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莫纳什大学代写作业: 高层适应、目标识别

 莫纳什大学代写作业:  高层适应、目标识别



莫纳什大学代写作业:  高层适应、目标识别

Object identification, whereby an object is seen as the same object despite substantial changes in other attributes (e.g., illumination, location, occlusion), is one of the major challenges for the visual system. The detection and recognition of meaningful objects in complex environments is a crucial skill that underlies a wide range of behaviors, from diagnosing tumors on medical images to identifying preys and predators, and from recognizing edible foods to finding familiar faces in a crowd. In humans and primates with visual abilities, these processes operate effortlessly, quickly and automatically. However, the computational challenges of object recognition are far from trivial. In particular, the recognition of coherent meaningful objects involves integration of components at different levels of visual complexity, starting from local contours at primary visual areas to complex object at higher level modules. These representations are highly tolerant to environmental variations such as changes in viewpoint, size, position, background clutter, etc.

Many studies now provide converging evidence that the neural substrates involved in object recognition are mainly located in the ventral visual stream. The underlying mechanism appears to consist of a number of processing stages that are organized in rough hierarchical order. The information is projected from retina to V1, and from V1 to higher cortical areas ending up at AIT (Anterior Inferior Temporal Cortex) in monkey which is considered homologous to human area LOC (Lateral Occipital Complex) and the fusifurmgyrus. These areas, unlike the lower ones, encode object identity. For example, it has been shown that cells in area TE of the IT (inferior temporal) cortex of the monkey brains respond selectively to various complex object-features. Moreover, the neurons that respond to similar features are clustered in columnar region stretched out vertically to the cortical surface (Tanifuji et al. 2001). Furthermore, these neurons represent the complex visual objects using a sparse population code (Tanaka et al. 1991; Tanaka 1999). On the other hand, several fMRI studies have shown a similar distributed representation of objects in human ventral pathway. For example,studies contrasting faces, houses and chairs have identified differential activation maps bilaterally in ventral temporal and ventral occipital cortices and superior temporal sulcus (Ishai et al. (1999), Ishai et al.(2000). They propose that bject representation in the ventral visual pathway is done by a distributed representation of information about the form and not by small highly selective cortical patches. Figure 1 sketches a class of object recognition models starting from the retinal input ending up in categorization and identification and shows a sketch of a class of object recognition models.

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