By Taufik Abrao
This booklet explores various purposes for seek equipment and methods in numerous fields of electric engineering. The booklet will function an invaluable source for college students, researchers and practitioners to additional take advantage of the possibility of seek tools in fixing demanding optimization difficulties that come up in complex engineering applied sciences, corresponding to picture and video processing concerns, detection and source allocation in telecommunication platforms, defense and harmonic relief in energy iteration platforms, in addition to redundancy optimization challenge and search-fuzzy studying mechanisms in commercial applications.
Heuristic seek is a crucial sub-discipline of optimization thought and unearths purposes in an unlimited number of fields, together with existence technology and engineering.
Search tools were invaluable in fixing tricky engineering-oriented difficulties that both couldn't be solved the other manner or recommendations take a long time to be computed.
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Extra resources for Search Algorithms for Engineering Optimization
Sample text
To provide a fair comparison, the threshold and morphological edge-thinning operations are neglected. The simulation results show that the proposed method outperforms the other two methods in terms of visual quality of the extracted edge information and sensitivity to weaker edges. The qualitative results of the edge detector proposed in [8] were presented after applying the thinning step, hence a fair comparison with the here-presented results could not be made. It is worth mentioning that the number of iterations used in experiments in [8] was much higher (1000 iterations) than required by our algorithm.
In order to test the proposed method on different input images, simulations were performed on "House", "Lena" and "Cameraman" images of size 256 × 256 pixels. The results confirm the effectiveness of the method, as shown in Figure 12. It can be noticed that the found edge segments are often not unidirectional, which indicates that the fitness function was adequately defined and the ants found the true edges. The main contribution of the proposed broken-edge-linking method is in using a bottom-up approach that avoids using a global threshold to find the missing segments.
The Prewitt operator [25] extracts contour features by fitting a Least Squares Error (LSE) quadratic surface over an image window and differentiate the fitted surface. The edge detectors proposed in [31] and [3] use local gradient operators, sometimes with additional smoothing for noise removal. The Laplacian operator [9] applies a second order differential operator to find edge points based on the zero crossing properties of the processed edge points. Although conventional edge detectors usually perform linear filtering operations, there are various nonlinear methods proposed.