where can we show a scene of land or natural environment
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Hi
Explanation:
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The problem of scene analysis has been studied in a number of different fields over the past decades. These studies have led to important insights into problems of scene analysis, but not all of these insights are widely appreciated, and there remain critical shortcomings in current approaches that hinder further progress. Here we take the view that scene analysis is a universal problem solved by all animals, and that we can gain new insight by studying the problems that animals face in complex natural environments. In particular, the jumping spider, songbird, echolocating bat, and electric fish, all exhibit behaviors that require robust solutions to scene analysis problems encountered in the natural environment. By examining the behaviors of these seemingly disparate animals, we emerge with a framework for studying scene analysis comprising four essential properties: (1) the ability to solve ill-posed problems, (2) the ability to integrate and store information across time and modality, (3) efficient recovery and representation of 3D scene structure, and (4) the use of optimal motor actions for acquiring information to progress toward behavioral goals.
Introduction
In recent decades, research on scene analysis has advanced in many different fields. Perceptual studies have characterized the many cues that contribute to scene analysis capabilities. Computational approaches have made great strides in developing algorithms for processing real-world scenes. Animal behavior and neurobiological studies have investigated animal capabilities and neural representations of stimulus features. In spite of these advances, we believe there remain fundamental limitations in many of the ways scene analysis is defined and studied, and that these will continue to impede research progress until these shortcomings are more widely recognized and new approaches are devised to overcome them. The purpose of this article is to identify these shortcomings and to propose a framework for studying scene analysis that embraces the complex problems that animals need to solve in the natural environment.
A major limitation we see in current approaches is that they do not acknowledge or address the complexity of the problems that need be solved. Experiments based on simplistic, reflexive models of animal behavior, or with the implicit assumption of simple feature detection schemes, have little chance of providing insight into the mechanisms of scene analysis in complex natural settings. An additional limitation lies with the extensive use of “idealized” stimuli and stripped down tasks that yield results which are often difficult to generalize to more ecologically relevant stimuli and behaviors. For example, scene analysis experiments designed around auditory tone bursts are of limited value in helping us to understand how complex acoustic patterns such as speech are separated from noisy acoustic backgrounds. Visual grouping experiments using bar-like stimuli are a far cry from the situation a predator faces in detecting and tracking prey in a complex visual environment. At the same time, computational approaches in the engineering and computer science community, although often applied to natural scenes, have provided only limited insight into scene perception in humans and other animals. The disconnect here is due to the fact that tasks are chosen according to certain technological goals that are often motivated by industrial applications (e.g., image search) where the computational goals are different from those in more ecologically relevant settings. In the neuroscience community, studies of animal behavior and physiology have focused largely on specific stimulus features or assume feedforward processing pipelines that do not address the more complex set of problems required for extraction of these stimulus features in natural scenes.
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