This course provides hands-on training in computer vision techniques using popular tools and frameworks, including Roboflow for dataset management, Google Colab for collaborative coding, YOLO for object detection, Ulytics for data labeling and analysis, and OpenCV for image processing tasks

javascript training

Course Features



Job Placement Assistance

Job Placement Assistance

Live Training

Live Training

 100% Job Oriented Training

100% Job Oriented Training

 Customized Syllabus

Customized Syllabus

Best Fees Structure and Packages

Best Fees Structure and Packages

Course Syllabus

Introduction to Computer Vision and Python

Overview of computer vision principles

Introduction to Python programming for computer vision

Setting up Python development environment

Applications of computer vision in various industries

Basics of digital image processing and representation

Image Processing Techniques

Image enhancement: Histogram equalization, contrast stretching

Filtering operations: Gaussian filter, median filter, convolution

Edge detection: Sobel, Prewitt, Canny edge detectors

Feature detection and extraction (e.g., Harris corner detection, SIFT, SURF)

Feature matching and descriptor techniques

Feature-based image alignment and stitching

Object Detection and Recognition

Introduction to object detection and recognition

Traditional methods for object detection (e.g., Viola-Jones algorithm)

Modern approaches: Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)

Image Processing with OpenCV

Basics of image representation and manipulation

Image enhancement techniques

Filtering and convolution operations

Feature Extraction and Descriptors

Feature detection algorithms (e.g., Harris corner detection)

Feature descriptors (e.g., SIFT, SURF)

Feature matching techniques

Object Detection with OpenCV

Introduction to object detection

Haar cascades for object detection

Template matching and sliding window techniques

Deep Learning for Computer Vision

Introduction to deep learning concepts

Convolutional Neural Networks (CNNs) for image classification

Transfer learning with pre-trained CNNs

Convolutional Neural Networks with TensorFlow

Introduction to TensorFlow and Keras

Building and training CNN models with TensorFlow

Evaluation and testing of CNN models

Image Segmentation

Introduction to image segmentation techniques

Thresholding and contour detection

Watershed algorithm for image segmentation

Advanced Topics in Computer Vision

Optical Character Recognition (OCR) with Tesseract

Face detection and recognition

Hand gesture recognition using computer vision techniques

Create your free Account