Computer Vision Engineering

Foundations of Digital Image Processing

Image Representation and Signal Processing

• Master the mathematical representation of digital images as matrices and tensors, including color space transformations (RGB, HSV, YCrCb, LAB) and their implications for illumination invariance.
• Understand spatial domain operations, including point processes, histogram equalization, and contrast stretching to normalize image data before algorithmic processing.
• Implement linear and non-linear filtering techniques such as Gaussian blurring, median filtering for noise reduction, and Laplacian operators for edge enhancement.

Geometric Transformations and Calibration

• Apply affine and projective transformations to manipulate image perspective, including rotation, scaling, shearing, and homography estimation.
• Perform camera calibration to account for intrinsic parameters (focal length, optical center) and extrinsic parameters (rotation/translation relative to the world).
• Correct for radial and tangential lens distortion using mathematical models to ensure geometric accuracy in computer vision pipelines.

Feature Extraction and Object Description

Classical Feature Detectors and Descriptors

• Detect corners and points of interest using algorithms like Harris Corner Detection and Shi-Tomasi, which provide the basis for motion tracking and image alignment.
• Generate robust local descriptors such as SIFT (Scale-Invariant Feature Transform) and ORB (Oriented FAST and Rotated BRIEF) to match keypoints between different viewpoints or lighting conditions.
• Apply global shape descriptors and contours, including Canny edge detection, Hough transforms for geometric shape detection, and chain codes for boundary representation.

Deep Learning for Vision

Convolutional Neural Network (CNN) Architectures

• Design and train custom CNN architectures, focusing on layer mechanics like convolution, pooling, batch normalization, and dropout to prevent overfitting.
• Master transfer learning techniques, including fine-tuning pre-trained models (ResNet, EfficientNet, Vision Transformers) on domain-specific datasets to achieve high accuracy with limited data.
• Implement advanced loss functions for classification, such as Cross-Entropy, Focal Loss for handling class imbalance, and Triplet Loss for metric learning.

Object Detection and Segmentation

• Build end-to-end detection systems using architectures like YOLO (You Only Look Once) and Faster R-CNN, focusing on anchor box management and non-maximum suppression techniques.
• Execute semantic segmentation to classify individual pixels using Fully Convolutional Networks (FCN) and U-Net architectures for medical or autonomous navigation tasks.
• Apply instance segmentation via Mask R-CNN, which allows for the simultaneous detection and pixel-level masking of distinct objects within a single frame.

Video Analysis and Motion Understanding

Temporal Data Processing

• Implement optical flow estimation algorithms, such as Lucas-Kanade and Farneback, to determine the velocity of objects between video frames.
• Utilize background subtraction and temporal differencing to detect moving objects in stationary camera setups.
• Integrate Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units to analyze temporal sequences and recognize actions or activities in video streams.

Tracking and Multi-Object Fusion

• Deploy Kalman Filters to predict the trajectory of moving objects and maintain identity through temporary occlusions.
• Use DeepSORT algorithms to integrate appearance-based re-identification with motion models for tracking multiple distinct objects in dense environments.

Advanced Deployment and Optimization

Model Efficiency and Real-Time Systems

• Compress deep learning models using weight quantization, pruning, and knowledge distillation to ensure functionality on edge devices and embedded hardware.
• Optimize inference pipelines for hardware acceleration using tools like TensorRT or OpenVINO to minimize latency in mission-critical applications.
• Design robust data pipelines for real-time video processing, including frame buffering, multi-threading, and hardware-accelerated image decoding.