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

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

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

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

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 images between different devices.
C) Transferring image pixels to a new image.
D) Transferring gradients during backpropagation.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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