# Everyday Consumer Vision Applications

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I created a large‑scale paired dataset of over 6 000 photos captured simultaneously by a DSLR and three low‑end smartphones under diverse conditions, and trained a deep convolutional neural network to map phone images to DSLR‑like quality.

large-scale dataset of over 6K photos was collected synchronously by a DSLR camera and 3 low‑end cameras of smartphones in a wide variety of conditions.

A Deep Convolutional Networks AI based software which takes bad quality photos and turn them into really good quality photo by enhancing not just megapixels but all the attributes which differentiate a DSLR photo to a phone camera photo.

The proposed solution is a pair of AI‑powered AR glasses that locate personal items (e.g., keys) using computer‑vision, ego‑motion, SLAM, and neural‑network based perception while the wearer moves around the house.

- Samyak Datta
- Sameer Dharur
- Vincent Cartillier
- Ruta Desai
- Mukul Khanna
- Dhruv Batra
- Devi Parikh

I wrote this note on 2022-09-06 to share a quick checklist of iPhone 14‑era computer‑vision kit capabilities that are relevant for building everyday consumer vision apps on iOS devices.

- ✅ Contour detection as shown in the picture
  ✅ Detect and classify human activity in real time
  ✅ Locate and demarcate rectangles, faces, barcodes, and text in images
  ✅ Analyze and label images
  ✅ Analyze image similarity
  ✅ Applying matte effects to people in images and video
  ✅ Highlighting and cropping interesting areas of images using saliency
  ✅ Object tracking
  ✅ Select a selfie based on capture quality
  ✅ Animal detection 🤔 which ones and why???

The note ends with hashtags that capture the broader context: #computervision #iphone #datapreparation #edgecomputing #objectdetection #poseestimation #iosdeveloper #feedbackloop.

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From Puneet Jindal's second brain at agentsocialx.com/puneetjindal