194 skills found · Page 5 of 7
WenxueCui / KPN Denoising PytorchReimplement of Burst Denoising with Kernel Prediction Networks and Multi-Kernel Prediction Networks for Denoising of Image Burst by using PyTorch.
adelbibi / Multi Template Scale Adaptive Kernelized Correlation FiltersCode and data
icecube / KdeKernel Density Estimation: accelerated, multi-dimensional, and adaptive bandwidth
Mog9 / Kernel FusionA CUDA Python experiment demonstrating kernel fusion by combining ReLU and LayerNorm into a single GPU pass and comparing it against the unfused multi-kernel pipeline.
KWflyer / WTFFWide Kernel Time-Frequency Fusion （WTFF）--Multi-Domain Time-Frequency Fusion Feature Contrastive Learning for Machinery Fault Diagnosis
mehdihadeli / Genai Eshop Semantic KernelPractical GenAI-Eshop application using Semantic Kernel, multi-agent orchestrations, Mcp tools, A2A Agents, Semantic Search, Aspire and more.
karrenberg / WfvopenclIMPORTANT NOTICE: This implementation is long outdated. WFVOpenCL is an OpenCL driver for CPUs on the basis of LLVM. This driver employs Whole-Function Vectorization (WFV) in addition to multi-threading to fully exploit the available data-parallelism by executing as many kernel instances in parallel as possible.
swatijha2496 / FACE RECOGNITION USING OPENCV IN PYTHONFace is most commonly used biometric to recognize people. Face recognition has received substantial attention from researchers due to human activities found in various applications of security like airport, criminal detection, face tracking, forensic etc. Compared to other biometric traits like palm print, Iris, finger print etc., face biometrics can be non-intrusive. They can be taken even without user’s knowledge and further can be used for security based applications like criminal detection, face tracking, airport security, and forensic surveillance systems. Face recognition involves capturing face image from a video or from a surveillance camera. They are compared with the stored database. Face biometrics involves training known images, classify them with known classes and then they are stored in the database. When a test image is given to the system it is classified and compared with stored database. Face biometrics is a challenging field of research with various limitations imposed for a machine face recognition like variations in head pose, change in illumination, facial expression, aging, occlusion due to accessories etc.,. Various approaches were suggested by researchers in overcoming the limitations stated. 72 Automatic face recognition involves face detection, feature extraction and face recognition. Face recognition algorithms are broadly classified into two classes as image template based and geometric feature based. The template based methods compute correlation between face and one or more model templates to find the face identity. Principal component analysis, linear discriminate analysis, kernel methods etc. are used to construct face templates. The geometric feature based methods are used to analyze explicit local features and their geometric relations (elastic bung graph method). Multi resolution tools such as contour lets, ridge lets were found to be useful for analyzing information content of images and found its application in image processing, pattern recognition, and computer vision. Curvelets transform is used for texture classification and image de-noising. Application of Curvelets transform for feature extraction in image processing is still under research.
Bosch-SW / Linux IccomLinux kernel multi- bidirectional- logical- channel communication driver for connecting User/Kernel space entities via full-duplex symmetrical transport.
tugrul512bit / LibGPGPUMulti-GPU & CPU OpenCL kernel executor with load-balancing as if there is one big GPU.
akshaykokane / Implementing Multi Agent With A2A SemanticKernelNo description available
lsl-zsj / MKMCKF OEOrientation estimation for IMUs using multi-kernel maximum Correntropy Kalman filter
liujikun / Multi Kernel Joint Domain MatchingA Multi-Kernel domain adaptation method for unsupervised transfer learning on cross-source and cross-region remote sensing data classification
philippjbauer / Code Presents Sk AgentsThis repository contains the code samples and practical implementations from the "CODE Presents" webinar "Building Intelligent Multi-Agent Systems with Microsoft's Semantic Kernel." The session demonstrates how to orchestrate collaborative AI agents in .NET, featuring persistent vector memory and MCP tool integration.
wangsiwei2010 / Multiple Kernel K Means Clustering With Matrix Induced Regularizationmatlab code for AAAI16：Multiple Kernel k-Means Clustering with Matrix-Induced Regularization
jasmarc / MRVMMulti-class, Multi-kernel Relevance Vector Machine
root-11 / MasliteA very fast multi agent messaging kernel in Python
microsoft / SkmultiagentsMulti-Agents Demo using Semantic Kernel and Azure AI Foundry Agents Service
qintian0321 / SimpleMKL PythonPython implementation of SimpleMKL algorithm for multi-kernel SVM learning
bertaveira / Mojosplat3D Gaussian Splatting kernels implemented in Mojo for PyTorch - Exploring multi-vendor GPU performance
