A) The use of computer screens to display images.
B) The study of how human vision works.
C) The process of filtering and enhancing visual images.
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) Changing the image dimensions.
C) Enhancing image quality and reducing noise for better analysis.
D) Randomly distorting images.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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