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# Phillip Catalana

**Headline:** Professional profile
**Location:** Davis, CA, USA

## About

Phillip Catalana develops image\-classification and defect\-detection solutions using both classical computer vision and deep learning\. His work includes a bottle defect detection system that combined MATLAB\-based vision techniques with a ResNet\-18 classifier trained on a 200\-image dataset\. Phillip achieved 97\.8% classification accuracy on that defect\-detection work\. He is particularly experienced with classical computer vision in MATLAB, including edge detection and defect marking, and has applied image\-comparison methods to complex visual inspection challenges\. Phillip also improved model performance through sensitivity tuning during the development process\. As he explores opportunities, Phillip’s current priorities are expanding his professional network and building experience\. He is open to roles across industries and does not limit his search to a particular team or company culture\. Phillip plans early while evaluating opportunities and approaches new settings with an open\-minded interest in learning broadly and developing practical experience\.

## Highlights

- Built a bottle defect detection system using classical computer vision in MATLAB and a ResNet\-18 classifier\.
- Trained a ResNet\-18 image\-classification neural network on a 200\-image dataset for bottle defect detection\.
- Achieved 97\.8% accuracy on the bottle defect detection system\.
- Applied MATLAB\-based classical computer vision techniques, including edge detection and defect marking\.
- Solved a complex image\-comparison challenge in visual inspection work\.
- Improved image\-classification accuracy through sensitivity tuning\.
- Is expanding his professional network and building experience while exploring opportunities\.

## FAQ

### What does Phillip do?

Phillip Catalana develops image\-classification and defect\-detection solutions using classical computer vision and deep learning\. He is currently focused on expanding his professional network and building experience as he explores opportunities\.

### What did Phillip build for bottle defect detection?

Phillip built a bottle defect detection system using classical computer vision in MATLAB alongside a ResNet\-18 image classifier\. The classifier was trained using a 200\-image dataset\.

### What accuracy did Phillip achieve with ResNet\-18?

Phillip trained a ResNet\-18 neural network for the bottle defect detection system and achieved 97\.8% image\-classification accuracy\.

### What classical computer vision experience does Phillip have?

Phillip has experience with classical computer vision in MATLAB, including edge detection and marking defects in images\.

### How did Phillip approach complex image\-comparison and accuracy challenges?

Phillip addressed a complex image\-comparison challenge as part of his visual inspection work\. He also improved accuracy through sensitivity tuning\.

### What are Phillip’s current career priorities?

Phillip’s current career priorities are building experience and expanding his professional network\.

### What team or company culture does Phillip prefer?

Phillip is open to different team and company cultures and does not have a specific culture preference\.

### How does Phillip approach new career opportunities?

Phillip plans early while exploring opportunities and is open\-minded about learning across settings as he builds broad experience\.

## Links

- LinkedIn: https://www\.linkedin\.com/in/ACoAAFLBu9MBDO6TEXVmZYf3hgKZgd\_\_k6odeUM

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