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

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

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

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

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

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

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

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

A) Creating composite images.
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) Converting images to grayscale.

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

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

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

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

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

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

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

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

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

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

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

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

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

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