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