A) The use of computer screens to display images.
B) The process of filtering and enhancing visual images.
C) The study of how human vision works.
D) The field of study that enables computers to interpret and understand visual information from the real world.

A) Blurring images for artistic effect.
B) Randomly distorting images.
C) Enhancing image quality and reducing noise for better analysis.
D) Changing the image dimensions.

A) Combining multiple images into one.
B) Creating a mirror image of the original.
C) Dividing an image into meaningful regions or objects for analysis.
D) Removing colors from an image.

A) F1 Score
B) R-squared
C) Accuracy
D) Mean Squared Error

A) Adding more layers to the network
B) Using smaller batch sizes
C) Increasing the learning rate
D) Dropout regularization

A) Using pre-trained models and fine-tuning for a specific task.
B) Transferring gradients during backpropagation.
C) Transferring image pixels to a new image.
D) Transferring images between different devices.

A) Reducing the spatial dimensions of the input.
B) Normalizing input values.
C) Introducing non-linearity to the network.
D) Increasing the number of parameters.

A) ReLU (Rectified Linear Unit)
B) Linear
C) Sigmoid
D) Tanh

A) Creating composite images.
B) Converting images to grayscale.
C) Blurring images for privacy protection.
D) Summarizing the performance of a classification model using true positive, false positive, true negative, and false negative values.

A) Weather dataset
B) Spam dataset
C) ImageNet
D) Song lyrics dataset

A) Identifying and delineating individual objects within a scene.
B) Smoothing pixel intensities.
C) Converting images to black and white.
D) Applying color filters to images.

A) Mapping one image onto another image plane.
B) Detecting object edges.
C) Normalizing image histograms.
D) Blurring image boundaries.

A) Fourier transform
B) Lucas-Kanade method
C) Histogram equalization
D) Gaussian blur

A) InceptionNet
B) AlexNet
C) ResNet (Residual Network)
D) VGGNet

A) Mean Squared Error
B) Cross-Entropy Loss
C) Binary Cross-Entropy Loss
D) L1 Loss

A) ReLU
B) Softmax
C) Sigmoid
D) Tanh

A) Increasing image resolution
B) Non-local means denoising
C) Rotating images
D) Adding noise to images

A) Support Vector Machines (SVM)
B) Convolutional Neural Networks (CNNs)
C) K-Nearest Neighbors (KNN)
D) Principal Component Analysis (PCA)

A) Selective Image Filtering Technique
B) Semi-Integrated Face Tracking
C) Scale-Invariant Feature Transform
D) Segmentation of Image Features and Textures

A) Controlled Neural Network
B) Complex Neuron Network
C) Convolutional Neural Network
D) Computerized Neuron Network

A) Activation layer
B) Fully connected layer
C) Pooling layer
D) Convolutional layer

A) Transfer Learning
B) Image Cropping
C) Noise Injection
D) PCA Dimensionality Reduction

A) Image segmentation
B) Image classification
C) Feature extraction
D) Object detection