Certification in Computer Vision

Explore how image representation uses two-dimensional arrays of pixels, with grayscale and color spaces defining intensity from 0 to 255, enabling feature extraction through filtering, thresholding, and edge detection.

Explore image representation and feature extraction using OpenCV and Matplotlib. Learn to load and display grayscale and color images, convert BGR to RGB, and apply canny edge detection.

Learn how the Harris corner detector identifies corners where gradient changes sharply via the structure tensor and corner response, with thresholding and non-maximum suppression. It also introduces sift features.

Explore histogram of oriented gradients for object detection by analyzing gradient orientations in local image regions, and learn how hog-based feature extraction supports computer vision with OpenCV and scikit-image.

Learn how image segmentation divides an image into meaningful parts using thresholding, region growing, clustering, and deep learning to locate objects, boundaries, and regions of interest for computer vision tasks.

Explore various image segmentation types, from thresholding and clustering to edge, region, contour, and semantic segmentation, including dense and instance segmentation with deep learning.

Learn how thresholding converts grayscale images to binary by applying global thresholds and Otsu's method with cv2 and matplotlib.

Demonstrates k-means clustering to segment images into three color-based clusters, reconstructing the image from centroids, and compares with canny edge detection for age-based segmentation.

Apply the watershed algorithm to segment overlapping objects via grayscale conversion, thresholding, noise removal, distance transform, and marks, with deep learning segmentation using Deeplab v3 or U-Net in TensorFlow.

Learn how image segmentation partitions an image into meaningful segments using thresholding to deep learning, enabling object detection, medical imaging, semantic segmentation, and autonomous driving.

Master object detection in computer vision by identifying and locating objects in images with bounding boxes, going beyond image classification for autonomous driving, surveillance, and image retrieval.

Explore key concepts in object detection, including bounding boxes, iou, and confidence scores, and compare traditional methods like Haar cascade and hog+svm with detectors like yolo and faster r-cnn.

Learn to implement object detection with pre-trained models using OpenCV and TensorFlow, including Haarcascade face detection, grayscale preprocessing, multiscale detection, and drawing bounding boxes.

Explore YOLO, a real-time object detection system, using OpenCV's dnn to load YOLO v3 weights and cfg, perform forward pass, apply confidence and non-max suppression, and draw labeled boxes.

Explore faster R-CNN with TensorFlow using the object detection API to load a pre-trained faster R-CNN model, prepare the Coco label map, run detection on an image, and visualize results.

Master object detection by contrasting traditional methods like haarcascade and hog + svm with deep learning models such as YOLO, SSD, and Faster R-CNN, using OpenCV and TensorFlow.

Describe image classification as assigning a single label to an image, using labeled data and CNNs to enable facial recognition, object detection, and medical imaging.

Explore the key components of image classification: feature extraction, model training, evaluation, and inference. Contrast handcrafted features such as sift and hog with cnn models like LeNet, AlexNet, and ResNet.

Implement image classification using traditional methods with sift features and an svm classifier, and explore deep learning approaches, including building a visual vocabulary with k-means and evaluating accuracy.

Apply deep learning methods for image classification using a pre-trained VGG16 model with TensorFlow and Keras, including data preprocessing and a custom layer on the base model output.

Explore image recognition and scene understanding, including object detection, labeling, and contextual interpretation for applications in autonomous driving, surveillance, robotics, and augmented reality.

Explore key components of image recognition, including object detection, image classification, semantic and instance segmentation, and related CNN and R-CNN techniques for scene understanding.

Implement image recognition with a CNN using a pre-trained ResNet50 on ImageNet by loading, preprocessing, predicting, and decoding top categories with their scores in Python.

Explore instance segmentation with Mask R-CNN using a pre-trained COCO model in OpenCV. Build and run forward pass to extract detection boxes and segmentation masks, then visualize results with matplotlib.

Explore scene classification with a cnn-rnn hybrid, using Inceptionv3 features fed through time distributed layers and an LSTM to classify video frames into 101 scene classes.

Master image recognition and scene understanding with CNN and RNN, including Fast R-CNN, Faster R-CNN, region-based CNN variants, and temporal context with LSTM and GRU, using TensorFlow and OpenCV.

Learn how object tracking continuously maintains an object's identity across frames, unlike detection, enabling surveillance, autonomous driving, and human–computer interaction, and apply Kalman filters, optical flow, and Siamese networks.

Explore key components of video object tracking, from initialization and first-frame detection to data association, tracking, and updating object state using Kalman filter, Hungarian algorithm, and deep learning trackers.

Explore the KLT tracker with OpenCV, initializing on the first frame, selecting a region of interest, and updating tracking across video frames, with a note on SORT for robust tracking.

Combine yolov4 detection with the deep sort tracker to achieve robust multi-object tracking in video. Learn practical implementation in python using cv2 and numpy, including non-max suppression and real-time visualization.

Explore image generation and image-to-image translation in computer vision, powered by GANs and VAEs, applying these deep learning techniques to art, design, medical imaging, and data augmentation.

Explore image generation and image-to-image translation with generative models like GANs and VAEs, including CycleGAN, DCGAN, and conditional GANs, plus style transfer and domain adaptation.

Explore generative adversarial networks in computer vision by building a deep convolutional GAN that generates handwritten digits using a generator and a discriminator.

Explore image to image translation with pix2pix, detailing paired data, generator and discriminator architectures, skip connections, and the training loop with adversarial and l1 losses.

Explore cycle gan for unpaired image to image translation, detailing generator and discriminator architectures and cycle consistency guiding training with TensorFlow Keras and Adam optimizer.

Explore image generation and image-to-image translation with GANs, including pix2pix and cycle GAN, using unpaired data and cycle consistency. Apply these techniques to artistic creation and data augmentation.

Explore advanced topics in computer vision, from 3D vision and depth estimation to transformer-based attention and video understanding, supported by deep learning and reinforcement learning.

Explore self-supervised learning that learns representations from unlabeled data via pretext tasks like image inpainting and jigsaw puzzles, and apply domain adaptation, transfer learning, and few-shot and zero-shot techniques.

Advance graph neural networks for vision to model object interactions with graph representations in images and videos, and explore explainable AI, multimodal learning, and attention for trustworthy applications.

Explore how computer vision powers autonomous vehicles with lane and object detection, and support healthcare imaging, security analytics, retail personalization, and AR/VR experiences.

Explore future trends in computer vision, including on-device deep learning, edge computing for real-time privacy, and multimodal fusion across vision, text, and audio.

Explore privacy preserving techniques such as federated learning and differential privacy, and examine continual learning and adaptation to evolving environments in future computer vision applications.