Image Processing and Computer vision

Trainig and Internship


What you'll learn
  • All the concepts and coding of Natural Language Processing
  • Image Processing
  • Computer Vision
  • Open CV
  • Certifications – Certificate will be provide after complication of the course.
  • Available –Online / Offline Learning (No recorded lecture / live session of each class).
  • 60 hrs Course..
  • Rich course content for master learning
  • Pay after six demo class.
  • 80% Hands on Course 20% Theory.
  • One MCQ and one case study is compulsory for completion of course..
  • Certificate will generated after successfully completion of the course.
  • Digital signal processing.
  • Python.
  • Machine learning
  • Deep Learning


Master computer vision and image processing essentials. Learn to extract important features from image data, and apply deep learning techniques to classification tasks.


Linear image processing, Model fitting, Frequency domain analysis, Camera Models and Views, Camera models, Stereo geometry, Camera calibration, Multiple views, Image Features, Feature detection, Feature descriptors, Model fitting, Lighting, Photometry, Lightness, Shape from shading


Overview of image motion, Optical flow, Introduction to tracking, Parametric models, Non-parametric models, Tracking considerations, Classification and, recognitionClassification: Generative models, Discriminative model, Action recognitionUseful Methods: Color spaces and segmentation, Binary morphology, 3D perception


CNNs for Recognition, Verification, Detection, Segmentation:CNNs for Recognition and Verification (Siamese Networks, Triplet Loss, Contrastive Loss, Ranking Loss); CNNs for Detection:Background of Object Detection, R-CNN, Fast R-CNN, Faster R-NN, YOLO, SSD, RetinaNet; CNNs for Segmentation: FCN, SegNet, U-Net, Mask-RCNN, CNN + RNN Models for Video Understanding: Spatio-temporal Models, Action/Activity Recognition


Introduction to Attention Models in Vision; Vision and Language: Image Captioning, Visual QA, Visual Dialog; Spatial Transformers; Transformer Networks


Review of (Popular) Deep Generative Models: GANs, VAEs; Other Generative Models: PixelRNNs, NADE, Normalizing Flows, etc, Variants and Applications of Generative Models in Vision:


PHI India makes a variety of student smart cards that can be presented to an authenticating reader and linked via IoT to an access control system. Types of cards include ones for physical access to facilities, photo I.D. (with hologram or UV printing), time and attendance, logical access (to monitor use of electronic data like coursework, e-learning resources, printers and Internet), loyalty and membership, payment (for vending machines, printing, photocopying) and health/medical data (blood type, emergency contacts).

  • How a robot can move and sense the world around it, creating a visual representation of the world as it navigates.
  • Features and Object Recognition
  • Learn why distinguishing features are important in pattern and object recognition tasks.
  • Write code to extract information about an object’s color and shape
  • Use features to identify areas on a face and to recognize the shape of a car or pedestrian on a road.


Image Segmentation

  • Implement k-means clustering to break an image up into parts.
  • Find the contours and edges of multiple objects in an image
  • Learn about background subtraction for video.