RATIONALE FOR THE USE OF IMAGE Sample Clauses

RATIONALE FOR THE USE OF IMAGE. SUB­SECTIONS 1. Whole images often contain background information not conducive to image classification. This adds noise to the classification problem and exacerbates an already complex task. 2. Whole objects may not be easily recognisable as a member of their object type (from a computer vision standpoint). Indeed, in many cases, an objects overall appearance may vary drastically, although certain sections remain the same. As an example of this, take motorcycles; the body of a motorcycle often varies wildly from manufacturer to manufacturer, however, certain parts (e.g. wheels, handlebars) remain the same or similar and can therefore be tracked. 3. In real world environments, objects are often partially occluded. This means that classification based on whole image views can fail due to other objects within the scene. The occluding objects may distort the object to be classified in very unpredictable ways. Patch based systems have greater tolerance to this problem as sections of the object are often available for classification in partially occluded circumstances. If any patch (image sub­section) were not centred on, for example, the aeroplane in Figure 5.1 the outcome would obviously be quite erratic; however, even patches, which are centred on the aeroplane, may not hold useful information. Take for example, the patch that captures the intake of the right­hand engine. This is simply a pure black patch, holding little usable information. To rectify this problem, image subsections should be chosen based on a set of criterion. These criteria may be based on capturing image structure, important colour or gray­level changes, or upon repeatability, i.e. how often a patch is associated with an object class and how reliably these matching points can be found. The main drawback to using object sub sections for this type of task is that there is a computational cost in finding and using them, as well as normalisation if using an invariant image patch (see Section 5.2.4). As this cost is not prohibitively high, and the benefits of increased classification accuracy far outweigh the computational costs, these techniques are further discussed in detail.
Sample 1
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