Help Bakit mahirap daw Computer Vision?any thoughts

[RIMURU_tempest300]

Hello po mga paps sabing ng prof ko adviser ng capstone ko mahirap daw computer vision kasi capstone title ko is "performance comparative analysis of facial recognition in various camera resolution" and diko alam kung anong algorithm gagamitin ko sa aking capstone sa pag recognize ng mga faces OpenCV ba or CNN any thoughts po sa mga experyensyado mga paps ty

 

[🤖]

Hello! Ang exciting ng topic ng capstone mo, at challenging din talaga ang field ng computer vision. Pagdating sa facial recognition at performance comparative analysis, maraming factors ang kailangan isaalang-alang tulad ng accuracy, speed, at resource requirements.

Sa pagpili ng algorithm, depende sa goals mo at available resources. Kung gusto mo ng detailed analysis ng performance at mas customizability, maaaring mag-focus sa traditional computer vision techniques gamit ang OpenCV. Maaari kang gumamit ng mga features extraction algorithms tulad ng Haar Cascades o Local Binary Patterns para sa facial recognition.

Sa kabilang banda, kung nais mo ng mas advanced at deep learning approach, maaari kang mag-explore ng Convolutional Neural Networks (CNN). Ang CNN ay kilala sa kanilang kakayahan na mag-extract ng features ng data at magturo ng mga patterns, na maaaring magamit sa facial recognition.

Kahit na mas advanced at mas complex ang CNN, maaaring mas mataas ang accuracy nito kaysa sa traditional computer vision techniques. Subalit, maaaring mas mahirap itong i-train at magrequire ng mas maraming computational resources.

Sa huli, it is important to weigh the pros and cons of each approach based on your project requirements, resources, and expertise. Maaaring maganda rin na magconsult sa iyong adviser at iba pang mga experts sa field ng computer vision para makapili ng tamang approach para sa iyong capstone project. Good luck sa iyong research at capstone!

 

[RIMURU_tempest300]

Hello! Ang exciting ng topic ng capstone mo, at challenging din talaga ang field ng computer vision. Pagdating sa facial recognition at performance comparative analysis, maraming factors ang kailangan isaalang-alang tulad ng accuracy, speed, at resource requirements.

Sa pagpili ng algorithm, depende sa goals mo at available resources. Kung gusto mo ng detailed analysis ng performance at mas customizability, maaaring mag-focus sa traditional computer vision techniques gamit ang OpenCV. Maaari kang gumamit ng mga features extraction algorithms tulad ng Haar Cascades o Local Binary Patterns para sa facial recognition.

Sa kabilang banda, kung nais mo ng mas advanced at deep learning approach, maaari kang mag-explore ng Convolutional Neural Networks (CNN). Ang CNN ay kilala sa kanilang kakayahan na mag-extract ng features ng data at magturo ng mga patterns, na maaaring magamit sa facial recognition.

Kahit na mas advanced at mas complex ang CNN, maaaring mas mataas ang accuracy nito kaysa sa traditional computer vision techniques. Subalit, maaaring mas mahirap itong i-train at magrequire ng mas maraming computational resources.

Sa huli, it is important to weigh the pros and cons of each approach based on your project requirements, resources, and expertise. Maaaring maganda rin na magconsult sa iyong adviser at iba pang mga experts sa field ng computer vision para makapili ng tamang approach para sa iyong capstone project. Good luck sa iyong research at capstone!

base on my description of my capstone project what is your recommended algorithm?

 

[🤖]

base on my description of my capstone project what is your recommended algorithm?

Based on the description of your capstone project, "performance comparative analysis of facial recognition in various camera resolution," where you aim to compare the performance of facial recognition across different camera resolutions, I would recommend using Convolutional Neural Networks (CNN) for your facial recognition algorithm.

CNNs are powerful deep learning algorithms that excel in tasks such as image recognition, including facial recognition. They are known for their ability to automatically learn features from data, which can be beneficial when dealing with variations in camera resolution. CNNs can adapt to different resolutions and extract relevant features for accurate facial recognition across various image qualities.

By leveraging CNNs, you can potentially achieve higher accuracy in facial recognition compared to traditional computer vision techniques like OpenCV. However, it is essential to consider the complexity of training CNN models and the computational resources required. You may need a sufficient amount of data for training and validation to ensure the CNN model performs well across different camera resolutions.

Overall, CNNs are a recommended algorithm for your capstone project due to their strong performance in image recognition tasks and adaptability to varying camera resolutions. Make sure to carefully design your CNN architecture, preprocess your data effectively, and fine-tune the model to achieve the desired performance in your comparative analysis of facial recognition. Good luck with your capstone project!