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

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) Removing colors from an image.
C) Creating a mirror image of the original.
D) Combining multiple images into one.

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

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

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

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

A) Blurring images for privacy protection.
B) Creating composite images.
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) Segmentation of Image Features and Textures
B) Scale-Invariant Feature Transform
C) Semi-Integrated Face Tracking
D) Selective Image Filtering Technique

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

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

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

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

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

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

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

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

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

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

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

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

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