Week 1: Introduction into Computer Vision Flashcards
What is the definition of ‘computer vision’?
Computer vision is a field of computer science that focuses on enabling computers to identify and understand objects and people in images and videos. Like other types of AI, computer vision seeks to perform and automate tasks that replicate human capabilities.
Define ‘image acquisition’ and its components.
Image acquisition involves capturing visual data. It includes data sampling (capturing discrete points from a continuous signal) and quantization (representing sampled points with a limited range of values).
How are images represented in computer systems?
Images are typically represented as two-dimensional matrices, where each element corresponds to a pixel. These matrices depict the image’s horizontal and vertical pixels.
Explain the concept of resolution in digital images.
Resolution refers to the amount of visible detail in an image and is related to the number of pixels. Higher resolution often indicates better image quality.
What are the fundamental components of colour space in digital images?
Common colour spaces include RGB (red, green, blue), CMY (cyan, magenta, yellow), and HSI (hue, saturation, intensity), each representing colours differently.
Describe the process of quantization in image processing.
Quantization involves reducing the number of colours in an image by mapping similar colours to a smaller set of values, which helps in compression and storage efficiency.
What role do cones and rods play in human vision?
Cones are responsible for color vision and detecting fine details, while rods perceive brightness but do not discern color.
Explain the difference between intensity and chrominance in color representation.
Intensity represents the brightness of a color, while chrominance denotes the color itself, measuring how much white light is mixed with it.
How are RGB values represented in digital images?
RGB values are typically represented by combining red, green, and blue components to create a vast array of colors.
What is the significance of HSI color space in image processing?
HSI color space is advantageous for human perception-based applications due to its representation of hue, saturation, and intensity.
How are resolution and pixel count related in digital imaging?
Resolution is related to the pixel count, with more pixels often indicating higher image quality and detail.
What are the characteristics of image kernels?
Image kernels are small matrices used in image effects and manipulation, altering pixel values based on their neighboring pixels.
What is the impact of illumination on color perception in images?
Illumination affects color perception as it influences how light is reflected off objects and the wavelengths that reach our eyes.
Discuss the differences between object representation and image representation.
Objects are three-dimensional and continuous in space, while images are two-dimensional, discrete in space, and represent a single snapshot in time.
Explain the significance of human cones in color vision.
Cones are responsible for detecting different wavelengths of light, allowing humans to perceive various colors.
What distinguishes a natural image from a synthetic image?
Natural images depict scenes from the real world, while synthetic images are generated artificially.
Discuss the characteristics of document images.
Document images typically contain text, graphs, or tables and are often characterized by high contrast.
How do variations in pixel count affect image quality?
Higher pixel count often leads to better image quality, but artificially increasing pixel count without improving image detail may not enhance quality.
In image representation, what do the elements in a matrix signify?
Each element in an image matrix represents a pixel’s intensity or color value, forming the visual representation of the image.
Can computers beat human vision?
Yes and no
* Humans are generally better at
deep understanding and
interpretation
* Computers are generally better at
large-scale analysis
Why Sampling?
- Sampling can solve such a problem by
taking samples at the fixed time
interval, as shown in figures, where
the time T represents the sampling
interval or sampling period in
seconds. - This process is called sample and hold.
Since there exists one amplitude level
for each sampling interval.
Face image retrieval (FIR)
systems aim to find faces that match a
query given by the user
Face Recognition
Systems aim to label a face with a name from a database of faces
Face Verification
Systems aim to confirm who someone says they are
What is the primary focus when dealing with Haar cascades in the context of face detection using OpenCV?
The primary focus revolves around comprehending and executing Haar cascades within OpenCV for face detection purposes
What are some fundamental tasks when dealing with images in Python?
Loading, displaying, and storing images are fundamental tasks in Python when working with image processing applications.
What operations constitute basic image processing in Python?
Basic image processing involves performing operations like blurring, sharpening, or adjusting contrast and brightness on images.
How do image transformations contribute to image processing in Python?
Image transformations involve resizing, rotating, or flipping images, enabling various alterations for processing purposes.
How are images typically represented in Python code?
Images are commonly represented as arrays or matrices of pixel values in Python code for image processing tasks.
Why is Matplotlib significant in the realm of image processing in Python?
Matplotlib provides tools to visualize images, facilitating analysis and understanding during various stages of image processing workflows in Python.
