The Eyes of AI: Different Types of Computer Vision Algorithms

7 min readFeb 1, 2024


The strains among technological know-how fiction and truth blur ever faster as technology like ChatGPT, Google’s GenAI, and Gemini usher in a brand new generation. Artificial intelligence (AI) is hastily remodeling our global, infiltrating our lives in methods we won’t even realize. From facial popularity unlocking our phones to AI-powered suggestions influencing our purchases, this technology is quietly weaving itself into the fabric of our routines.

The pace of development is, certainly, breathtaking. frequently stuck in the whirlwind of everyday lifestyles, we may miss the subtle but ubiquitous presence of AI in normal responsibilities. However, for engineers, ignoring this transformative generation is no longer an alternative. Embracing its capacity and actively engaging with its improvement is paramount.

Consequently, in this technical blog, we delve into the exciting realm of Computer Vision, a branch of AI and machine learning with vast potential to shape our future.

In the vast landscape of artificial intelligence, computer vision emerges as a critical field that focuses on extracting information from digital images and videos. Its applications span across diverse domains, ranging from self-driving cars and robotics to medical imaging and augmented reality.

Types of Computer Vision Algorithms

1. Image Classification

Image classification involves assigning a label to an image based on its content. While seemingly straightforward, achieving high accuracy remains challenging due to the inherent complexities of images — noise, clutter, and interpretation difficulties.

Image classification
Image classification

Image Classification Use Case:

  1. Retail industry: Image classification performs a crucial role in self-checkout kiosks and stock management systems by automatically identifying products based on their look. This streamlines the checkout process for customers and helps businesses maintain accurate stock stages and levels.
  2. Medical imaging: In healthcare, image classification algorithms can analyze medical scans (X-rays, MRIs, etc.) to come across abnormalities or classify tissue sorts, aiding in quicker diagnosis and treatment selections and decisions.

2. Semantic Segmentation

The feature of Semantic segmentation is no label. Semantic segmentation takes a step further, assigning a label to each pixel in an image including the background, with a different color based on its category class or class label.

Semantic Segmentation

Semantic Segmentation Use Case:

  1. Self-driving vehicles: For autonomous vehicles to navigate properly and safely, they need to understand their environment in detail. Semantic segmentation helps self-driving vehicles phase the street, road, pedestrians, motors, and different objects, allowing them to make informed decisions.
  2. Medical image analysis: In medical imaging analysis, semantic segmentation may be used to phase organs, tumors, or different structures inside medical scans, imparting precious insights for diagnosis and treatment planning.

3. Instance Segmentation

Instance Segmentation involves identifying the boundaries of objects in an image and labeling pixels with different colors. The exact outline of objects within an image is provided through image segmentation.

Instance Segmentation

Instance Segmentation Use Case:

  1. Security and surveillance: Security cameras can use instance segmentation to perceive and track individual people inside a crowd, enabling them to detect suspicious activity or monitor specific individuals.
  2. Object tracking in videos: Instance segmentation is important for monitoring objects in videos, along with athletes in sports evaluation and analysis or animals in wildlife documentaries.

4. Image Classification with Localization

Image classification with localization is a more complex version of the image classification problem. Assigns a class label to the image and also creates a bounding box around single objects in the image.

Image Classification with Localization

Image Classification with Localization Use Case:

  1. Self-driving cars: In addition to identifying objects, self-driving cars must recognize their precise vicinity relative to those objects. Image classification with localization enables them to determine the position of motors, pedestrians, and different objects on the street.
  2. Aerial imaging: Drones and other aerial vehicles can use image classification with localization to identify and map objects on the ground, such as buildings, roads, or wildfires.

5. Object Recognition

Object Recognition Similarly, object recognition identifies objects in an image by outputting the object’s class label and class probability. For example, the class label might be “rose” and the associated class probability might be 0.1. The important point here is that the object recognition model recognizes whether there is a rose in the image. However, it cannot detect where the object is.

