Table of Contents
Question
What is the primary difference between object detection, object recognition, and image segmentation?
A. Object detection classifies objects, object recognition detects object locations, and image segmentation identifies object categories.
B. Object detection detects objects in videos, object recognition identifies object types, and image segmentation tracks object movements.
C. Object detection divides images into segments, object recognition detects object locations, and image segmentation classifies objects.
D. Object detection identifies object locations and categories, object recognition classifies objects, and image segmentation divides images into meaningful segments.
Answer
D. Object detection identifies object locations and categories, object recognition classifies objects, and image segmentation divides images into meaningful segments.
Explanation
To fully understand the distinctions between these three computer vision tasks, it’s important to clarify their objectives and methods:
Object Detection
Object detection involves identifying both the location and category of objects within an image or video. It provides bounding boxes around detected objects and assigns a class label to each box. This task is critical for applications such as autonomous driving, where identifying and localizing pedestrians, vehicles, or traffic signs is essential.
Key Features:
- Combines localization (where the object is) and classification (what the object is).
- Outputs bounding boxes around objects.
- Example: Detecting a car in an image and drawing a box around it labeled “Car.”
Use Cases: Video surveillance, robotics, autonomous vehicles.
Object Recognition
Object recognition focuses solely on identifying what an object is (classification) without determining its location in the image. It processes an entire image to assign a single or multiple class labels.
Key Features:
- Classifies objects but does not localize them.
- Example: Recognizing that an image contains a “Dog” without specifying where it is in the image.
Use Cases: Image search engines, tagging in photo libraries.
Image Segmentation
Image segmentation divides an image into meaningful regions by assigning a label to each pixel. This provides a more granular understanding of the image compared to detection or recognition. Segmentation can be further categorized into:
Semantic Segmentation: Assigns a class label to every pixel (e.g., all pixels belonging to “Car”).
Instance Segmentation: Differentiates between individual instances of the same class (e.g., separating two cars in one image).
Key Features:
- Focuses on pixel-level classification for precise boundary delineation.
- Example: Identifying and coloring all pixels of a car in an image.
Use Cases: Medical imaging (tumor detection), autonomous driving (lane marking).
Why Option D is Correct
Option D accurately describes the roles of these tasks:
- Object detection combines localization and classification.
- Object recognition focuses on classification only.
- Image segmentation divides images into detailed regions for fine-grained analysis.
Other options misrepresent these tasks:
- Option A incorrectly swaps roles between detection and recognition.
- Option B introduces irrelevant concepts like tracking movements.
- Option C confuses segmentation with classification tasks.
By understanding these distinctions, developers can select the appropriate computer vision technique for their specific application needs.
Computer Vision for Developers skill assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Computer Vision for Developers exam and earn Computer Vision for Developers certification.