yudhisteer Goto Github PK
Name: Yudhisteer
Type: User
Bio: Computer Vision | Machine Learning Engineer
Location: Seattle, Washington
Name: Yudhisteer
Type: User
Bio: Computer Vision | Machine Learning Engineer
Location: Seattle, Washington
This repositiory is about exploring fun datasets to extract insights and be able to adopt meaningful data visualization for storytelling.
The project explores 3D reconstruction using Multi-View Stereo (MVS) and Structure from Motion (SfM). We show the equations how to calibrate an uncalibrated stereo then reproject our image to 3D space.
Predictive Maintenance: Anomaly Detection in AC Motos for Dyeing Department
This project delves into NeRF basics, such as ray tracing, ray casting, and ray marching. It starts with a simple task of reconstructing a red sphere in 3D. Then, we create a custom 3D model in Blender and take images to reconstruct the model using NeRF built from scratch and train for 7 hours!
The second phase of the AR project for predictive maintenance is to combine IoT and AR to get a real-time visualization of the status of machines in the production plants.
For the first phase of the AR project, the goal was to show how AR could be used in preventive maintenance and in the training of maintenance crews.
The goal of this project is to select an optimized container dimensions to load products that will reduce space wastage. By optimzing the total volume occupied by the products and their total weights, we seek the best orientation and position of the products during loading.
This project consists of research on a 6 degree of freedom robot. A 3D model of the robot has been made on Unity to demonstrate Forward and Inverse Kinematics. Important concepts in robotics like Singularities, Path planning and Motion Control are explained as well.
This project involves automating the attendance system of RT Knits using Face Recognition. Due to Covid-19, people are obliged to wear masks hence, the system is successful in recognizing people despite wearing masks.
The purpose of the project is to understand a basic GAN from scratch. A WGAN was built to generate people's faces based in the Celeba Dataset. VQGAN + CLIP model was used to generate unique designs that would be used in fashion.
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Lane detection using Semantic Segmentation. To be used in industries by autonomous shuttles in a controlled environment.
In this project I aim to develop an unsupervised sense-and-avoid system for UAVs using sparse and dense optical flow.
This project consisted of automating the process of taking measurements of garments in the Quality Control Department.
The second phase of the project is to automate the process of taking garment measurements and data entry using Computer Vision.
Using the KITTI dataset, we employed Open3D to visualize, downsample, segment with RANSAC, cluster via DBSCAN, create 3D bounding boxes, and perform surface reconstruction on point clouds.
This project is about beating the market and debugging a myth: LSTM + Stock Market = $$$.
This project focuses on harnessing the power of Pseudo-LiDARs and 3D computer vision for unmanned aerial vehicles (UAVs). By integrating Pseudo-LiDAR technology with Stereo Global Matching (SGBM) algorithms, we aim to enable UAVs to perceive their surroundings in three dimensions accurately.
This project shows the implementation of tracking algorithms like SORT and Deep SORT from scratch. The project aims to assist emergency responders in assessing the number of people at a disaster site and tracking their movements for rescue operations, especially in situations where they are being carried away by floodwaters.
The goal of the project was to design the logistic model of autonomous robots that would supply garment parts from the Cutting Dept to the Makeup Dept in the shortest time possible and using the most optimized path.
This project focuses on training robots to grasp everyday objects accurately. We gather a unique point cloud dataset using an iPhone's LiDAR and process it with Polycam. We develop a PointNet model from scratch to perform multi-class classification and part-segmentation, guiding the robot on where to grasp objects.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.