ar07530@st.habib.edu.pk
I am a Computer Science student with hands-on experience in Computer Vision, Machine Learning, and Geospatial Systems. I am passionate about building intelligent, real-world solutions.<\/p>\n
ar07530@st.habib.edu.pk
alirazaaijaz0@gmail.com
https:\/\/www.linkedin.com\/in\/ali-raza-580a3a201\/
Portfolio<\/p>\n
+923327951497<\/p>\n
Dhanani School of Science and Engineering
BS Computer Science<\/p>\n
2025
Bash|
\nC++|
\nPython|
\nSql|
N\/A
Cyber Security ; Data Analyst; AI\/ML Engineer ;
N\/A
Event Lead,Gaming Club Habib Debate Union|Senior Editor,Pride Press|General Secretary,Pride Press|Sports Representative,Sports & Recreational Club|Final Year Project Showcase Participant|
N\/A|
N\/A
Drone Image Stitching
In this project, we developed a cost-effective alternative to satellite imaging by stitching high-resolution drone images into ultra-large panoramas. Using 150\u2013200 images per scene at 4K\u20138K resolution, the pipeline applies classical feature detection algorithms (SIFT, SURF, ORB) alongside modern deep learning matchers (SuperGlue, LoFTR) to align and blend images seamlessly. Feature matching via BF Matcher and FLANN, combined with RANSAC-based homography estimation, ensures geometric accuracy, while multiband and feathering techniques produce smooth final outputs. The project benchmarks classical vs. deep learning approaches on accuracy and computational cost, offering insights into scalable, affordable aerial imaging solutions applicable to agriculture, urban planning, disaster response, and environmental monitoring.<\/p>\n
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ar07530@st.habib.edu.pkI am a Computer Science student with hands-on experience in Computer Vision, Machine Learning, and Geospatial Systems. I am passionate about building intelligent, real-world solutions. ar07530@st.habib.edu.pkalirazaaijaz0@gmail.comhttps:\/\/www.linkedin.com\/in\/ali-raza-580a3a201\/Portfolio +923327951497 Dhanani School of Science and EngineeringBS Computer Science 2025Bash| C++| Python| Sql|N\/ACyber Security ; Data Analyst; AI\/ML Engineer ;N\/AEvent Lead,Gaming Club Habib Debate Union|Senior Editor,Pride Press|General Secretary,Pride Press|Sports […]<\/p>\n","protected":false},"author":9,"featured_media":7185,"menu_order":0,"comment_status":"","ping_status":"","template":"single-graduate-01.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"program":[29],"school":[42],"class":[41],"minor":[],"competencies":[],"class_list":["post-8078","graduate","type-graduate","status-publish","format-standard","has-post-thumbnail","hentry","program-bscs","school-dhanani-school-of-science-and-engineering","class-class-of-2025"],"acf":{"scholarship":"","aspiration_statement":"I am a Computer Science student with hands-on experience in Computer Vision, Machine Learning, and Geospatial Systems. I am passionate about building intelligent, real-world solutions.","video_profile_link":"","habib_email":"ar07530@st.habib.edu.pk","email":"alirazaaijaz0@gmail.com","linkedin_link":"https:\/\/www.linkedin.com\/in\/ali-raza-580a3a201\/","portfolio_Title":"Portfolio","portfolio_link":"","personal_cell_1":"+923327951497","personal_cell_2":"","core_skills":[{"skill":"Bash"},{"skill":"C++"},{"skill":"Python"},{"skill":"Sql"}],"academic_awards":[{"academic_record":"N\/A"}],"leadership-experiences":[{"leadership":"Event Lead, Gaming Club Habib Debate Union"},{"leadership":"Senior Editor, Pride Press"},{"leadership":"General Secretary, Pride Press"},{"leadership":"Sports Representative, Sports & Recreational Club"},{"leadership":"Final Year Project Showcase Participant"}],"work-experience":[{"experience":"N\/A"}],"publications-projects":[{"publications_title":"","publications":"N\/A"}],"project_title":"Drone Image Stitching","fyp_desc":"In this project, we developed a cost-effective alternative to satellite imaging by stitching high-resolution drone images into ultra-large panoramas. Using 150\u2013200 images per scene at 4K\u20138K resolution, the pipeline applies classical feature detection algorithms (SIFT, SURF, ORB) alongside modern deep learning matchers (SuperGlue, LoFTR) to align and blend images seamlessly. Feature matching via BF Matcher and FLANN, combined with RANSAC-based homography estimation, ensures geometric accuracy, while multiband and feathering techniques produce smooth final outputs. The project benchmarks classical vs. deep learning approaches on accuracy and computational cost, offering insights into scalable, affordable aerial imaging solutions applicable to agriculture, urban planning, disaster response, and environmental monitoring.","project_pictures":null,"project-video":""},"yoast_head":"\n