24Nov

Semantic Segmentation in Facial Recognition

Impact OutsourcingData Annotation

Facial recognition technology is becoming a feature in our everyday lives. More and more companies are using facial recognition technology to detect and identify faces for various use cases. These include monitoring a driver’s facial expression for safe driving and unlocking smartphones, just to name a few. 

Using specific image annotation techniques e.g. semantic segmentation and landmark annotation, logical computer vision models for facial recognition are probable. These unique data labels to aid in identifying the shape and variation of objects. 

Keypoint Annotation for Facial Features Detection

Also referred to as landmark annotation, keypoint annotation is suitable for building AI-based facial recognition applications. By making high-quality keypoint annotations across different classes for pinpoint detection of facial features/attributes. 

Landmark annotation involves labeling a facial image using key points placed at specific locations on the face. This aids the model to identify the facial expression or gesture to effectively train a logical AI bases facial recognition application. Landmarking aids in determining the authentic density of an object in specific areas. 

Semantic Segmentation for Facial Recognition

Semantic segmentation is employed to produce datasets crucial to building self-driving cars and ADAS semi-autonomous cars. Also known as image segmentation, its use cases are ever-increasing given the evolving AI technology. 

At Impact Outsourcing, we offer the best data annotation services at a fraction of the total cost. By trusting us, your datasets will be of the highest quality, perfect for training logical AI/ML models. Be it in healthcare, automotive, robotics, or agriculture, Impact Outsourcing has the solutions to build your world-class AI/ML application.

24Nov
VIDEO ANNOTATION OUTSOURCING impactoutsourcing.co.ke

Data Annotation and its Benefits Defined

Impact OutsourcingData Annotation

Data annotation refers to tagging/labeling data from different formats e.g. text, video, images, etc. To build a practical AI/ML application, accurately labeled data is needed so that the application can learn and understand the patterns it’s designed for.

The value of having precisely annotated data to train a computer vision-based ML model cannot be underrated. Using a wide array of data annotation methods and tools, accurate data sets for practical computer vision training are created. By using tags or added metadata, data is made more informative to AI/ML models. 

Types of Data Annotation

Depending on an AI/ML model’s algorithm (which varies depending on the sector and use case) data annotation techniques employ a variety of tools, approaches, and data labeling expertise.

Most of the training data is mainly available in text, image, and video. These different data types are labeled using different annotation techniques. In this blog, we are going to cover the different types of annotation suitable for training AI/ML models.

Bounding Box Annotation

Sometimes referred to as 2D and 3D bounding boxes, it refers to drawing rectangular lines on an image thus making it visible to a Machine Learning model. This method is perfect for training models whose use cases are in retail, agriculture, and fashion, just to name a few.

Semantic Segmentation

Also referred to as image segmentation, it involves clustering areas of an image together as belonging to the same class. A form of pixel-level prediction since every pixel in an image is grouped differently depending on the category. Semantic segmentation is mainly employed in the automotive industry and agriculture.

Keypoint/Landmark Annotation

For landmark/keypoint annotation, one must label significant points at specific points. Keypoint annotation is mainly used for gesture and facial recognition. To build a logical image recognition AI model, accurately annotated points are crucial.

Polygonal Annotation

Polygonal annotation allows you to capture more lines and angles. Polygonal annotation is basically plotting/drawing more lines to capture more angles. This annotation technique is mainly used in drone and satellite imaging technology.

LIDAR Annotation

Lidar Annotation works by assigning anatomical or structural points of interest which leads to error-free data sets that ascertain the form of different-sized objects. This enables Artificial Intelligence and Machine Learning algorithms better recognize their surroundings when deployed.

There exists a wide range of practical use cases for data annotation for computer vision. Below is a mention of a few sectors where data annotation for computer vision is strongly put to use.

  1. Autonomous Automobiles
  2. Autonomous Flying
  3. Sports and Gaming
  4. Retail
  5. Agriculture 
  6. Livestock Management
  7. Forest Management
  8. Media
  9. Security and Surveillance
  10. Robotics

Why Impact Outsourcing?

Impact Outsourcing offers annotation services be it Lidar, Semantic Segmentation, Keypoint, etc. With our professionally managed workforce headed by experienced project managers, we are well-positioned to deliver quality datasets for your AI/ML project.

