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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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