Postgraduate Certificate in Applied Computer Vision and Image Processing
-- ViewingNowThe Postgraduate Certificate in Applied Computer Vision and Image Processing is a comprehensive course that equips learners with essential skills in image processing and computer vision. This certification program focuses on the importance of image analysis and understanding in various industries, including security, healthcare, autonomous vehicles, and robotics.
5,003+
Students enrolled
GBP £ 140
GBP £ 202
Save 44% with our special offer
ě´ ęłźě ě ëí´
100% ě¨ëźě¸
ě´ëěë íěľ
ęłľě ę°ëĽí ě¸ěŚě
LinkedIn íëĄíě ěśę°
ěëŁęšě§ 2ę°ě
죟 2-3ěę°
ě¸ě ë ěě
ë기 ę¸°ę° ěě
ęłźě ě¸ëśěŹí
⢠Introduction to Computer Vision & Image Processing: This unit will cover the fundamental concepts, principles, and techniques in computer vision and image processing. It will provide an overview of the field, including image acquisition, processing, analysis, and understanding.
⢠Image Fundamentals and Low-Level Processing: This unit will focus on image fundamentals and low-level processing techniques, including color spaces, histogram equalization, filtering, edge detection, and image enhancement.
⢠Feature Extraction and Description: This unit will cover feature extraction and description techniques, such as scale-invariant feature transform (SIFT), speeded-up robust features (SURF), histograms of oriented gradients (HOG), and others.
⢠Machine Learning for Computer Vision: This unit will provide an overview of machine learning techniques and algorithms used in computer vision, including support vector machines (SVM), decision trees, random forests, and neural networks.
⢠Object Detection and Recognition: This unit will focus on object detection and recognition techniques, including sliding window, convolutional neural networks (CNN), and region-based convolutional neural networks (R-CNN).
⢠Image Segmentation and Restoration: This unit will cover image segmentation and restoration techniques, including region growing, watershed, level set, and total variation minimization.
⢠3D Computer Vision and Structure from Motion: This unit will focus on 3D computer vision and structure from motion techniques, including stereo matching, multi-view geometry, and bundle adjustment.
⢠Deep Learning for Computer Vision: This unit will provide an overview of deep learning techniques used in computer vision, including CNN, recurrent neural networks (RNN), long short-term memory (LSTM), and autoencoders.
⢠Applications of Computer Vision and Image Processing: This unit will cover various applications of computer vision and image processing, including facial recognition, medical imaging, robotics, and augmented reality (AR).
ę˛˝ë Ľ 경ëĄ
ě í ěęą´
- 죟ě ě ëí 기본 ě´í´
- ěě´ ě¸ě´ ëĽěë
- ěť´í¨í° ë° ě¸í°ëˇ ě ꡟ
- 기본 ěť´í¨í° 기ě
- ęłźě ěëŁě ëí íě
ěŹě ęłľě ěę˛Šě´ íěíě§ ěěľëë¤. ě ꡟěąě ěí´ ě¤ęłë ęłźě .
ęłźě ěí
ě´ ęłźě ě ę˛˝ë Ľ ę°ë°ě ěí ě¤ěŠě ě¸ ě§ěęłź 기ě ě ě ęłľíŠëë¤. ꡸ę˛ě:
- ě¸ě ë°ě 기ę´ě ěí´ ě¸ěŚëě§ ěě
- ęśíě´ ěë 기ę´ě ěí´ ęˇě ëě§ ěě
- ęłľě ě겊ě ëł´ěě
ęłźě ě ěąęłľě ěźëĄ ěëŁí늴 ěëŁ ě¸ěŚě뼟 ë°ę˛ ëŠëë¤.
ě ěŹëë¤ě´ ę˛˝ë Ľě ěí´ ě°ëŚŹëĽź ě ííëę°
댏롰 ëĄëŠ ě¤...
ě죟 돝ë ě§ëŹ¸
ě˝ě¤ ěę°ëŁ
- 죟 3-4ěę°
- 쥰기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- 죟 2-3ěę°
- ě 기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- ě 체 ě˝ě¤ ě ꡟ
- ëě§í¸ ě¸ěŚě
- ě˝ě¤ ěëŁ
ęłźě ě ëł´ ë°ę¸°
íěŹëĄ ě§ëś
ě´ ęłźě ě ëšěŠě ě§ëśí기 ěí´ íěŹëĽź ěí ě˛ęľŹě뼟 ěě˛íě¸ě.
ě˛ęľŹěëĄ ę˛°ě ę˛˝ë Ľ ě¸ěŚě íë
