Search Results - (( basic optimization matching algorithm ) OR ( using vectorization _ algorithm ))

Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence by S. S. S. , Ranjit

“…On the theoretical side, four fundamental issues are explored: (1) division of frame, (2) basic block-based matching, (3) motion vector estimation, and (4) block-matching algorithm development. …”
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Designing an integrated AIOT system for tracking class attendance by Kuak, Xuan Ren

“…For face detection the system uses a YOLO (You Only Look Once) algorithm, which allows quick and efficient recognition of student faces in the classroom context. …”
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Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications by Teo, Chee Huat

“…DSF algorithm is a hybrid stereo matching algorithm which divided into two phases. …”
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Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman by Che Osman, Siti Eshah

“…Recently, intelligent searching methods were proposed to enhance the computational optimization issues in motion estimation but still lack in obtaining the best solution of block matching. …”
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Stereo matching algorithm using census transform and segment tree for depth estimation by Hamzah, Rostam Affendi, Zainal Azali, Muhammad Nazmi, Mohd Noh, Zarina, Tengku Wook, Tg Mohd Faisal, Zainal Abidin, Izwan

“…This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. …”
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Comparison between Lamarckian Evolution and Baldwin Evolution of neural network by Taha, Imad, Inazy, Qabas

“…Genetic Algorithms are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. …”
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A fuzzy case-based reasoning model for software requirements specifications quality assessment by Mostafa S.A., Gunasekaran S.S., Khaleefah S.H., Mustapha A., Jubair M.A., Hassan M.H.

“…Additionally, for efficient cases retrieval in the CBR, relevant cases selection and nearest cases selection heuristic search algorithms are used in the system. Basically, the input to the relevant cases algorithm is the available cases in the system case base and the output is the relevant cases. …”

Enhanced Image View Synthesis Using Multistage Hybrid Median Filter For Stereo Images by Ali Hussein Aboali, Maged Yahya

“…Disparity depth map estimation of stereo matching algorithm is one of the most active research topics in computer vision.In the field of image processing,many existing stereo matching algorithms to obtain disparity depth map are developed and designed with low accuracy.To improve the accuracy of disparity depth map is quite challenging and difficult especially with uncontrolled dynamic environment.The accuracy is affected by many unwanted aspects including random noises,horizontal streaks,low texture,depth map non-edge preserving, occlusion,and depth discontinuities.Thus,this research proposed a new robust method of hybrid stereo matching algorithm with significant accuracy of computation.The thesis will present in detail the development,design, and analysis of performance on Multistage Hybrid Median Filter (MHMF).There are two main parts involved in our developed method which combined in two main stages.Stage 1 consists of the Sum of Absolute Differences (SAD) from Basic Block Matching (BBM) algorithm and the part of Scanline Optimization (SO) from Dynamic Programming (DP) algorithm.While,Stage 2 is the main core of our MHMF as a post-processing step which included segmentation,merging, and hybrid median filtering.The significant feature of the post-processing step is on its ability to handle efficiently the unwanted aspects obtained from the raw disparity depth map on the step of optimization.In order to remove and overcome the challenges unwanted aspects, the proposed MHMF has three stages of filtering process along with the developed approaches in Stage 2 of MHMF algorithm.There are two categories of evaluation performed on the obtained disparity depth map: subjective evaluation and objective evaluation.The objective evaluation involves the evaluation on Middlebury Stereo Vision system and evaluation using traditional methods such as Mean Square Errors (MSE),Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM).Based on the results of the standard benchmarking datasets from Middlebury,the proposed algorithm is able to reduce errors of non-occluded and all errors respectively.While,the subjective evaluation is done for datasets captured from MV BLUE FOX camera using human's eyes perception.Based on the results,the proposed MHMF is able to obtain accurate results, specifically 69% and 71% of non-occluded and all errors for disparity depth map, and it outperformed some of the existing methods in the literature such as BBM and DP algorithms.…”
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A Test Vector Minimization Algorithm Based On Delta Debugging For Post-Silicon Validation Of Pcie Rootport by Toh , Yi Feng

“…Test results using test vector sets containing deliberately introduced erroneous test vectors show that the minimizer is able to isolate the erroneous test vectors. …”
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Scalable Self-Organizing Model for Grid Resource Discovery by Mahamat Issa, Hassan, Azween, Abdullah

“…The paper also proposes a resource discovery algorithm that optimizes resource query on the Grid network. …”
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Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions by Kian, Sheng Lim, Zuwairie, Ibrahim, Salinda, Buyamin, Anita, Ahmad, Faradila, Naim, Kamarul Hawari, Ghazali, Norrima, Mokhtar

“…The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. …”
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Using the bees algorithm to optimise a support vector machine for wood defect classification by Pham, D.T, Muhammad, Zaidi, Mahmuddin, Massudi, Ghanbarzadeh, Afshin, Koc, Ebubekir, Otri, Sameh

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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

“…This paper presents an intelligent system to reduce Non Technical Loss (NTL) using hybrid Support Vector Machine (SVM) and Genetic Algorithm (GA). …”

Enhanced ontology-based text classification algorithm for structurally organized documents by Oleiwi, Suha Sahib

“…This research combines the ontology and text representation for classification by developing five algorithms. The first and second algorithms namely Concept Feature Vector (CFV) and Structure Feature Vector (SFV), create feature vector to represent the document. …”
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The influence of sentiments in digital currency prediction using hybrid sentiment-based Support Vector Machine with Whale Optimization Algorithm (SVMWOA) by Hitam, Nor Azizah, Ismail, Amelia Ritahani, Samsudin, Ruhaidah, Ameerbakhsh, Omair

“…Support Vector Machine (SVM) technique is used with the Whale Optimization Algorithm (WOA) which is inspired by the swarm optimization algorithms. …”
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Mobile robot path optimization algorithm using vector calculus and mapping of 2 dimensional space by Zahari, Ammar, Ismail , Amelia Ritahani, Desia, Recky

“…This research explores path integration in mobile robot navigation and path optimization technique using vector calculus. A simulated robot in a simulated environment is used to test the algorithm that is to be developed. …”
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Web Algorithm search engine based network modelling of Malaria Transmission by Eze, Monday Okpoto

“…MATLAB was used to implement the model system. The output shows the public places which habour the infected malaria vectors, and their corresponding vector densities. …”
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Optimizing support vector machine parameters using continuous ant colony optimization by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous parameters, there is a need to discretize the continuous value into a discrete value.This discretization process results in loss of some information and, hence, affects the classification accuracy and seek time.This study proposes an algorithm to optimize Support Vector Machine parameters using continuous Ant Colony Optimization without the need to discretize continuous values for Support Vector Machine parameters.Seven datasets from UCI were used to evaluate the performance of the proposed hybrid algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques.Experimental results of the proposed algorithm also show promising performance in terms of computational speed.…”
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Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method by Abbaszadeh M., Soltani-Mohammadi S., Ahmed A.N.

Vector Evaluated Gravitational Search Algorithm (VEGSA) for multi-objective optimization problems by Zuwairie, Ibrahim, Badaruddin, Muhammad, Kamarul Hawari, Ghazali, Lim, Kian Sheng, Sophan Wahyudi, Nawawi, Zulkifli, Md. Yusof

“…The proposed algorithm, which is called Vector Evaluated Gravitational Search Algorithm (VEGSA), uses a number of populations of particles. …”
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