Image Analytics has become a Game Changer for SMEs
As the popular saying goes “A picture is worth thousand words” that is what nitezens are doing with social media channels. A staggering number of images are captures as photographs by social medias, X-rays, scans and other testing images by healthcare industry and thousands of hours of video by surveillance cameras. These images and videos are an immense source of information and industries cannot ignore the insights that this visual data can reveal. Image analytics is gaining grounds as companies are waking up to the value in this technology. By 2021, the global image recognition market is expected to touch USD 38.92 Billion and video analytics market is expected to grow to USD 8.55 Billion by 2023.
So what is image analytics?
It is the extraction of meaningful insights from unstructured data forms like images and videos by converting them into machine readable data using image processing libraries. Advances in deep learning and machine learning have been the driving force behind the growth of computer vision and image recognition concepts.
When major business houses are turning to data-driven analytics to manage increasing global competition and market volatility, small and medium-sized enterprises cannot ignore the power of image analytics. Image analytics enabled by deep learning and machine leaning capabilities of AI has made inroads into almost all industries changing the consumer experience, inventory management and have driven down costs increasing efficiency of manufacturing and marketing
Our AI powered image solution incorporates image classification, object detection, image segmentation and edge detection through deep learning capability of AI. Our image analytics follows a unique workflow – initial training phase, followed by testing phase, which results in tagging and classification of image objects so that meta data is available for analysis.Click here to schedule a Demo
Some of the industries that have successfully integrated image analysis with data analytics include:
Images are an integral part of healthcare system and doctors use images to gain better understanding of the medical condition. Analysing diagnostic images need proficiency of skilled healthcare professional are often time consuming and repeated for accuracy there by making it a costly affair. AI based deep learning, machine learning along with algorithms that mimics decision making ability of human brain promises to revolutionise the way medical images are interrupted and understood, leading to better diagnostics and treatment with narrower margin of error. It has been successfully used in:
- In detecting early onset of disease conditions using X-rays.
- In drug discovery during clinical trials image analytics has made detection of presence or absence of conditions more accurate.
- In cancer discovery and treatment.
- In AI-enhanced microscopes to scan for harmful bacteria in blood samples
- By Surgeons during delicate surgery to visualize surgical procedures
We have has been successfully implemented Image recognition in disease pathology through image analytics powered by MI ability of AI and DI algorithms on select data sets. Our Image analytic suite for healthcare incorporates specially crafted algorithms to detect both onset and stage of development of medical conditions by identifying specific patterns. A deep learning model (Convolution Neural Network) which uses multi-layer neural network is devised to detect retinopathy patterns from fundus images and Lobar and Bronchial Pneumonia in X-rays. This has reduced diagnostic time considerably ensuring shorter treatment journey.Click here for the case study on Retinopathy
Image analytics is fast finding acceptance in retail industry. AI enabled real-time visual recognition of goods has been a game changer for adaption of technology to harness valuable insights from images/video captured. Merchandising and sales are main concerns of any FMCG industry, which needs implementation at individual retail outlets. Leveraging image analytics on shelf /store images FMCG manufacturers have improved shelf monitoring, share of shelf calculations, stock availability, pricing and store audits to reap rich dividends by monitoring and tracking store operations in real time. Image analysis has enabled business houses to gain better understanding of customers’ preferences, characteristic and their shopping journey from entry to billing. Customer face recognition has revolutionised the way retailers are enhancing customer experience identifying frequent and big spenders to device customer specific selling strategies.
Optimisation of field sales representatives’ time due to shorter store check times and enhanced store coverage times has improved sales and margins.
The main purpose of surveillance is to monitor changing behaviour in order to protect people and property. Surveillance is becoming more digital if the vast number of CCTV installations is any indication. It finds use in crime detection and prevention, disaster response and management, fire protection and anti-terrorist measures. Conventional surveillance systems require human intervention or sensor mechanisms. The changing industrial and personal requirements for stringent security needs has paved the way for image/video content analysis using enhanced image quality along with networked cameras. A computer image based surveillance system captures video feeds, processes it, applies predefined logic, displays analysed data and manages alerts.
Access control is one of the main applications of video processing-based surveillance. Video streaming device is used to detect, identify and/or authorise a person at each entry point.
Intrusion detection is another important application of video analytics, where video stream captured from a CCTV camera is used to track an object within a specified area to be identified as intruder.
Crowd management in places like airports, stadiums, malls and other busy areas using image analysis is yet another novel use of image analytics. These tools have been employed to estimate foot falls and restrict movement in prohibited areas.
Fire detection is one of the most important components of a surveillance system and the earlier is fire detection the better. With the rapid advancement in mage analytics conventional sensor based fire detection systems have been replaced with the computer vision based fire detection systems.
Fire detection module is a part of our imaging solution suite which incorporates image-based real- time fire detection method based on computer vision techniques. AI powered object classification and deep learning object localization algorithm uses coordinates of the location of an object in the image to locate it with the help of bounding boxes.Click here to set up a demo with us to showcase our Fire protection image analytics.
Insurance industry has not been far behind in leveraging image analytics to improve customer loyalty. Assessment of damaged vehicle by accessing during damage claim settlement is time consuming, but with analysis of images of the damaged vehicle, settlement times have been drastically reduced. Surveying large tracts of agricultural land to estimate crop quality and crop cover is vital for insurers to accurately estimate risk factor to determine crop insurance premiums. Manual survey is laborious and rife with inaccuracies, but satellite image analytics has vastly improved accuracy and time.
Some more applications of image analytics
- Banking industry uses facial recognition to facilitate mobile banking and images of signatures/handwriting are used to ease cheque deposition.
- Law enforcement uses vehicle recognition technology to identify lost/stolen vehicles.
- Imaging analytics has been successfully employed by social media to identify and remove fake accounts.
- Process industries have implemented image analytics in an innovative way to detect leaks and faults in pipelines and fouling in heat exchangers and cooling towers. Our imaging solution suite incorporates image classification module to Identify in fouling heat exchangers using CNN.
Despite the advancement in image analytics it is yet to overcome certain limitations like
- Difficulty in capturing quality data
- Restriction on input data structure
- Difficulty in handling/processing huge visual files
As visual content becomes more and more prominent along with immense progress in measuring images and video the vast power of image analytics still remain untapped despite advances in machine and deep learning algorithms. In these days of availability of Big Data and infrastructure that supports real-time processing image analytics might find newer and unconventional applications.