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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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