Hand Gesture of Recognition Pattern Analysis by Image Treatment Techniques

Area of Science:

• Computer Vision
• Human-Computer Interaction
• Robotics

Background:

• Gesture recognition systems are crucial for intuitive human-computer interaction.
• Depth cameras provide valuable 3D information for motion analysis.
• Efficient algorithms are needed to process complex visual data in real-time.

Purpose of the Study:

• To develop a C++ program for recognizing human gestures captured by a depth camera.
• To explore and present 3D image acquisition techniques (stereovision, structured light, time-of-flight).
• To implement and analyze image processing methods like morphology and segmentation for gesture analysis.

Main Methods:

• Acquisition of sequential depth images using a depth camera.
• Development of a modular C++ program with three key processing blocks: hand detection, movement detection, and gesture classification.
• Application of image processing techniques including morphology and segmentation.

Main Results:

• Successful implementation of a C++ program capable of processing depth image sequences.
• Demonstration of a multi-stage approach for gesture recognition, starting from raw image data to final gesture identification.
• Integration of various 3D imaging and image processing techniques within the program's architecture.

Conclusions:

• The developed C++ program effectively recognizes human gestures from depth camera data.
• The modular design allows for flexibility and potential expansion of gesture recognition capabilities.
• The study highlights the importance of combining 3D imaging with advanced image processing for robust gesture analysis.