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

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

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

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

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

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

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

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

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

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

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) Cross-Entropy Loss
D) L1 Loss

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

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

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

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

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

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

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

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