Learn how to generate or manipulate images with our DALL·E models

 

Very Realistic, New variations!!!

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Robotics and Vision at Amazon

• Multiple cameras provide a set of eyes on the scene 
• 3D scene understanding is generated by multiple camera angles
• Motion algorithms take this understanding of the scene and item identification 
• Recognize and estimate the shape of individual items
• Pinch-grasping robot achieved a 10-fold reduction in damage on certain items
• A robot optimized for deployment in a fulfillment center may require suction and pinching
• Depending on the type of object recommend a certain way to handle

Ref - Link

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NLP Tools

Open-source tool for data-centric NLP. Argilla helps domain experts and data teams to build better NLP datasets in less time. = 

Haystack is an open source NLP framework that leverages pre-trained Transformer models.

VidGear is a High-Performance Video Processing Python Library that provides an easy-to-use, highly extensible, thoroughly optimised Multi-Threaded + Asyncio API Framework

Lightly is a computer vision framework for self-supervised learning.

An image hashing library written in Python

Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python.

PaddleSeg is an end-to-end high-efficent development toolkit for image segmentation based on PaddlePaddle

Best-of Machine Learning with Python

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Vision and Manufacturing

 

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Vision and Retail / LEGO

 

• Manual process to automated process
• High-quality image checks
• Better attention-based models

Ref - Link

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Computer Vision Landscape 2022 Report

Why Computer Vision?

Hardware used

• High-resolution types
• Compact types
• High-speed types
• Standard types

Illumination techniques

• Backlighting - Detect the presence or absence of gaps or holes, identify bubbles, cracks, or scratches
• Dark field lighting - Features like edges, scratches, or notches
• Bar lighting
• Dome lighting

Key Works

Change Detection Techniques

• Frame differencing.
• Background subtraction.
• Motion segmentation.

Tracking Techniques

• ByteTrack
• Simple Online And Realtime Tracking (SORT)
• DeepSORT
• TransMOT
• FairMOT

From Post

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AI Vision - Energy Domain

 

• Vison for Windturbine inspection

• Types of Defects / Vision Implementation
• Energy Assets Monitoring

• Different damages - Erosion / Cracking
• Remote inspection with drones

• Thousands of photographs
• High Res pictures
• 4 hours for inspection for single turbine

• Identify different types of defects
• Plot the location

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AI Vision - Shell

 

Eyes - See, Brains to Think

• Remote inspections
• Warehouse / Counting Stock
• Robots / Drones for Refinery Management
• Safety / Monitoring Traffic
• Robots / Drones / Cameras / Handheld devices - Data Collection

• Safety and Compliance
• Reduce Exposure to field

• Spilling
• Analog Meter Reading
• Track Active Lights

• Follow map
• Collect data
• Analyze and send

• Frames - Detect Clock Region - Classify Clock / Non-Clock / Locate region / Compute angle of the needle
• Value - open/closed
• Needle  - Angle
• DL - Vision Analytics

• Adoption Recommendation
• Phased Adoption of Video Analytics
• Data Source - Model Building - Prototype
• Image Anamoly Detection - Darker image same type of day
• Variations of normal

Ref  - Link

Ref - Link

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Avatar = Magic of Computer Vision

• Pose Estimation
• Pose Detection
• Expression Capture
• GAN to reconstruct

State of Art = Vision in Action

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Production Detection from logos

Ref from post, post1

• Detecting all the possible image regions that might contain a logo
• Matching the detected regions against an ever-evolving set of logo prototypes
• Representation of an image with its text description
• Zero-shot logo recognition relies heavily on an embedding model for matching query regions against a constantly evolving set of cropped logo images
• Text pipeline to extract the text in the image via optical character recognition (OCR)
• We explicitly pair positive examples with their hard negatives, to encourage the model to distinguish logos from similar texts
• Create embedding for similar products and assign appropriate weights

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Vision for Product Catalog Image Correction

Do you know for each category near / far view has an impact on click-to-view conversion

Challenge - How do we know similar products?

Solution - Build vectors of text for each category/features/images. measuring distances between vector representations of products in an embedding space. Features for image vector - product image and the product title

Challenge - How do you change/customize catalog?

Solution - The title provides context that helps the model focus on the relevant regions of the image. Based on it remove the background / zoom  it

Challenge - How does the solution look like? How many models?

Solution -  Global network takes the whole image as input and based on the product title, it determines which portion of the image to focus on. That information is used to crop the input image, and the cropped image passes to the local branch.

From Ref - Using computer vision to weed out product catalogue errors

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