AI, Computer Vision & Deep Learning Consulting
Big Vision LLC is a consulting firm with deep expertise in advanced Computer Vision and Machine Learning (CVML) research and development. We work on a wide variety of problems including image recognition, object detection and tracking, automatic document analysis, face detection and recognition, computational photography, augmented reality, 3D reconstruction, and medical image processing to name a few.
We provide turnkey solutions for clients who do not have the resources to build a complete CVML solution in-house. A typical CVML project often requires an extensive data collection effort, thoughtful dataset design for training and testing, research and development for implementing the state of the art solution and finally deploying a system that will easily integrate with the client’s existing systems. To deliver this end-to-end solution we employ a data collection team and a group of R&D experts and systems engineers who have experience in moving ideas from research to practice.
We have built scalable systems that are currently in production and are serving millions of requests a month in critical environments ( e.g. a large U.S. bank). Our solutions have a huge real-world impact in terms of speed and accuracy of algorithms, reduction in manual work, and identification and creation of intellectual property.
Computer Vision and Deep Learning Projects
Image Recognition using Deep Learning
Our client, is the Netflix for legos. Users of their website receive a lego set for a fixed priced monthly subscription. After they have played with the lego set, they return it to receive a new set. The users are not charged for missing lego pieces but before sending out the same lego set to a different user, it needs to be ensured that the set is complete with all pieces. To accomplish this, our client had previously built an image recognition system to identify and count lego pieces with humans in the loop to ensure a very high degree of accuracy.
Our deep learning based image recognition system, that replaced their existing system, decreased human effort by over 70%. We were able to further reduce their cost by taking over the remaining 30% of manual work.
Our training system can be used by people with no knowledge of computer vision. The new image recognition system continuously improves as humans in the loop re-train the system with hard examples on which the system fails. We integrated with their existing system via a robust web API.
- Drones : We have built a deep learning based face recognition system that is deployed on a drone. We overcame several challenges to surpass our promised face recognition rate and speed. We ported our face recognition system onto an embedded device ( Jetson TX2 ) that was not yet fully supported by deep learning frameworks like Tensorflow. We also dealt with low resolution video data with severe compression artefacts, motion blur and lens distortion.
- Cell Phones: We worked with one of our clients for developing state of the art face recognition algorithms for mobile authentication. Our Deep Learning based approach significantly improved their recognition system that was based on traditional computer vision techniques. On extremely challenging examples under arbitrary lighting conditions, their recognition rate improved to over 95% with a false positive rate of 1 in 10,000. In addition, we also implemented spoof detection using Deep Learning which accurately detects spoof attacks based on a person’s picture.
Real Time Video Monitoring
ID Document Analysis
One of our clients is in the business of authenticating identity documents (driver’s license, passports, etc.) We have developed several imaging and image analysis algorithms that fall in the following broad categories —
- Geometric Correction: We have developed algorithms for automatically detecting and rectifying ID Type 1 (e.g. driver’s license, credit cards etc.), ID Type 2 (e.g. South African green book) and ID Type 3 (e.g. passports). Imaging these documents present significant challenges because of arbitrary camera orientation, glare, and bad lighting conditions.
- Fraud and Tamper Detection in digital IDs: We have developed Deep Learning based algorithms that automatically detect several different kinds of tampers in digital ID documents.
- Information Extraction using OCR: We are currently working on extracting text information from mobile phone photos of various id documents. The difficulty in this problem arises from images acquired by untrained users under uncontrolled lighting conditions that often include glare and blur. The images of a passport’s data page taken using a cell phone have significant curvature and this required us to solve challenging image alignment problems.
- Liveness Detection: Our video based liveness detection foils a large number of attack vectors with a very high degree of accuracy to ensure that the person being photographed for recognition is a live human.
Object detection in Aerial Images
Video based Sports Analytics
Detecting parasites in Horse Feces
We implemented a Deep Learning based solution for locating and identifying different kinds of parasites in images of horse feces for MEP Equine Solutions. The image on the right shows parasites Ascarides (A) and Strongyles (S) being detected while negatives marked as N.