18Nov

The Importance of Image Annotation Services For Artificial Intelligence and Machine Learning

Impact OutsourcingImage Annotation Services

Recognizing, extracting, differentiating, and comprehending data from digital mediums like pictures or videos can be characterized as an image annotation. This is one of the main building blocks in developing computer vision for image recognition in AI/ML applications. 

The use cases for computer vision vary from autonomous vehicles and medical imaging to security. As a consequence, image annotation is ever critical in building logical AI/ML models across multiple fields and sectors. 

Use Cases of Image Annotation 

By utilizing photo and video data, AI/ML models can be taught to interpret and interact with the world just like a woman would. Data annotation for Artificial Intelligence and Machine Learning is ever more crucial at this development stage. An AI/ML model’s accuracy is dependent on the quality of annotations fed into the application. Poorly annotated data will almost guarantee a flawed application and vice versa. 

Below are some use cases where image annotation is employed; 

Autonomous Vehicles 

An effective AI/ML application should correctly identify road signs, traffic lights, pavements, bike lanes, etc. Below are some of the data annotation techniques employed when building autonomous vehicle applications; 

  1. LIDAR Sensing Technology
  2. Object and Dimension Detection 
  3. Navigation with Steering Response
  4. Advanced Driver Assistance Systems (ADAS) 

Agriculture 

Agriculture has not been left out in this great AI revolution. Players in this sector can use AI/ML technologies to aid crop monitoring, reducing human participation. Agricultural AI can aid the sector in the following ways; 

  1. Livestock management
  2. Crop and plant health monitoring
  3. Detection of pests and diseases

Security and Surveillance 

The ever-increasing number of security cameras has been a critical factor in the evolution of AI/ML as a security feature. Image annotation is a necessary component for building futuristic models for crowd detection, reading face IDs for theft detection, and pedestrian tracking to name a few. 

Conclusion 

Over the years, our team has bridged boundaries and crossed time zones operating from Kenya for clients across the globe. We strive to tackle our clients’ tasks as effectively as they’d do them. Contact us today and let us power your AI/ML application by providing carefully annotated high-quality datasets.

18Nov
Frequently Employed Text Annotation Techniques For Natural Language Processing

Frequently Employed Text Annotation Techniques For Natural Language Processing

Impact OutsourcingText Annotation Techniques

Artificial Intelligence and Machine Learning are now part of everyday life. The consequences of these new technologies have affected how we see and interact with the world. AI and ML applications have limitless potential to radically change and drive the global economy forward. These algorithms are opening new frontiers in medicine, the arts, and finance. Natural Language Processing is at the front of all these.

Recent breakthroughs in NLP mean that people with speech impairments can conveniently communicate using automatic voice recognition software. However, to realize this technology data must be carefully annotated to train the AI models. Otherwise, all the hype around AI and ML would be wishful thinking.

To adequately train an NLP model, massive amounts of annotated texts are necessary. Below is a breakdown of the different types of text annotation tools for computer vision NLP.

Entity Text Annotation

Crucial to training chatbots, entity annotation is the foundation block for training logical NLP solutions. Recognizing, fragmenting, and annotating values is referred to as text mining.  

Entity Linking

This refers to connecting similar entities to larger data repositories. This process is crucial to creating a logical NLP computer vision model.

Sentiment Annotation

Sarcasm is one of our natural reactions as humans. When giving reviews, we sometimes opt to be sarcastic whenever confronted with a bad experience at a spa or a hotel. A poorly trained software might understand sarcasm as genuine praise when it’s the complete opposite.

To avoid this, sentiment annotation/analysis is crucial. Judging from a person’s emotion or tone, people are able to label each sentiment as either positive, negative, or neutral. 

Linguistic Annotation

This is also referred to as corpus annotation. This refers to tagging language data in both text and audio recordings. Labelers are tasked with identifying and highlighting phonetic, grammatical, and semantic features in both audio and text. 

Intent Annotation

Intent annotation is mainly employed to decipher a user’s intention. Different users have different intentions when interacting with chatbots. Some users wish to learn about their overhead charges, others want statements, etc. This annotation technique uses different labels to categorize a user’s intent.

Conclusion

With this blog, we hope you have a better understanding of text annotation and how it is employed for computer vision. Text annotation can prove to be overwhelming when faced with ever-increasing data volumes. 

To cure this, it’s always wise to outsource this non-core yet important part of AI/ML model development. 

At Impact Outsourcing, we provide a professionally managed workforce backed by years of experience in data annotation. Contact us today and let us power your next NLP computer vision model.