Computer Vision Algorithm Engineer
Resume:
EXPERIENCE
SKILL HIGHLIGHTS
EDUCATION
CONTACT
Tyhzair Martin
Philadelphia, PA, USA
**************@*****.***
**************@*****.***
Bachelor of Science:
Computer Engineering 2013
University of Illinois Urbana-
Champaign, IL
Algorithm
Math
C++
Python
Computer Vision
Deep Learning
LSTM
CNN
ACHIEVEMENT
19th place in ACM/ICPC
World Finals 2010
AI & Algorithm Architect
Apr 2022 To Jun 2024
Aug 2019 To Nov 2021
meTech1021
meTech1021
Library Media Database Indexing and Retrieval System AI & Algorithm Engineer
Data Collection: Gathered a diverse set of labeled images and videos retrieval system based on each category.
Preprocessing: Applied resizing, normalization, and data augmentation to clean and prepare the images and videos for analysis. Extract key frames from videos for uniform processing.
Feature Extraction: Implemented a convolutional neural network
(CNN) for image data and a combination of CNN and LSTM for video data to extract relevant features from images and temporal features from video sequences.
Database Construction: Organize extracted features and metadata
(labels, timestamps) into a structured database, which supports efficient querying and retrieval.
Indexing and Search: Developed a retrieval system using techniques nearest neighbor search to allow for fast and accurate matching of user queries with content in the database.
3D Photosphere Generation Algorithm
Algorithm Developer
Keypoint Extraction: Utilized feature detection algorithm in Python and C++ to extract keypoints from each photo, leveraging GPU acceleration with CUDA for improved performance.
Keypoint Matching: Implemented feature matching algorithms to match keypoints between adjacent photos. Optimized the matching process using parallel processing techniques on the GPU to handle large datasets efficiently.
Homography Estimation: Estimated the homography matrix or camera pose between each pair of matched photos using RANSAC
(Random Sample Consensus) to filter outliers, ensuring robust performance in various lighting conditions and scenes.
Image Stitching: Warped and stitched the images together based on the computed homographies. Leveraged CUDA to accelerate the image transformation and blending processes.
Cloud Infrastructure: Deployed the entire workflow on AWS, utilizing auto-scaling to manage resource allocation dynamically based on workload demands, ensuring efficient processing of large batches of images.
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