Some interview questions and answers for the computer vision field. Part 5 shows some image segmentation questions and answers.

Image Segmentation Interview Questions & Answers

1. What is Image Segmentation?

   Answer: Image Segmentation is a computer vision task that involves dividing an image into distinct, meaningful regions or segments based on certain criteria, such as color, intensity, texture, or other features. Each segment represents a region of similar characteristics.

2. What are the different types of Image Segmentation?

   Answer: There are three main types of Image Segmentation:

   • Semantic Segmentation: Assigns a label to every pixel in an image to represent the class of the object or region it belongs to.

   • Instance Segmentation: Identifies individual objects or instances in an image and assigns a unique label to each one.

   • Panoptic Segmentation: Combines both semantic and instance segmentation, providing a comprehensive understanding of the image by labeling all pixels with object classes and instance IDs.

3. What is the difference between Semantic Segmentation and Instance Segmentation?

   Answer:

   • Semantic Segmentation assigns a label to every pixel in an image based on the category it belongs to (e.g., road, sky, person), without distinguishing between individual instances of the same class.

   • Instance Segmentation goes further by not only assigning a label to each pixel, but also differentiating between different instances of the same class (e.g., distinguishing between different people in an image).

4. Explain the concept of Convolutional Neural Networks (CNNs) in Image Segmentation.

   Answer: CNNs are used in Image Segmentation for their ability to automatically learn hierarchical features from the data. In Segmentation tasks, CNNs typically have an encoder-decoder architecture, where the encoder extracts features from the input image and the decoder generates a segmentation map with the same spatial dimensions.

5. What are some popular architectures used for Image Segmentation?

   Answer:

   • U-Net: Known for its symmetrical encoder-decoder structure, widely used for biomedical image segmentation.
   • Mask R-CNN: Combines object detection and instance segmentation, popular for precise object delineation.
   • FCN (Fully Convolutional Network): Converts any pre-trained classification network into a segmentation network.
   • DeepLab: Utilizes atrous convolutions to capture multi-scale context.

6. Explain the concept of Atrous (Dilated) Convolution in Image Segmentation.

   Answer: Atrous Convolution allows for an increased receptive field without increasing the number of parameters. It introduces gaps or dilations between the weights in the kernel, effectively increasing the stride of the convolution operation. This is particularly useful in capturing features at different scales.

7. What are some challenges in Image Segmentation?

   Answer: Challenges in Image Segmentation include:

   • Boundary Ambiguity: Determining precise object boundaries can be difficult, especially in regions with gradual transitions.
   • Class Imbalance: Some classes may be underrepresented, leading to imbalanced training data.
   • Variability in Object Appearance: Objects of the same class can have significant variation in appearance, making it challenging to generalize.

8. What are some practical applications of Image Segmentation?

   Answer: Image Segmentation has various applications, including:

   • Medical Imaging: Identifying and analyzing specific structures or anomalies in medical images.
   • Autonomous Vehicles: Segmenting objects in the environment for navigation and object detection.
   • Satellite Imagery: Land cover classification, urban planning, and environmental monitoring.
   • Biometrics: Face or fingerprint segmentation for identification.