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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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