Computer Vision Quiz

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Questions and Answers

What is the possible solution mentioned to address sensitivity to small shifts and color-based appearance variations in feature representations?

edges and intensity oriented gradients

What is Intersection over Union used to evaluate in object detection algorithms?

The area of union between pixels

Object orientation

The overlap between two bounding boxes (correct)

Pixel intensity variations

Deterministic tracking methods are only suitable for offline usage.

True

____ filtering algorithms estimate posterior belief and state distribution based on control data and sensor measurements.

Bayes Signup and view all the answers

Match the object tracking algorithm components with their descriptions:

State = Contains all information about the system Measurements = Provide information about the state Models = Include information about object, sensors, and environment Inference = Estimation of the true state based on data Signup and view all the answers

What is the main goal of Reinforcement Learning?

Maximize future rewards Signup and view all the answers

What is the key advantage of Supervised Learning?

Very high accuracy Signup and view all the answers

Unsupervised Learning involves learning from labelled examples.

False Signup and view all the answers

Semantic segmentation involves dividing an image into multiple segments and assigning each pixel a ____________.

label Signup and view all the answers

Match the Machine Learning paradigm with its description:

Supervised Learning = Learning from examples with labelled data Unsupervised Learning = Learning similarities between examples without labelled data Semi-supervised Learning = Learning from limited examples with a mix of labelled and unlabelled data Self-supervised Learning = Making up own examples to learn from based on intrinsic signals Signup and view all the answers

What does DeepSORT use to improve the ability to track objects in the presence of occlusion?

CNN Signup and view all the answers

What is the fundamental matrix used for in epipolar geometry?

Describing epipolar geometry of two views Signup and view all the answers

Stereo cameras can deliver scaled scenes because they have two lenses.

True Signup and view all the answers

The apparent motion of objects between two stereo images is represented by a ________ map.

disparity Signup and view all the answers

Match the following matching techniques with their descriptions:

Feature matching = Matching is done mostly at salient points like corners or blobs Area based matching = Used in dense point cloud generation by matching regions in images Signup and view all the answers

What is the purpose of adjusting the images to make epipolar lines horizontal?

Easier search process Signup and view all the answers

What are some cues that humans use to estimate depth from a single image?

Occlusion, Perspective, Atmospheric cue (objects further away get blurry), Patterns of light and shadow, Height cue Signup and view all the answers

What is an important element of traditional methods for estimating depth from a single image?

Incorporating global context Signup and view all the answers

The first successful method to determine absolute depth involved learning 3 sets of parameters per row of the image, including estimating absolute depth of a patch, uncertainty estimation, and a smoothing term in the _____.

model Signup and view all the answers

Using superpixels to calculate depth involves determining the depth of all pixels belonging to a surface based on their 2D coordinates.

False Signup and view all the answers

Match the following approaches with their method of solving the depth estimation problem:

Supervised regression problem = Stacking coarse network and fine network Classification problem = Discretizing space and producing confidence on depth estimation Ordinal regression problem = Solving pixel-wise binary classification with multiple heads and feature extractor Signup and view all the answers

What are some limitations of estimating depth from a single image using traditional methods?

Low transferability of the model to other locations, Less accurate than stereo, Complicated methods Signup and view all the answers

Study Notes

Machine Learning Paradigms

Supervised Learning:
- Learning from examples with labeled data
- Examples: email classification, image classification, speech recognition, language translation, convolutional networks for scene understanding
- Function approximation: deduce the equation from the data
- Advantages: abundant labeled data available, high accuracy
Unsupervised Learning:
- Learning patterns and structures without labeled data
- Examples: clustering, dimensionality reduction, anomaly detection
- Goal: learn hidden representation of data
- Evaluation metrics: silhouette score, Davies-Bouldin index, reconstruction error
Semi-supervised Learning:
- Learning from limited labeled data and abundant unlabeled data
- Challenges: cost and time requirements, labeling bias, dynamic nature of data
- Techniques: pseudo-labeling, consistency regularization, self-training
- Applications: label-scarce applications, medical image segmentation
Self-supervised Learning:
- Learning from intrinsic signals in the data
- Generating supervisory signals from input data
- Pretext tasks: predicting one part of the data from another
- Techniques: generative, contrastive, adversarial
- Applications: depth estimation, 3D reconstruction
Reinforcement Learning:
- Learning without examples, through interaction with the environment
- Optimization of a reward signal
- Goal: maximize future reward
- DRL algorithms: value-based, policy-based, actor-critic

Semantic Segmentation

Definition: dividing an image into multiple segments or regions and assigning each pixel a label corresponding to the semantic class it belongs to
Applications: autonomous robots, medical imaging, satellite image analysis, surveillance systems
Traditional Methods: low-level features, thresholding, edge detection, region growing, clustering
Convolutional Neural Networks (CNNs):
- Extract hierarchical features
- Encoder: extracts high-level features using convolutional and pooling layers
- Decoder: reconstructs spatial dimensions using upsampling techniques
- Predicts labels in real-time
Evaluation Metrics: pixel accuracy, mean pixel accuracy, precision, recall, F-score, mean intersection over union, frequency weighted intersection over union, dice coefficient
Loss Functions: cross-entropy loss, dice loss, weighted loss functions, combination loss function, binary cross-entropy loss

Object Detection

Definition: detecting objects of certain classes and locating them using bounding boxes
Challenges: illumination, object pose, clutter, occlusion, intra-class appearance, viewpoint
General Workflow:
1. Specify an object model
2. Feature representations
3. Generate hypotheses
4. Score hypotheses
5. Resolve detections
Non-max Suppression: discarding bounding boxes with a confidence score below a certain threshold
2-Stage Framework: region proposal and classification stages
1-Stage Detectors: single feed-forward CNN that directly outputs bounding boxes and classification

