A) The field of study that enables computers to interpret and understand visual information from the real world. B) The use of computer screens to display images. C) The process of filtering and enhancing visual 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) Combining multiple images into one. C) Creating a mirror image of the original. D) Removing colors from an image.
A) F1 Score B) Mean Squared Error C) R-squared D) Accuracy
A) Adding more layers to the network B) Using smaller batch sizes C) Increasing the learning rate D) Dropout regularization
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) Introducing non-linearity to the network. B) Reducing the spatial dimensions of the input. C) Normalizing input values. D) Increasing the number of parameters.
A) Tanh B) Sigmoid C) Linear D) ReLU (Rectified Linear Unit)
A) Creating composite images. B) Blurring images for privacy protection. C) Summarizing the performance of a classification model using true positive, false positive, true negative, and false negative values. D) Converting images to grayscale.
A) Song lyrics dataset B) ImageNet C) Spam dataset D) Weather dataset
A) Smoothing pixel intensities. B) Applying color filters to images. C) Identifying and delineating individual objects within a scene. D) Converting images to black and white.
A) Lucas-Kanade method B) Gaussian blur C) Histogram equalization D) Fourier transform
A) Normalizing image histograms. B) Mapping one image onto another image plane. C) Detecting object edges. D) Blurring image boundaries.
A) Increasing image resolution B) Rotating images C) Adding noise to images D) Non-local means denoising
A) Image segmentation B) Feature extraction C) Image classification D) Object detection
A) Computerized Neuron Network B) Complex Neuron Network C) Controlled Neural Network D) Convolutional Neural Network
A) Activation layer B) Convolutional layer C) Pooling layer D) Fully connected layer
A) Cross-Entropy Loss B) Mean Squared Error C) Binary Cross-Entropy Loss D) L1 Loss
A) InceptionNet B) VGGNet C) ResNet (Residual Network) D) AlexNet
A) K-Nearest Neighbors (KNN) B) Support Vector Machines (SVM) C) Convolutional Neural Networks (CNNs) D) Principal Component Analysis (PCA)
A) Segmentation of Image Features and Textures B) Scale-Invariant Feature Transform C) Semi-Integrated Face Tracking D) Selective Image Filtering Technique
A) Softmax B) ReLU C) Sigmoid D) Tanh
A) Transfer Learning B) Image Cropping C) PCA Dimensionality Reduction D) Noise Injection |