A Review and Performance Analysis of Image Edge Detection Algorithms

Edge detection is the fundamental operation of digital image processing and applied in many fields like industrial, medical, satellite, agriculture etc. According to this growth of edge detection applications, many researchers and scholars are interested to develop the edge detection algorithm by using various techniques. This paper illustrates the review for what are the novel techniques are used for the edge detection, which operators are mostly used by them and how they get the accurate results to compare with existing methods. It also discussing the performance analysis of most commonly used edge detection operators such as Canny, Laplacian Gaussian (LoG), Sobel, Prewitt and Roberts,. Finally the accuracy, PSNR (Peak Signal to Noise Ratio) and execution time are tabulated and realize the most precious and fast computed edge detection method is uncovered

Edge detection based on type-1 fuzzy logic and guided smoothening

Edge detection is an important phenomenon in computer vision. Edge detection is helpful in contour detection and thus helpful in obtaining the important information. Edge detection process heavily depends on chosen technique. Soft computing techniques are considered as powerful edge detection methods due to their adaptability. This paper presents a fuzzy logic based edge detection method where the quality of edges is controlled using sharpening guided filter and noise due to the sharpening is controlled using Gaussian filter. The accuracy of the method is judged using a variety of statistical measures. It has been found that by proper selecting the smoothening parameters a significant improvement in the detected edges can be obtained

A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification

Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed flexible convolutional auto-encoder with much less computational resource and without any manual intervention. We use the designed architecture optimization algorithm to test the proposed flexible convolutional auto-encoder through utilizing one graphic processing unit card on four extensively used image classification datasets. Experimental results show that our work in this paper significantly outperform the peer competitors including the state-of-the-art algorithm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems, 201

Modeling flocculation and deflocculation processes of cohesive sediments

The transport and fate of cohesive sediments are responsible for many engineering, environmental, economic and policy issues that relate to, for example, siltation and dredging in navigation channels, water quality, water turbidity, pollutant transports, and biological ecosystem responses. Our current understanding, however, is insufficient to conduct accurate quantitative predictions of these processes. This is because the cohesive particles in natural waters will flocculate, which determines the settling, and thus the deposition behaviors. The simulation of flocculation processes is a primary challenge since the time variation of Floc Size Distribution (FSD) is controlled by a partial differential equation that also contains the integration of FSD itself. Previous models either address less characteristic sizes, which produce biased FSDs, or are incapable of modeling a relative large study domain in order to better express the FSDs with more size groups. In this study, a cohesive sediment flocculation model developed based on the framework of Population Balance Model (PBM) is solved by the Quadrature Method of Moments (QMOM). This PBM�QMOM flocculation model has reasonably compromised by both the model robustness and model efficiency. The former lies in the capability of describing the time evolution of the FSDs with a maximum of eight size classes, and the latter is reflected in its efficiency to solve PBM with transport terms and the potential to be coupled in a flow-mud estuary model. The model predictions are compared to both the analytical (or trusted class method) results for general PBMs (i.e., beyond the scope of specific research field), and the published experimental results of kaolinite suspension and colloidal montmorillonite. After that, an experimental activity has been carried out to develop a Sony NEX-5R camera system (with extension tubes and close-up) to automatically acquire floc images under various controlled environments, and to use MATLAB software to process the FSDs. This process is validated by the results of two set of sample particles. The validated camera system is first applied in a five liter mixing chamber to investigate the effects of salinity and selected organic matters on kaolinite flocculation. Then, the camera system is improved and assembled in a waterproof house for underwater use to provide data for a conceptual one-dimensional application in a relatively large turbulence tank. The flow field of the tank is measured by an acoustic Doppler velocimetry. The flocculation processes in the mixing chamber or cylindrical tank are modeled by PBM�QMOM and validated by camera statistical FSDs. While chemical and biological effects are not explicitly included in PBM�QMOM (implicitly included in fitting parameters) at this time to address the basic mechanisms of flocculation, these effects can be further extended when the process itself is better understood through other laboratory experiments or field measurements