Computer vision: distinguish image evidence, recognition and inference
Computer vision is the area of computing that analyses images or video to identify patterns, objects and spatial relationships. A system can propose what appears in a picture, where it appears or which pixels belong together. Those proposals need checking against the visible source; recognising a box does not establish what is inside it.
The useful starting question is what the image can support. An object label, a visible count and an explanation of someone’s intention require different evidence. This definition uses a fictional stockroom scene to make those differences inspectable, without claiming an image-processing result.
How computer vision works
A digital image represents a scene as pixel values. A vision method processes those values to produce a task-specific output, such as a category, an object location or a region mask. Learned models use patterns acquired during training; other image-processing methods can use explicit rules. Neither approach directly observes everything about the real-world scene.
Lighting, viewpoint, resolution and obstructions affect the information available. A bright patch may hide printed characters, while a foreground package can conceal another package. Processing can improve the representation of available information, but a persuasive description cannot reveal a hidden surface. Preserve the original image when reviewing a transformed or cropped version.
For video, the input also has a time dimension. A single frame can show position without showing how an object arrived there. The video analysis guide covers a separate clip-review design; this page concerns the image task and the strength of its evidence.
Classification, detection and segmentation
| Task | Question | Typical output | Boundary |
|---|---|---|---|
| Classification | What category describes this image or selected crop? | A label such as package or shelving. | A scene label supplies no individual object count. |
| Detection | Where are the candidate objects? | Object labels with locations, often bounding boxes. | A box can include background and does not reveal concealed contents. |
| Segmentation | Which pixels belong to a class or an individual object? | A pixel-level region mask. | A visible-region mask does not necessarily describe a complete physical object. |
Semantic segmentation groups pixels by category. Instance segmentation separates individual objects of a category. These distinctions describe the output contract, rather than a ranking of methods. Choose the task that answers the actual question: a broad scene category will not meet a requirement to inspect each visible package.
A stockroom-image evidence worksheet
Authored scene description, with illustrative annotations. No photograph, detector run or measured accuracy accompanies this exercise. Imagine a shelf with three fully visible rectangular packages. A fourth rectangular edge appears behind the middle package. One foreground package has a printed symbol, but the wording beneath it is unreadable. Every package is closed.
Call this described source Stockroom A, revision one. The locators below are manual references within that description, not returned coordinates. The example’s visible count concerns complete package shapes; it does not count all packages that might exist on or behind the shelf.
| Locator | Available observation | Proposed label or claim | Review decision |
|---|---|---|---|
| Front shelf | Three complete rectangular package shapes are visible. | Three visible packages. | Accept within the authored scene; total stock remains unknown. |
| Behind middle package | One partly hidden rectangular edge. | A possible additional package. | Retain as a candidate; its complete boundary is obscured. |
| Foreground symbol | A printed symbol with unreadable accompanying words. | A package containing fragile equipment. | Unsupported contents claim; symbol interpretation also needs review. |
| Closed surfaces | No package interior is shown. | All packages contain the expected items. | Unresolved; no interior or independent stock record is supplied. |
A detector could propose four boxes while a reviewer accepts only three fully visible packages and one uncertain candidate. Preserve both records rather than silently converting candidates into confirmed stock. If the task permits counting partial objects, define the minimum evidence for that decision before comparing counts.
An image can also contain a label that says what a package should contain. Reading that label is different from inspecting its contents. Even perfectly recognised wording remains a statement printed on the package, rather than independent confirmation that the item is present.
What is object recognition in computer vision?
Object recognition assigns meaning to visual patterns, such as proposing that a rectangular object is a carton. Detection adds a location contract. The same object can look different from another angle, and different objects can look similar. Treat a recognised category as a task result to validate, especially when only part of the object is visible.
In Stockroom A, the category package is plausible from the described shapes. A more specific label such as medical device would need additional evidence. Context can help form a hypothesis, but a stockroom setting alone does not establish the category or identity of every stored item.
Where the concept meets AI Flowi Workflow
Read File’s loading contract belongs in the component catalogue, and the provider-call contract belongs in the Language Model reference; both are documented in the catalogue; not production-tested by Ai Flowi.
Neither reference alone establishes object detection, segmentation or camera control. A text-message interface also leaves image-pixel acceptance unresolved for the chosen provider. Use Document AI for document reading; the stockroom worksheet instead requires a visual counting policy.
For Stockroom A, decide whether the output should contain complete shapes, possible objects or physical stock. The first policy can report three complete visible packages. The second should list the obscured edge separately, with a reason for uncertainty. The third cannot be answered from this description. To resolve it, obtain another view or an independent stock record that can distinguish the concealed object. Adding a more specific object label would not supply that missing evidence.
Common mistakes and related terms
Do not turn the count of visible objects into a complete inventory, treat an uncertain region as a confirmed object or mistake a category for a verified identity. Keep crop identity and image revision with any annotation; a box checked against one crop cannot automatically be reused after the image changes.
Avoid unattended physical safety decisions from this exercise. It supplies neither the relevant procedure nor complete scene evidence. Data annotation explains how reference labels are prepared. OCR addresses recognised characters, and machine learning pipelines explain the distinction between learning a model and applying it.
By Ai Flowi · Published