Object Recognition

Object Recognition Use Case:

  1. Retail product detection: Smart shelves and refrigerators can use object recognition to identify and track items, reducing the need for manual inventory checks and restocking.
  2. Brand Awareness Analysis: Detected and identified various brand logos in images posted on social media. This allowed us to analyze brand awareness and customer interactions and provide valuable insights into our brand.

6. Object Detection

Object detection, as the name suggests, detects specific objects in an image. This is similar to Image classification with localization and is especially useful when there are multiple types of objects in a single image. Bounding boxes are used for object detection and localization.

Unlike object recognition, object detection tells you what objects are in the image and outputs a bounding box (x, y, width, height) to indicate the location of the object inside the bounding box.

Object Detection

Object Detection Use Case:

  1. Self-Driving Cars: Object Detection is the backbone of secure self-autonomous driving. It identifies nearby cars, pedestrians, cyclists, and road signs and symptoms, enabling real-time decision-making for navigation and impediment avoidance.
  2. Manufacturing Quality Control: It enables identifying defects in products in the course of manufacturing by detecting anomalies in shape, size, or texture, ensuring product quality and consistency.

7. Pattern Recognition

Pattern recognition detects and identifies repeating shapes, colors, and other visual indicators in visual input. Popular pattern recognition applications for computer vision include facial recognition, movement recognition, OCR, and medical image recognition.

Pattern Recognition
Pattern Recognition

Pattern Recognition Use Case:

  1. Fraud detection: Pattern recognition can be used to identify fraudulent transactions with the aid of detecting patterns that might be inconsistent with normal behavior.
  2. Customer behavior analysis: Pattern recognition can be used to analyze purchaser conduct to discover traits and styles that may be used to enhance advertising and marketing campaigns.

8. Facial Recognition

Face Recognition Face recognition is a type of advanced object detection where the primary object is a human face. Facial recognition can detect multiple faces within an image, along with key facial attributes such as emotional state or whether a person is wearing a hat. Some facial recognition models can also verify identity. It can therefore be used to control access to sensitive areas.

Facial Recognition

Facial Recognition Use Case:

  1. Security: Facial recognition is regularly utilized in security applications, which include getting the right of entry to control and surveillance, to perceive individuals, and to save unauthorized admission.
  2. Access control: Facial recognition can be used to grant or deny access entry to restricted areas, such as buildings, facilities, or events.

9. Edge Detection

Edge Detection Edge detection is a technology that extracts edges from an image by identifying the boundaries of objects in the image. This is the first step in object recognition. The main principle of edge detection is to detect changes in brightness and intensity levels.

Edge Detection
Edge Detection

Edge Detection Use Case:

  1. Robot Navigation and Obstacle Avoidance: Robots navigating complex environments rely on edge detection to perceive object boundaries and keep away from collisions. By detecting edges, robots can map their surroundings and safely maneuver around obstacles.
  2. Fingerprint Identification: In biometric identification systems, edge detection plays an essential role in extracting and analyzing fingerprint features for accurate individual identification.

10. Feature Matching

Feature matching is a type of pattern detection that compares features of an image that may have different orientations, perspectives, lighting, sizes, and colors.

Feature Matching — Orientations
Feature Matching — Perspectives
Feature Matching — Lighting
Feature Matching — Sizes
Feature Matching — Colors

Automatic object tracking, 3D object reconstruction, robot navigation, image retrieval, and indexing are just some of the applications of feature detection and matching.

Feature Matching Use Case:

  1. Object tracking: Feature matching can be used to track the movement of objects over time by comparing features in images taken at different times.
  2. 3D reconstruction: Feature matching can be used to reconstruct 3D objects from more than one image by using matching capabilities between the images.


In conclusion, computer vision problems find considerable applications throughout diverse industries, shaping the way we interact with technology. As the field continues to conform, we are able to anticipate even greater groundbreaking improvements in the integration of artificial intelligence with visual data.

AI sees reality differently. Are you ready to join the vision?




Partner Engineer at Google | Solutions Architect | Cloud Engineer | AWS & GCP Certified