Object Tracking

Definition: following an object in a sequence of images
Problem Statement: estimate target state (position) over time
Challenges: geometric change, illumination, occlusions, image quality, similar objects, non-linear motion
Main Components: detection and loss detection, estimation, models
Object Representation: point tracking, appearance tracking, silhouette tracking
Motion Estimation: deterministic, probabilistic
Deterministic Methods: quick convergence, efficient, but can get stuck in a local max or min
Probabilistic Methods: can be used in real-time, consider history, entire probability densities, multi-model### Object Tracking and Modelling
Control inputs: change of dynamic system
Models: Object (Temporal dynamics), Sensors (characteristics), Context (background modelling)
Inference: Bayesian inference, estimate state x given measurement data z, control data u, respective state transition and measurement probabilities
Belief: estimate of true state

Recursive Bayes Filter Algorithm

Prediction is corrected at each frame and model updated
Prediction/Time update: Calculate prior belief on dynamic model
Correction/Measurement update: Calculate posterior belief based on measurement model; update belief based on new measurement and incorporate measurement using Bayes theorem

Kalman Filters

Regular Kalman filter: used for linear and regular dynamic models (e.g. airplanes)
Extended Kalman filter: used for non-linear models

Appearance Modelling

Appearance of object needs to be modelled in a way that it is detectable in different frames
Representation needs to be flexible enough to cope with different scales, poses, illuminations and occlusions

Object Approximation

Segmentation
Polygonal approximation
Bounding ellipse/box
Position only
Goal: measure affinity in different frames

Appearance Tracking

Lukas Kanade tracker: minimizes sum of squared differences, uses gradient descent, assigns different weights to pixels
Mean Shift: builds from spatial templates, colour histogram, aims to maximize coefficient by assessing mean shift
Correlation filter: better computational speed, uses Fast Fourier transform to determine position of template patch, optimizes a distance metric
Template of the target object is compared to different regions to find best match
High response for target and low response for background

Feature Representation

Features are extracted from image window
1. Learning phase: learning how to recognize object, discriminative features are needed
1. Detection phase: given object description, algorithm searches for same object in scene captured by following frames
1. Update: descriptor is updated using most recent frame
CNN can be used to extract features

DeepSORT

Uses CNN to improve ability to track objects in the presence of occlusion
Evolution of SORT: uses first Kalman filter in image space and then frame by frame data association using the Hungarian method
DeepSORT replaces association metric with a more informed metric that combines motion and appearance information
Limited number of features
Hungarian algorithm: matching based on cost matrix that considers Mahalanobis distance for motion consistency and cosine distance for appearance similarity

Structure from Motion Problem

Figuring out how the camera moved through space and creating 3D structure of scene
Visual odometry: estimating the position of a camera by looking at changes that are caused by motion
Looking at different features and tracking them

3D Stereo Reconstruction

Must be done in real-time
Stereo cameras: can deliver scaled scenes, have two lenses which allows them to mimic the human eye by taking two slightly different pictures
Mono camera: no scaled scenes
Why is stereo reconstruction needed?
- Avoiding collision of robot with objects
- Better understand semantics of scene
- 3D mapping of environment

Pinhole Camera Model

Mathematical relationship between 3D point and projection into image plane
When you make a very small hole in a box, what is in the outside world will be projected into the back of the box

Epipolar Geometry

Epipolar plane: center of two cameras plus a point in the real world
Conjugate points: points in image plane the real-life point projects to in each camera
Epipoles: projection of one camera into the other; all epipolar lines go through these points
Epipolar lines: point in one image is a line in the other (cause it is seen from different perspectives)
Epipolar plane is defined by P1, e1, and center of camera
Epipolar constraint: a point in one image must lay on a line in the other image

Fundamental Matrix

Described epipolar geometry of two views (3x3 matrix)
Epipolar plane is mathematically described using the fundamental matrix

Homogenous Coordinates

A point in n-dimensional space is represented by n + 1 coordinates
Through triangulations, points in epipolar plane can be found

Disparity Map

Apparent motion of objects between two stereo images
Depth is given by disparity
2D image where each pixel represents a disparity between corresponding points of the two images of a stereo pair

Image Matching

Image matching algorithms identify and match different features such as edges, points, patterns of two or more images
Correspondence of same point in image space allows to determine points position in object space
Feature matching: matching different features; used in image orientation
Area-based matching: used in dense point cloud generation

Feature Matching

Matching is mostly just done at salient points (corners, blobs,…)
Identifying a corner: based on gradients (directional change in the intensity of the image)
Identifying blobs: regions with positive or negative color value compared to neighborhood

Area-Based Matching

Cross-correlation: determine position in search image with a high similarity with a reference pattern
Repeated for all pixels to get dense reconstruction
Problem with correlation: max correlation does not necessarily be the correct match cause of noise, occlusions
Solution: also consider neighboring points

Deep Learning and 3D Reconstructions

Replacing different parts with neural networks, such as cross-correlation with a CNN
Deep feature mapping: 2 image patches that are converted to vectors to be compared
Similarity can be defined by traditional method (feature mapping) or a fully connected decision network
Variants developed that can consider global context without increasing computational loss
Sparse reconstruction: every correctly matched points lead to a single point in 3D

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Test your knowledge of computer vision concepts, including feature representations, object detection, and tracking algorithms.