572 skills found · Page 9 of 20
jainsee24 / Parallel Face DetectionImage segmentation is the process of dividing an image into multiple parts. It is typically used to identify objects or other relevant information in digital images. There are many ways to perform image segmentation including Thresholding methods, Color-based segmentation, Transform methods among many others. Alternately edge detection can be used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Image thresholding is a simple, yet effective, way of partitioning an image into a foreground and background. This image analysis technique is a type of image segmentation that isolates objects by converting grayscale images into binary images. Image thresholding is most effective in images with high levels of contrast. Otsu's method, named after Nobuyuki Otsu, is one such implementation of Image Thresholding which involves iterating through all the possible threshold values and calculating a measure of spread for the pixel levels each side of the threshold, i.e. the pixels that either fall in foreground or background. The aim is to find the threshold value where the sum of foreground and background spreads is at its minimum. Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. An image can have horizontal, vertical or diagonal edges. The Sobel operator is used to detect two kinds of edges in an image by making use of a derivative mask, one for the horizontal edges and one for the vertical edges. 1. Introduction Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars. Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. 2. Needs/Problems There have been widely applied many researches related to face recognition system. The system is commonly used for video surveillance, human and computer interaction, robot navigation, and etc. Along with the utilization of the system, it leads to the need for a faster system response, such as robot navigation or application for public safety. A number of classification algorithms have been applied to face recognition system, but it still has a problem in terms of computing time. In this system, computing time of the classification or feature extraction is an important thing for further concern. To improve the algorithmic efficiency of face detection, we combine the eigenface method using Haar-like features to detect both of eyes and face, and Robert cross edge detector to locate the human face position. Robert Cross uses the integral image representation and simple rectangular features to eliminate the need of expensive calculation of multi-scale image pyramid. 3. Objectives Some techniques used in this application are 1. Eigen-face technique 2. KLT Algorithm 3. Parallel for loop in openmp 4. OpenCV for face detection. 5. Further uses of the techniques
weifengliu-ssslab / Benchmark SpTRSM Using CSCFast Synchronization-Free Algorithms for Parallel Sparse Triangular Solves with Multiple Right-Hand Sides (SpTRSM)
markwkm / QuicksortParallel quicksort algorithms
HanjieLuo / EDLine ParallelA parallel implementation of EDLine algorithm.
purtroppo / PageRankC implementation of the PageRank algorithm, with and without parallelization.
uma-pi1 / DSGDppImplementations of various parallel algorithms for matrix factorization (including DSGD++)
LIS-Laboratory / CupccuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU
abarankab / Parallel BoruvkaAn implementation of parallel Boruvka's algorithm written in C++ using OpenMP.
zhangxiaoya / CCLThe implementation of algorithm Parallel graph component labelling with GPUs and CUDA.
GonguanLu / Lagrangian Relaxation Algorithm For RRSLRPTo solve the RRS-LRP problem based on resource-space-time network, we developed a Lagrangian Relaxation Algorithm framework to decompose the origin problem into classic knapsack sub-problem and vehicle routing problem with recharging station (VRP-RS). The knapsack problem is solved by dynamic programming algorithm and a dynamic programming algorithm in RST network is developed to solve the VRP-RS. The dual problem of adjusting the Lagrangian multipliers was solved by an ascent method using sub-gradients approach. The algorithm framework is naturally suitable for parallel computing and distributed computing techniques due to the decomposition structure.
ZPGuiGroupWhu / Spark Based DBSCAN AlgorithmsA parallel algorithm package for DBSCAN based on Apache Spark, including KDBSCAN, KDSG and other optimized DBSCAN algorithms. This framework consists of three parts, i.e., front-end web visualization components, Web service API component and back-end Spark-based algorithm packages.
Brionengine / Brion Quantum A.I. General SystemBrion is the world’s first quantum AI model with the deepest integrations, combining the power of quantum computing and artificial intelligence. Built on cutting-edge quantum algorithms and secure protocols, it is designed for unmatched high-speed, parallel computation that solves complex problems possibly infinitely faster.
crisbour / PhaseRetrievalCGH using Phase Retrieval Algorithm for parallel two photon polymerization.
vollmerm / Racket GaA parallel genetic algorithm implementation in Racket Scheme
nickgreenquist / Cu2recCUDA Implementation of Parallel Matrix Factorization Algorithm for Recommender Systems
v-hill / Parallel DelaunayParallel implementation of Guibas & Stolfi's divide-and-conquer algorithm for Delaunay triangulation, using MPI in Python.
architkhullar / RobotMotionPlanning TermProjectAs a part of term project (Spring 2018) for Robot Motion Planning class (Masters coursework for Computer Science), I have implemented Reduced Visibility Graph, path finding algorithm for a parking space scenario with parallel and reverse parking slots for a car
sallesviana / PinMeshPinMesh is an exact and efficient parallel algorithm for preprocess a polyhedral triangular mesh and, then, query a cloud of points against the mesh.
Yonhoo / 3D Connect Component Label With GPUThis is my 3D parallel algorithm, but it is not perfect. It provides a way for you to optimize it. I hope someone can give a good opinion
tueboesen / Sparse Iterative Parallel Linear SolverThis is a sparse iterative parallel linear solver I developed during my PhD. The solver works by splitting the matrix into blocks which are then solved in parallel. The solver assumes the input matrix to already have been ordered by a Reverse Cuthill-Mckee reordering algorithm.
