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首页> 外文期刊>IAENG Internaitonal journal of computer science >A Hierarchical Temporal Memory Based Hand Posture Recognition Method
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A Hierarchical Temporal Memory Based Hand Posture Recognition Method

机译：基于分层时间记忆的手势识别方法

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摘要

In various pattern recognition applications, angle variation is always a main challenging factor for producing reliable recognition. To increase the endurance ability on angle variation, this paper adopts a Hierarchical Temporal Memory (HTM) algorithm which applies temporal information to organize time-sequence change of image features, and constructs invariant features so that the influence of angle variation can be effectively learnt and overcome. The proposed multi-angle HTM-based posture recognition method consists of two main modules of Hand Posture Image Pre-processing (HPIP) and Hand Posture Recognition (HPR). In HPIP, each input image is first processed individually by skin color detection, foreground segmentation and edge detection. Then, the three processed results are further combined linearly to locate a hand posture region. If a forearm exists in the located hand posture region, a forearm segmentation process will be executed to keep only the part of palm. In HPR, the normalized image is forwarded to a HTM model for learning and recognizing of different kinds of hand postures. Experiment results show that when using the same continuous unconstrained hand posture database, the proposed method can achieve an 89.1% high recognition rate for discriminating three kinds of hand postures, which are scissors, stone and paper, and outperforms both Adaboost (78.1%) and SVM (79.9%).
机译：在各种模式识别应用中，角度变化始终是产生可靠识别的主要挑战因素。为了提高对角度变化的承受能力，本文采用了一种分层时间记忆（HTM）算法，该算法运用时间信息来组织图像特征的时间序列变化，并构造不变特征，从而可以有效地学习和掌握角度变化的影响。克服。所提出的基于HTM的多角度姿势识别方法包括手势图像预处理（HPIP）和手势姿势识别（HPR）两个主要模块。在HPIP中，首先通过肤色检测，前景分割和边缘检测分别处理每个输入图像。然后，将三个处理结果进一步线性组合以定位手部姿势区域。如果在定位的手部姿势区域中存在前臂，则将执行前臂分割过程以仅保留手掌的一部分。在HPR中，将归一化的图像转发到HTM模型，以学习和识别各种手势。实验结果表明，在使用相同的连续无约束手部姿势数据库的情况下，该方法可以区分剪刀，石头和纸三种手部，其识别率高达89.1％，均优于Adaboost（78.1％）和Adaboost。支持向量机（79.9％）。

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