Image Edge Detection Algorithms - Sobel Research Papers (original) (raw)

In today’s digital world, image edge detection is widely used in all over the globe. Digital image edge detection is used to detect the sharp changes in brightness of the images and the edge of a digital image. There are many classical techniques used to detect image edge detection like Canny, Sobel, Prewitt and Robert edge detector method. The proposed research work mainly focuses on the study of different edge detection methods which were previously derived and to propose an efficient method of digital image edge detection technique using Discrete Wavelet Transform (DWT). The results were compared with one of the best edge detector method: Canny Edge Detector. In the proposed image edge detection method, the first analytical result showed that an input image after applying the thresholding technique represents a segmentation of image edges and the second analytical results showed the 8- connected components of pixel connectivity in two dimensional images related to their neighbor pixels producing the enhanced edges. To evaluate the performance of the proposed algorithm with the existing algorithms, the two thresholding values were applied as T_low and T_high using hysteresis thresholding method. PSNR and width σ were also computed to detect the efficiency of the proposed algorithm.

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- DWT, Thresholding, PSNR, Image Edge Detection Algorithms - Sobel

The purpose of this project was to create several methods for achieving non-photorealistic appearance of the objects or, in other words, the goal was to create several different implementations of the cartoonish shader, which can... more

- by Roman Fekolkin
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- Algorithms, Computer Graphics, Image Processing, Visualization

In this paper, a hardware system for Sobel Edge Detection Algorithm is designed and simulated for a 128 pixel, 8-bit monochrome line-scan camera. The system is designed to detect objects as they move along a conveyor belt in a manufacturing environment, the camera will observe dark objects on a light conveyor belt. The edge detector is required to detect horizontal and vertical edges using Sobel edge detection method. The Sobel operator requires 3 lines and takes 3 pixels per line, thus using a 3×3 input block to process each pixel. The centre pixel of the 3×3 pixel block can be classified as an edge point or otherwise by thresholding the value from the operator. The FPGA based Sobel edge detector is designed and simulated using Altera Quartus II 8.1 web edition by targeting Cyclone II development boards.

This paper proposes an improved detection algorithm for LTE Random Access preamble detection and evaluates the performance of the algorithm with respect to the performance of algorithms proposed in the literature using MDP metric. Keywords— detection algorithm; LTE; Random access; preamble; MDP I. INTRODUCTION The 3 rd Generation Partnership Project (3GPP) mandates a certain access procedure for achieving synchronization between the evolved base station (eNodeB) and the mobile User Equipment (UE) in the Long Term Evolution (LTE) Standard. This Access Procedure consists of three parts, cell search which enables the UE to assess the reception quality of the current and neighbouring cells so as to conclude if a handover is necessary; transfer of system information; and Random Access. A detailed description of this access procedure is discussed in the standard [1][2][3].This Random Access procedure consists of four steps executed by the UE and eNodeB [5] that helps the network identify the UE and establish timing synchronization [Fig. 1]. The following are the four steps of Random Access, Step 1: The UE selects one of the 64 available preambles generated from a root sequence and transmits it to the eNodeB. This preamble identifies the UE and provides Timing Advance (TA) information to the network. Step 2: The eNodeB replies with a Random access response message that consists of a temporary identity assigned to the UE, TA information and Uplink Grant resource. Step 3: The UE transmits a Terminal Identification message based on the temporary identity received in the previous step as part of Radio Resource Control (RRC) signaling. Step 4: The eNodeB replies with a contention resolution message completing the RRC signaling between the network and the UE.

This research presents new three proposed approaches to enhancement the visibility of the Infrared (IR) night vision images. The first proposed approach depends on Hybrid Adaptive Gamma Correction (AGC) with Histogram Matching (HGCHM). The second proposed approach stands up Merging Gamma Correction with Contrast Limited Adaptive Histogram Equalization (MGCCLAHE). The HM uses a reference visual image for converting of night vision images into daytime images. The third approach mixes the benefits of the CLAHE with the undecimated Additive Wavelet Transform (AWT) Using Homomorphic processing (CSAWUH). The quality assessments for the suggested approaches are entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy, lightness order error and the similarity of edges. Simulation results clear that the third proposed approach gives superior results to the two proposed approaches from entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy and the computation time perspectives. On the other hand, the second proposed approach takes long computation time in the implementation with respect to the two proposed approaches. The second proposed approach gives better results to the first proposed approach entropy, average gradient, contrast improvement factor, Sobel edge magnitude, and spectral entropy perspectives. The first proposed approach gives better results to the two proposed approaches from lightness order error and the similarity of edges perspectives.

- by دكتور عادل الفيشاوي
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- Entropy, Thermal Imaging, Histogram, Night Vision

Hydroacoustic detection of natural gas release from the seafloor has been conducted in the past by using singlebeam echosounders. In contrast, modern multi-beam swath mapping systems allow much wider coverage, higher resolution, and offer 3-D spatial correlation. Up to the present, the extremely high data rate hampers water column backscatter investigations and more sophisticated visualiza-tion and processing techniques are needed. Here, we present water column backscatter data acquired with a 50 kHz prototype multibeam system over a period of 75 seconds. Display types are of swath-images as well as of a " resorted " sin-glebeam presentation. Thus, individual and/or groups of gas bubbles rising from the 24 m deep seafloor clearly emerge in the acoustic images, making it possible to estimate rise velocities. A sophisticated processing scheme is introduced to identify those rising gas bubbles in the hydroacoustic data. We apply a cross-correlation technique adapted from particle imaging velocimetry (PIV) to the acoustic backscatter images. Temporal and spatial drift patterns of the bubbles are assessed and are shown to match very well to measured and theoretical rise patterns. The application of this processing to our field data gives clear results with respect to unambiguous bubble detection and remote bubble rise velocimetry. The method can identify and exclude the main source of misinterpretations , i.e. fish-mediated echoes. Although image-based cross-correlation techniques are well known in the field of fluid mechanics for high resolution and non-inversive current flow field analysis, we present the first application of this technique as an acoustic bubble detector.

- by huda ashiba
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- Information Systems, Entropy, Computer Software, Thermal Imaging