Autonomous Vehicles

Autonomous Vehicles are self-driving vehicles, in other words. The Autonomous vehicles use different sensors for sensing different physical parameters. Based upon these parameters, decisions are to be made in situations like choosing a route dynamically, controlling the vehicle speed etc. SFO Technologies’ proficiency lies in its Artificial Intelligence (AI) and Image processing dexterity.

The competency of the Transportation team in AI lies in its dedicated AI expert team. Deep learning, Feature Space Engineering & Neural network design for image detection and recognition in MATLAB, and Natural Language processing & Image recognition with Tensorflow are some of the major areas the team aims at. To be specific, following are the AI competencies that the Transportation team makes itself worthy enough in the contemporary AI market.

  • Deep neural network design with Nvidia Digits platform.
  • Caffe based design of convolutional neural networks.
  • GPU accelerated processing platform implementation.
  • Matlab based accelerated algorithm design and testing.
  • Machine learning based natural language processing for sentiment understanding and extractions.
  • Image detection and recognition with convolutional neural networks with application in Advance driver assistance systems.
  • Machine learning for RF signal processing and indoor location mapping.
  • Neural network based image feature extraction.

SFO leverages deep focus on Image Processing through its competencies in image enhancement, pattern recognition and feature extraction using the image processing tools such as Matlab, Labview and OpenCV. Categorizing into Pattern Recognition, Image Enhancement and Feature Extraction, following are the Image Processing competencies of our Transportation team.

Image processing Competencies:
Pattern Recognition:
  • Template Matching
  • Neural Network Based Image Segmentation
  • Edge Based Methods
  • Level Set Based Segmentation
  • Region Growing
Image Enhancement:
  • Intensity based Transformations
  • Statistics based Transformations
  • Geometric Transformation-Inverse
Feature extraction:
  • Wavelet Transforms
  • Hough Transform
  • Intensity Based Features
  • Key Point Based (SIFT, SURF, Haris).

Case Studies: