Computer Engineering Student & Undergraduate Researcher
I study how visual evidence can make multimodal AI systems more reliable by combining object-level verification, image segmentation, and geometric consistency with multimodal reasoning. My work connects food recognition, visual grounding, and portion estimation with knowledge-based retrieval and targeted recovery of missed or uncertain food items.
I am Jingbo (Jackie) Yue, a Computer Engineering student at Purdue University. I conduct undergraduate research in the Video and Image Processing Laboratory (VIPER), advised by Prof. Fengqing Zhu.
My recent research examines the reliability of image-based dietary assessment: how food regions can be verified using semantic and geometric evidence, how missed items can be recovered through targeted segmentation review, and how identified foods can be grounded to nutritional reference data.
I previously conducted computer vision research on image-based plant phenotyping and herbicide-injury assessment at Purdue's ABE Plant Sensor Lab (LeafSpec), led by Prof. Jian Jin. Beyond these applications, I enjoy connecting algorithmic image analysis with practical systems engineering.
Region-level segmentation, spatial consistency checks, and object-centric recognition. I am interested in combining semantic evidence with geometric constraints to reduce false-positive detections and missed food items.
Structured MLLM inference with item-wise verification, source-blind re-checking, and a targeted residual review. The goal is to recover omitted visual evidence without uncontrolled generation or repeated refinement.
Mapping visually identified foods to reference databases such as FNDDS, with attention to ingredient selection, retrieval quality, portion/mass estimation, and downstream nutritional accuracy.
Jingbo Yue, Bruce Coburn, Jinge Ma, Jui-Feng Chi, Fengqing Zhu
arXiv:2610.11144 · Submitted to IEEE ICASSP 2027
Bruce Coburn, Jingbo Yue, Jinge Ma, Siddeshwar Raghavan, Gautham Vinod, Fengqing Zhu
Accepted to IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2027) · arXiv:2607.12911
Training-free item verification with semantic/geometric evidence and a one-round, segmentation-assisted residual recovery step for image-based nutrition estimation.
Knowledge-grounded dietary assessment with agentic decomposition, selective retrieval from FNDDS, and structured MLLM-based inference.
Senior design prototype linking ESP32 controllers, an interactive touchscreen, NFC interactions, and UART communication, with a classical image-processing approach for board-state recognition.
Course project exploring image-based plant phenotyping and herbicide-injury assessment. The public repository contains Jupyter notebook experiments and final presentation materials.
Morphology · Edge detection · HSV color space
HOG descriptors · ROI extraction · Image features
Support Vector Machines · Classification
Homography · Perspective transformation · Mapping
Python · PyTorch · OpenCV · Jupyter
Linux · Git · CUDA workflows
C / C++ · ESP32 · UART · Microcontrollers
Dataset curation · Error analysis · Validation
Data structures · Digital signal processing
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Public research and engineering work.
Research collaborations, academic inquiries, and professional opportunities.