98 skills found · Page 1 of 4
photonixapp / PhotonixA modern, web-based photo management server. Run it on your home server and it will let you find the right photo from your collection on any device. Smart filtering is made possible by object recognition, face recognition, location awareness, color analysis and other ML algorithms.
ahmetozlu / Vehicle Counting Tensorflow:oncoming_automobile: "MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.
ahmetozlu / Color Recognition:art: Color recognition & classification & detection on webcam stream / on video / on single image using K-Nearest Neighbors (KNN) is trained with color histogram features by OpenCV.
jasur-2902 / CarRecognitionThis is one of the best vehicle recognition applications. It can determine the car's license plate number, color, model, brand and year.
beerboaa / Color Classification CNNcolor recognition (convolutional neural network implemented in Keras)
gyhandy / Humanoid Vision Enginecode for [ECCV 2022 paper] Contributions of Shape, Texture, and Color in Visual Recognition
hardik0 / Deep Learning With GoogleColabDeep Learning Applications (Darknet - YOLOv3, YOLOv4 | DeOldify - Image Colorization, Video Colorization | Face-Recognition) with Google Colaboratory - on the free Tesla K80/Tesla T4/Tesla P100 GPU - using Keras, Tensorflow and PyTorch.
StevieG47 / Matlab ComputerVisionCar Tracking, Lane Detection, Traffic Sign Recognition, Homography, Color Segmentation, Visual Odometry
Event-AHU / COESOT[Pattern Recognition 2025] A large-scale benchmark dataset for color-event based visual tracking
MikeDean2367 / AI EYEAI Eye的手机端的代码。可以实现视力检测、色盲检测、散光检测等，同时基于Mediapipe开发，实现了单目摄像头的测距和手势识别。This is the android version of app named "AI-Eye" using Mediapipe. It can realize vision detection, color blindness detection, astigmatism detection, etc. At the same time, based on the development of Mediapipe, it realizes the ranging and gesture recognition of monocular camera.
farukalamai / Traffic Lights Detection And Color Recognition Using Yolov8traffic-lights-detection-and-color-recognition-using-yolov8
walton-wang929 / Color Recognitioncolor recognition methods(kmeans and hsv)
nileshchopda / Traffic Light Detection And Color RecognitionTraffic Light Detection using Tensorflow Object Detection API and Microsoft COCO Dataset
DFRobot / Pxt DFRobot HuskyLensHuskyLens is an easy-to-use ai vision sensor with six built-in functions: face recognition, object tracking, object recognition, line tracking, color recognition, and label (qr code) recognition. Only one button is needed to complete the AI training, which can get rid of tedious training and complicated visual algorithm, and make you more focused on the conception and implementation of the project.
rezafuad / Vehicle Color RecognitionNo description available
Abhay-Chirania / Hand WriteHand gesture recognition based whiteboard that allows you to write on live webcam. This is the first version and has features like 4 different colors, eraser and a recording option that records your session and saves it in a "recordings" folder. Use index finger to draw and two or more fingers to move around and select items. Future version will contain more functionalities like changeable thickness, color palette, integration with zoom and google meet etc.
fulin426 / Color PicGenerate color palettes with image recognition
MarvinKweyu / ColorDetectImage processing: Detect and identify different color objects in an image/video
TheDeveloperMask / Vehicle Recognition Api Yolov4 PythonVehicle Recognition API - brand and color classification
Shaobinggao / Multi Illuminant Based Color ConstancyCombining bottom-up and top-down visual mechanisms for color constancy under varying illumination. This repository contains the datasets and codes published for color constancy under varying illmunations. -----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ Copyright (c) 2019 All rights reserved. This doucuments are a rough version for summarizing the results and codes in publication [1], which is available only for research purpose. We preserve the rights to further correct and update the data. This dataset contains three datasets for color constancy under varying illuminations, which are used in publication [1]. real-world dataset with multi-illuminant: the real-world dataset contains 37 images captured under vairous non-uniform light sources. synthetic dataset with multi-illuminant: the dataset with the synthetic multiple illuminants contains 100 images. MCC-BU+TD: This dataset contains results of multiple MCC algorithms on several real-world images taken from the web, which could be easily used and compared in any research publications. More information please refer to readme.txt in each folder. If you use this dataset for the evaluation of your approach and producing the results, please cite our work as follows: [1] S. Gao, Y. Ren, M. Zhang and Y. Li, "Combining bottom-up and top-down visual mechanisms for color constancy under varying illumination," in IEEE Transactions on Image Processing. doi: 10.1109/TIP.2019.2908783 [2] X.-S. Zhang, S.-B. Gao, R.-X. Li, X.-Y. Du, C.-Y. Li, and Y.-J. Li, “A retinal mechanism inspired color constancy model,” IEEE Transactions on Image Processing, vol. 25, no. 3, pp. 1219–1232, 2016. [3] K.-F. Yang, S.-B. Gao, Y.-J. Li, and Y. Li, “Efficient illuminant estimation for color constancy using grey pixels,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 2254–2263. [4] Gao, S. B., Yang, K. F., Li, C. Y., & Li, Y. J. (2015). Color constancy using double-opponency. IEEE transactions on pattern analysis and machine intelligence, 37(10), 1973-1985. Any questions and comments are welcome to gaoshaobing@scu.edu.cn
