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首页> 外文会议>Optics and Photonics for Information Processing; Proceedings of SPIE-The International Society for Optical Engineering; vol.6695 >A Biologically Inspired Neural Network Model to Transformation Invariant Object Recognition
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A Biologically Inspired Neural Network Model to Transformation Invariant Object Recognition

机译：启发式神经网络模型用于变换不变物体识别

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摘要

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to perform a successful recognition task in general. Further, the residual critic error in DHP is generally smaller than that of HDP, and DHP achieves a 100% success rate more frequently than HDP for individual objects/subjects. On the other hand, HDP is more robust than the DHP as far as success rate across the database is concerned when applied in a stochastic and uncertain environment, and the computational time involved in DHP is more.
机译：变换不变式图像识别由于在诸如军事行动，机器人技术，医疗实践，地理场景分析等许多领域的广泛应用而一直是活跃的研究领域。这项研究的主要目标是在存在图像变换（例如分辨率，旋转，平移，缩放和遮挡）的情况下检测物体。我们研究了这种启发不变的对象识别的生物启发神经网络（NN）模型。在用于NN的经典训练测试设置中，性能很大程度上取决于训练所涉及的变换或定向范围。但是，更严重的难题是，可能没有足够的培训数据来成功学习，甚至根本没有培训数据。为了缓解此问题，提出了一种生物学启发的强化学习（RL）方法。在本文中，探索了RL方法以进行不同类型的转换（例如，比例，大小，分辨率和旋转）的对象识别。 RL是在自适应评论家设计（ACD）框架中实现的，该框架分别近似于动作网络和评论家网络的神经动力学编程。研究了两种ACD算法，如启发式动态规划（HDP）和双重启发式动态规划（DHP），以获得变换不变目标识别。使用图像中的模拟转换以及带有姿势变化的大型UMIST人脸数据库，对两种学习算法进行统计评估。在人脸数据库身份验证的情况下，使用了UMIST数据库中来自20个不同主体的人脸的90度平面外旋转。我们的仿真结果表明，这两种用于不变变换对象识别和面部识别的设计都具有令人鼓舞的结果。比较这两种算法，DHP的学习能力优于HDP，因为DHP通常执行成功的识别任务所需的步骤更少。此外，DHP中的剩余批评者错误通常小于HDP，而对于单个对象/对象，DHP的成功率要比HDP高100％。另一方面，在随机和不确定的环境中应用时，就跨数据库的成功率而言，HDP比DHP更健壮，并且DHP涉及的计算时间更长。

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