NextWealth / Services / Computer Vision

Computer Vision Projects

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Computer Vision Data Transactions

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Computer Vision

Garner valuable insights from images, videos, sensors and geo to discover endless possibilities using Artificial Intelligence and Machine Learning

Computers have changed the world drastically and societies are more technologically advanced than ever. However, even the most advanced machines struggle to interpret what it sees. A computer cannot differentiate between a discarded takeaway bag and a piece of stone on a road or distinguish a frown from a smile on the same face. Computer Vision (CV) is the part of artificial intelligence (AI) that enables computers or machines to analyse and understand images. Computer Vision provides inputs to machines to make sense of the different images it is fed with. By reconstructing, recognising, and re-organising inputs, it guides machines to interpret objects, actions, events, and relations in the image.

Our Computer Vision Capabilities

Human beings use their vision and language capabilities to understand and communicate with the world around them. A scene they perceive can easily be described, and conversely, when something is described in a language, humans can decipher and instantly create an image out of it. As mundane as it is for humans, replicating these capabilities in machines is challenging. Computer Vision (CV) can thus be used for object detection and recognition, surveillance systems, activity recognition, image rendering extensively.

NextWealth can help companies by providing gold standard ground data; Audio, Video, Text, Geo and Sensor, to help build and train machine learning models.

Computer Vision (CV) analyses the following type of data for training machine learning models:

Images
Videos
Sensor
Geo

Meticulously annotate different types and size of images with precise capturing tools to make it recognizable for machines

Bounding Box

Effortlessly outline objects in a box for high-quality training data for diverse businesses.

Polygon Annotation

Mark contours in asymmetrical and coarse objects from images and videos, to make it machine friendly.

Lane Annotation

Detect streets accurately to perceive surroundings, ensuring trouble-free driving.

Keypoint Annotation

Use key points to identify and determine unique shapes and details of objects.

Semantic Segmentation

Tag each pixel of an image distinctly for fine-grained interpretation of images.

Annotate 2D and 3D patterns in running images and account for all moving objects in a video frame to train a wide range of machine learning models.

Video Classification

Identify actions,people and objects from different frames in videos.

Video Object Tracking

Detect one or multiple points of interest in each frame of a video.

Action Identification

Identify social interactions and differentiate between actions and consequences.

Bring together inputs from multiple sensors and disparate sources such as Radars, LiDARs, and Cameras to develop a single model of the surroundings of a vehicle.

Object Identification in 3D Point Cloud

Label the objects at every single point with highest accuracy and advanced annotations.

3D Semantic Segmentation

Divide and tag objects of interest in 3D images and label each pixel with a corresponding class.

Power autonomous vehicles, drones, and other computer vision applications to enable accurate interpretation of images and video using highly qualified and annotated data

POI Tagging

Detect clear and stable points of interest for subsequent processing of various data points.

Damage Area Annotation

Make asymmetrical and coarse objects in images and videos, machine friendly.

Footprint Annotation

Detect clear and stable points of interest for subsequent processing of various data points.

Computer Vision Applications

A picture is equivalent to 1000 words. Several images and videos exist on the internet, especially on social networking sites and search engines. Images can also be derived from textual data and supplied to vision systems These can be used as training data for machine learning, which can in turn be used for AI-ML data annotation or to train image classification and activity recognition systems. From automobile industry, finance to law enforcement and healthcare, every industry has applications that can be automated using Computer Vision.

Autonomous Vehicles

Enable the vehicles of the future to comprehend surroundings and help them perceive different objects and signs on the road to avoid crashes and mishaps.

Retail

Identify different types of objects sold in retail stores and help visual search machines recognize clothing or other accessories brought by customers, enable complete checkoutless shopping experience or offer an effective solution for quality and process control.

Real-time sports tracking

Accurately track players from game footages or poses to identify individual playing styles, drive meaningful insights from the game and enhance spectator experience of a sport.

Driver Monitoring

Prevent vehicles and other passengers from being involved in a mishap or accident, by accurately detecting driver drowsiness, distraction, and over- speeding.

Augmented Reality and Mixed Reality

Cover and embed virtual objects on real world imagery to enable computing devices and IOTs to establish concepts of depth and dimensions.

Healthcare

Significantly improve medical diagnosis to analyze various scans and imaging to detect anomalies such as tumors or search for signs of critical illnesses.