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

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

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

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

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) Normalizing input values.
C) Reducing the spatial dimensions of the input.
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) Semi-Integrated Face Tracking
B) Scale-Invariant Feature Transform
C) Segmentation of Image Features and Textures
D) Selective Image Filtering Technique

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

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

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

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

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

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

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

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

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

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

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

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

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