In computer vision, a blob is a region of an image that differs in properties like color or intensity from its surrounding areas. The term "blob" refers to areas that are connected and form a distinct pattern within an image, typically used to represent objects, features, or areas of interest in object detection and segmentation tasks. Blob detection methods aim to identify these regions by analyzing properties such as shape, size, and texture. A common technique for blob detection is the Laplacian of Gaussian (LoG), which highlights regions of the image that show significant changes in intensity. Another approach is Connected Component Labeling, which marks pixels that are connected based on a threshold, helping to group them into distinct blobs. In more advanced applications, blob analysis can be used to measure characteristics like area, circularity, and perimeter, which are helpful in tasks like object tracking and recognition. A practical example of blob detection is its use in object tracking in video streams, where each moving object is treated as a blob for further analysis. Blobs are important in a variety of computer vision tasks because they help isolate areas of interest, making it easier to focus on specific objects in the image.
What is blob in computer vision?
- Retrieval Augmented Generation (RAG) 101
- Master Video AI
- GenAI Ecosystem
- The Definitive Guide to Building RAG Apps with LlamaIndex
- AI & Machine Learning
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
Can distance glasses be used for reading and computers?
Distance glasses are optimized for viewing objects far away and are generally not suitable for close-up tasks like readi
How do serverless platforms enable API rate limiting?
Serverless platforms enable API rate limiting by providing built-in mechanisms and tools that help manage the number of
What are common mistakes users make when formulating queries for DeepResearch that could lead to poor results?
When users submit queries to DeepResearch, common mistakes often stem from unclear or unstructured input. A primary issu