix of artificial pheromone with the edge information of the image is built during the process. Demi-contractions in terms of the mathematical admissible perturbation are also used in order to obtain feasible results. In order to enhance the edge results, post-processing with the DeNoise convolutional neural network (DnCNN) was performed. When compared with Canny edge detection and similar techniques, the sensitive ACO model was found to obtain overall better results for the tested medical images; it outperformed the Canny edge detector by 37.76%.

Digital Image Evolution of Artwork Without Human Evaluation Using the Example of the Evolving Mona Lisa Problem

Whether for optimizing the speed of microprocessors or for sequence analysis in molecular biology — evolutionary algorithms are used in astoundingly many fields. Also, the art was influenced by evolutionary algorithms — with principles of natural evolution works of art that can be created or imitated, whereby initially generated art is put through an iterated process of selection and modification. This paper covers an application in which given images are emulated evolutionary using a finite number of semi-transparent overlapping polygons, which also became known under the name “Evolution of Mona Lisa”. In this context, different approaches to solve the problem are tested and presented here. In particular, we want to investigate whether Hill Climbing Algorithm in combination with Delaunay Triangulation and Canny Edge Detector that extracts the initial population directly from the original image performs better than the conventional Hill Climbing and Genetic Algorithm, where the initial population is generated randomly.

A Deep Learning Framework for Intrusion Detection and Multimodal Biometric Image Authentication

Nowadays, a demand is increased all over the world in the field of information security and security regulations. Intrusion detection (ID) plays a significant role in providing security to the information, and it is an important technology to identify various threats in network during transmission of information. The proposed system is to develop a two-layer security model: (1) Intrusion Detection, (2) Biometric Multimodal Authentication. In this research, an Improved Recurrent Neural Network with Bi directional Long Short-Term Memory (I-RNN-BiLSTM) is proposed, where the performance of the network is improved by introducing hybrid sigmoid-tanh activation function. The intrusion detection is performed using I-RNN-BiLSTM to classify the NSL-KDD dataset. To develop the biometric multimodal authentication system, three biometric images of face, iris, and fingerprint are considered and combined using Shuffling algorithm. The features are extracted by Gabor, Canny Edge, and Minutiae for face, iris, and fingerprint, respectively. The biometric multimodal authentication is performed by the proposed I-RNN-BiLSTM. The performance of the proposed I-RNN-BiLSTM has been analysed through different metrics like accuracy, f-score, and confusion matrix. The simulation results showed that the proposed system gives better results for intrusion detection. Proposed model attains an accuracy of 98% for the authentication process and accuracy of 98.94% for the intrusion detection process.