7 research outputs found
An efficient of estimation stages for segmentation skin lesions based optimization algorithm
Modern dermatology distinguishes premature diagnosis for example an important part in reducing the death percentage and promising less aggressive treatment for patients. The classifications comprise various stages that must be selected suitably using the characteristics of the filter pointing to get a dependable analysis. The dermoscopic images hold challenges to be faced and overcome to enhance the automatic diagnosis of hazardous lesions. It is calculated to survey a different metaheuristic and evolutionary computing working for filter design systems. Approximately general computing techniques are observed to improve features of infect design method. Nevertheless, the median filter (MF) is normally multimodal with respect to the filter factors and so, reliable approaches that can provide optimal solutions are required. The design of MF depends on modern artificial swarm intelligence technique (MASIT) optimization algorithm which has proven to be more effective than other population-based algorithms to improve of estimation stages for segmentation skin lesions. A controlled artificial bee colony (ABC) algorithm is advanced for solving factors optimization problems and, also the physical-programming-depend on ABC way is applied to proposal median filter, and the outcomes are compared to another approaches
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)
Block matching (BM) motion estimation plays a very important role in video
coding. In a BM approach, image frames in a video sequence are divided into
blocks. For each block in the current frame, the best matching block is
identified inside a region of the previous frame, aiming to minimize the sum of
absolute differences (SAD). Unfortunately, the SAD evaluation is
computationally expensive and represents the most consuming operation in the BM
process. Therefore, BM motion estimation can be approached as an optimization
problem, where the goal is to find the best matching block within a search
space. The simplest available BM method is the full search algorithm (FSA)
which finds the most accurate motion vector through an exhaustive computation
of SAD values for all elements of the search window. Recently, several fast BM
algorithms have been proposed to reduce the number of SAD operations by
calculating only a fixed subset of search locations at the price of poor
accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC)
optimization is proposed to reduce the number of search locations in the BM
process. In our algorithm, the computation of search locations is drastically
reduced by considering a fitness calculation strategy which indicates when it
is feasible to calculate or only estimate new search locations. Since the
proposed algorithm does not consider any fixed search pattern or any other
movement assumption as most of other BM approaches do, a high probability for
finding the true minimum (accurate motion vector) is expected. Conducted
simulations show that the proposed method achieves the best balance over other
fast BM algorithms, in terms of both estimation accuracy and computational
cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with
arXiv:1405.4721, arXiv:1406.448
Multi-circle detection on images using artificial bee colony (ABC) optimization
Hough transform has been the most common method for circle detection, exhibiting robustness, but adversely demanding considerable computational effort and large memory requirements. Alternative approaches include heuristic methods that employ iterative optimization procedures for detecting multiple circles. Since only one circle can be marked at each optimization cycle, multiple executions ought to be enforced in order to achieve multi-detection. This paper presents an algorithm for automatic detection of multiple circular shapes that considers the overall process as a multi-modal optimization problem. The approach is based on the artificial bee colony (ABC) algorithm, a swarm optimization algorithm inspired by the intelligent foraging behavior of honeybees. Unlike the original ABC algorithm, the proposed approach presents the addition of a memory for discarded solutions. Such memory allows holding important information regarding other local optima, which might have emerged during the optimization process. The detector uses a combination of three non-collinear edge points as parameters to determine circle candidates. A matching function (nectar-amount) determines if such circle candidates (bee-food sources) are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate circles are evolved through the ABC algorithm so that the best candidate (global optimum) can be fitted into an actual circle within the edge-only image. Then, an analysis of the incorporated memory is executed in order to identify potential local optima, i. e., other circles. The proposed method is able to detect single or multiple circles from a digital image through only one optimization pass. Simulation results over several synthetic and natural images, with a varying range of complexity, validate the efficiency of the proposed technique regarding its accuracy, speed, and robustness. Zapotitlán 2011 Springer-Verlag
Solving Graph Coloring Problems Using Discrete Artificial Bee Colony
ç‘æ³¢å¤§å¦ (University of Tsukuba)201