8 skills found
mooch443 / TrexTRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields.
pirahansiah / OpencvOpenCV 3 , Visual C++ 2015 , win 64x , computer vision, image processing, webcam,video,motion,frame,edge,vector image processing with opencv 3 & c++ Find Faces , Modify Videos and Photos Automatically , Identify, Count & Measure , Realtime Augmented Reality Technology, An introduction to Image Processing, Tutorial Real-Time Object Tracking Using OpenCV, Face Features Detection System, Fast Object Tracking in C++ using OpenCV, How to install OpenCV and Create Sample Project in Visual Studio, Camera calibration With OpenCV, Chessboard or asymmetrical circle pattern, Installing OpenCV 3.2.0 with Visual Studio 2015 and configuring OpenCV project, OpenCv Stereo Vision, Machine Learning,Getting Started with Neural Network,Pattern Recognition and Application,Computer Vision Real-time Pattern Recognition using C++,Deep Neural Networks for Speech and Image Processing,Introduction to Segmentation,Deep Learning,Computer Vision & Machine Learning, Pattern Recognition, Camera Calibration, Optical Flow, Humanoid Robot, Image Processing, iOS developer, Augmented Reality, C++, Java, Matlab, keywords: digital image processing, OpenCV C++ Computer program tutorial, augmented virtual reality (augmented reality), deep machine learning, computer vision with C++ Programming Language, JSON, machine vision, opencv stereo camera calibration, optical flow, video analysis, Image Processing (IP) [OpenCV], Intelligent Systems, Deep Learning, Artificial Intelligence (AI) www.tiziran.com پردازش تصویر , روبوتیک , برنامه نویسی موبایل
JeongHun0716 / Vsr LowVisual Speech Recognition For Low-Resource Languages with Automatic Labels (ICASSP 2024)
Satya3720 / Rock Identification Using Deep Convolution Neural NetworkRocks are a fundamental component of Earth. The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. It is a basic part of geological surveying and research, and mineral resources exploration. The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Working conditions in the field generally limit identification to visual methods, including using a magnifying glass for fine-grained rocks. Visual inspection assesses properties such as colour, composition, grain size, and structure. The attributes of rocks reflect their mineral and chemical composition, formation environment, and genesis. The colour of rock reflects its chemical composition. But these analysis is time taken process to identify the rocks.Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. Solution: Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.Who are experienced in the field of geological they can identify the rocks easily. But who are new to the field, it can help to identify the type of rock.
GayatriBehara / Face And Facial Expression Detection Based Authenticated ZigBee Based RoboIn this project a robot that can be operated by authorized person or operator is implemented. For this purpose we use a face recognition system which is capable of identifying the authorized person which allows him to command and operate it. The face recognition system consists of a web based camera which captures the image of human and this image is processed in MATLAB software. After processing the image it generates the activation code for the robot to be operated. The hardware system is based on the ATMEL microcontroller and an Zigbee module. The system provides continuous visual monitoring through the small camera attached to the mobile robot, sending data to the control unit when necessary. Remote testing is done on the mobile robot for search and rescue missions via an established radio frequency (RF) communication using DIGI XBee RF module. Intelligent mobile robots and cooperative multi agent robotic systems can be very efficient tools to speed up search and research operations in remote areas. This prototype robot is capable of moving across area and remotely guided by a person who is directed and navigated using remote camera and computer.. Mobile robots using Zigbee protocol for the purpose of navigation using personal computer, implemented with wireless vision system for remote monitoring and control. Its main feature is its use of the Zigbee protocol as the communication medium between the mobile robot and the PC controller. The robot can be monitored only by authorized persons who are previously present in the database for security reasons. For this we utilize the Face recognition technology. It is a system which can automatically identify and verify the individuals face. Thus face and emotion recognition offers one of the most natural and less obtrusive bio metric measures of identification
1091arrao / Voice Classification Using MLWhile tightening and expansion of our facial muscles cause some changes called facial expressions as a reaction to the different kinds of emotional situations of our brain, similarly there are some physiological changes like tone, loudness, rhythm and intonation in our voice, too. These visual and auditory changes have a great importance for human- human interaction human- machine interaction and human- computer interaction as they include critical information about humans’ emotional situations. Automatic emotion recognition systems are defined as systems that can analyze individual’s emotional situation by using this distinctive information. In this study, an automatic emotion recognition system in which auditory information is analyzed and classified in order to recognize human emotions is proposed. In the study spectral features and MFCC coefficients which are commonly used for feature extraction from voice signals are firstly used, and then deep learning-based LSTM algorithm is used for classification.
darshanhs11 / Detection Of Epilepsy Using CNNThe problem of epilepsy has grown exponentially and is now considered as one of the most prevailing neurological disorders affecting around 50 million people around the globe. Epilepsy is identified by analyzing the interictal activity present in the EEG signal. Visual analysis of EEG is a tedious process and subject to human error. This work proposes a robust method to ease the burden of intractable seizures by automatic recognition of ictal epileptiform activity in the EEG of epileptic patients. The classification between EEG having an epileptic seizure and non-seizure is done using various machine learning algorithms. The classifiers used are weighted KNN, Boosted Trees, Bagged Trees, subspace discrimination, Subspace KNN and RUS boosted tree. Based on the accuracy of the classifier we will select one method and we will export the model as function to use for validation purpose. These are the methods used to classify epileptic seizure EEG signals.
ZHOU-HN / Automatic MinesweeperSimulate mouse movement and clicks to automatically play Minesweeper based on visual recognition of the minefield.
