106 skills found · Page 3 of 4
adnan0819 / Tree Instance Segmentation Using Temporal Structured ImagesCVPR 2023 Data Contribution
ssyc123 / SISegOfficial code of the paper "Synthetic Instance Segmentation from Semantic Image Segmentation Masks"
peterlipan / Awesome Multi Instance Learning For Whole Slide ImagesA curated list of awesome Multi-instance Learning frameworks for Whole Slide Images (WSIs) classification, segmentation, etc.
YIBO0408 / Yolov8 Onnxruntime Cpp基于官方yolov8的onnxruntime的cpp例子修改，目前已经支持图像分类、目标检测、实例分割。Based on the cpp example modification of official yolov8's onnxruntime, it currently supports image classification, target detection, and instance segmentation.
arpsn123 / Dental X RAY Image Detection And Instance SegmentationThis repository presents a comprehensive solution for teeth segmentation on dental X-ray images using the powerful Detectron2 framework. With the increasing demand for automated dental image analysis, accurate segmentation of teeth is crucial for various applications in dentistry, including diagnosis, treatment planning, and research.
vaghawan / Airbus Ship Detection Using Keras Retinanet MaskrcnnDetection and segmentation of ship instances in satelite images using Keras Retinanet and Keras MaskRcnn.
DeepSportradar / Instance Segmentation ChallengeAn instance segmentation challenge on Basketball images, with a particular focus on occlusion resolution. An opportunity to publish at MMSports @ ACMMM and to win $1000.
BernardNyarko / Smart Monitor An AI Powered IoT Monitoring System For Small Medium Scale PremisesWith recent advances in both Artificial Intelligence (AI) and Internet of Things (IoT) capabilities, it is more possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time. Imagine a surveillance camera system that can detect various on-body weapons, suspicious objects, and traffic. This system could transform surveillance cameras from passive sentries into active observers, which would help prevent a possible mass shooting in a school, stadium, or mall. In this project, we tried to realize such systems by implementing Smart-Monitor, an AI-powered threat detector for intelligent surveillance cameras. The developed system can be deployed locally on the surveillance cameras at the network edge. Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images on-site, reducing the communication overheads and enabling swift security actions. We developed a mobile app that users can detect suspicious objects in an image and video captured by several cameras at the network edge. Also, the model can generate a high-quality segmentation mask for each object instance in the photo, along with the confidence percentage. The camera side used a Raspberry Pi 4 device, Neural Compute Stick 2 (NCS 2), Logitech C920 webcam, motion sensors, buzzers, pushbuttons, LED lights, Python Face recognition, and TensorFlow Custom Object Detection. When the system detects a motion in the surrounding environment, the motion sensors send a signal to the Raspberry Pi device notifying it to start capturing images for such physical activity. Using Python’s face recognition and TensorFlow 2 custom object detection Smart-Monitor can recognize eight classes, including a baseball bat, bird, cat, dog, gun, hammer, knife, and human faces. Finally, we evaluated our system using various performance metrics such as classification time and accuracy, scalability, etc.
apurv-nigam / 6DPoseEstimationCNNMulti Stage Convolutional Neural Network Based 6D Pose Estimation. In this repo, I provide code for my [IROS 2018 ]paper, "Detect Globally, Label Locally: Learning Accurate 6-DOF Object Pose Estimation by Joint Segmentation and Coordinate Regression". Paper proposes a deep architecture with an instance-level object segmentation network that exploits global image information for object/background segmentation and a pixel-level classification network for coordinate regression based on local features. We evaluate our approach on the standard ground-truth 6-DOF pose estimation benchmarks and show that our joint approach to accurate object segmentation and coordinate regression results in the state-of-the-art performance on both RGB and RGB-D 6-DOF pose estimation.
tzolov / Spring Boot Tensorflow DemoSpring Boot and Tensorflow demos for Image-Recognition, Pose-Estimation, Object-Detection, Instance-Segmentation
Justlovesmile / SISP(2024) The Official Repository of Paper "SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite Images"
sagieppel / Pointer Based Segmentation Find Segment Containing Given Image Point Witin Given ROI Mask Using CNNCategory independent sequential image segmentation. Find a segment containing a given image pixel within a given attention mask using a convolutional neural network (CNN), and its application for sequential region-by-region instance-aware segmentation of images with unfamiliar categories.
InformationSystemsFreiburg / Image Segmentation JapanThis small tutorial is targeted at researchers that have basic machine learning and Python programming skills that want to implement instance image segmentation for further use in their models.
arpsn123 / YoloTeeth X Ray Instance Segmentation Object Detection With YOLOv8YoloTeeth is a GitHub repository dedicated to leveraging YOLOv8 for precise instance segmentation and object detection in teeth X-ray images. The project aims to streamline dental image analysis by accurately identifying individual teeth, facilitating efficient diagnosis and research in dental healthcare.
thisishardik / Electrical Substation DetectionMachine Learning based feature extraction of electrical substations from satellite data. Powered by IEEE-ICETCI, RRSC-Central, NRSC, and ISRO, this project incorporates instance segmentation of substations using UNet, Albumentations for image augmentation, and OpenCV for computer vision tasks.
Michelle-NYX / DreamNetCS230 Project: In this project, we investigate and evaluate the performance of the state-of-the-art model for instance segmentation, Mask R-CNN, on the newly-released Mapillary dataset, whose images focus specifically on driving scenes. We transfer the learning results from the pre-trained weights, fine tune the final layers for Mapillary Datasets. The result shows a significant improvement in precision measurements from the baseline, and achieves at a surpassing performance than benchmarks.
dpeerlab / MaskRCNN CellAn implementation of Mask R-CNN designed for single-cell instance segmentation in the context of multiplexed tissue imaging
saraivaufc / Instance Segmentation MaskrcnnInstance segmentation of center pivot irrigation system in Brazil using Landsat images and Convolutional Neural Network
danqu130 / EvInsMOSInstance-Level Moving Object Segmentation from a Single Image with Events (IJCV 2025)
AI-Application-and-Integration-Lab / CL4WSIS[ICCV 2023] Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision
