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.

Enhancement of digital chest images using a modified Sobel edge detection algorithm

Computed tomography (CT) images are an essential factor in the diagnosing procedure for various diseases affecting the internal organs. Edge detection can be used for the appropriate enhancement of the lung CT scan images for the diagnosis of the various interstitial lung diseases (ILD). In order to solve the issues of edge detection provided by the traditional Sobel operator, the paper proposes a Sobel 12D edge detection algorithm which uses the additional direction templates for the better identification of the edge details. First, the vertical and horizontal directions available in the traditional Sobel operator are extended to few more directions (a total of 12 directions) which enhances the edge extraction ability. Next part, compute the edge detected image using the Sobel 12D, Laplace, Prewitt, Robert’s Cross and Scharr operators for edge detection separately. It is followed by image fusion method which optimizes the edge detection by combining the edge detected images obtained using the Sobel 12D approach and the Laplace operator. The experimental results shows that the proposed algorithms generates a better detection of the edges than the other edge detection operators.

Convolutional Neural Network for Pansharpening with Spatial Structure Enhancement Operator

Pansharpening aims to fuse the abundant spectral information of multispectral (MS) images and the spatial details of panchromatic (PAN) images, yielding a high-spatial-resolution MS (HRMS) image. Traditional methods only focus on the linear model, ignoring the fact that degradation process is a nonlinear inverse problem. Due to convolutional neural networks (CNNs) having an extraordinary effect in overcoming the shortcomings of traditional linear models, they have been adapted for pansharpening in the past few years. However, most existing CNN-based methods cannot take full advantage of the structural information of images. To address this problem, a new pansharpening method combining a spatial structure enhancement operator with a CNN architecture is employed in this study. The proposed method uses the Sobel operator as an edge-detection operator to extract abundant high-frequency information from the input PAN and MS images, hence obtaining the abundant spatial features of the images. Moreover, we utilize the CNN to acquire the spatial feature maps, preserving the information in both the spatial and spectral domains. Simulated experiments and real-data experiments demonstrated that our method had excellent performance in both quantitative and visual evaluation.

A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.

An Improved Sum of Squared Difference Algorithm for Automated Distance Measurement

In recent years, with improvement of photoelectric conversion efficiency and accuracy, photoelectric sensor was arranged to simulate binocular stereo vision for 3D measurement, and it has become an important distance measurement method. In this paper, an improved sum of squared difference (SSD) algorithm which can use binocular cameras to measure distance of vehicle ahead was proposed. Firstly, consistency matching calibration was performed when images were acquired. Then, Gaussian blur was used to smooth the image, and grayscale transformation was performed. Next, the Sobel operator was used to detect the edge of images. Finally, the improved SSD was used for stereo matching and disparity calculation, and the distance value could be obtained corresponding to each point. Experimental results showed that the improved SSD algorithm had an accuracy rate of 95.06% when stereo matching and disparity calculation were performed. This algorithm fully meets the requirements of distance measurement.

FPGA based Edge Detection using Sobel Filter

In image processing, Sobel operator is utilised especially inside algorithms of edge-detection. It is a discreet differentiation operator which calculates the gradient approximation of the function picture intensity. The outcome of the Sobel operation at each location of the image is either the appropriate gradient vector or the vector standard. The Sobel operator relies on the image being converted into horizontal and vertical with a tiny, separable and integrated valued filter. This means that the computation is quite inexpensive. PAN Poanta satellite image was used for this work using Java, Core Java in GDAL package. As compared to in built Sobel operator, the image generated for this work is very fine and sharp as a result of noise suppression to a considerable extent. Inorder to do edge detection efficiently with minimal amount of false results, a correct form of Sobel filter ( I’=√(I*X)²+(I*Y)2 ) was used instead of the approximation(I’=I*X+I*Y) for the sake of computation.

Falling-Point Recognition and Scoring Algorithm in Table Tennis Using Dual-Channel Target Motion Detection

In table tennis, the ball has numerous characteristics of high speed, small size, and changeable trajectory. Due to these characteristics, the human eye often cannot accurately judge the ball’s movement and position, leading to the problem of precise detection of the ball’s falling point and movement tracking. In sports, the use of machine learning for locating and detecting the ball and the use of deep learning for reconstructing and displaying the ball’s trajectories are considered futuristic technologies. Therefore, this paper proposes a novel algorithm for identifying and scoring points in table tennis based on dual-channel target motion detection. The proposed algorithm consists of multiple input channels to jointly learn different features of table tennis images. The original image is used as the input of the first channel, and then the Sobel operator is used to extract the first-order derivative feature of the original image, which is used as the input of the second channel. The table tennis feature information from the two channels is then fused and sent to the 3D neural network module. The fully connected layer is used to identify the table tennis ball’s drop point, compare it with a standard drop point, calculate the error distance, and give a score. We also constructed a data set and conducted experiments. The experimental results show that the method in this paper is effective in sports.