CREATIVE IMAGE OPTIMIZATION

Computer Vision for Creative Optimisation: KPI maximisation through image analysis

This challenge utilizes Computer Vision for Creative Optimization and KPI maximization through image analysis. Computer vision technology has transformed the world by allowing machines to achieve human-level understanding of images and videos. The success of deep learning-based computer vision has led to a number of novel applications such as Autonomous driving for cars and tumor detection.

Computer vision

Computer vision (CV) is a field of artificial intelligence that enables computers to extract information from images, videos, and other visual sources. computer vision, computer scientists extract knowledge from an image by manipulating it through image transforms. In the mathematical language of image algebra an image transformation often corresponds to an image-template product. When performing this operation on a computer, savings in time and memory as well as a better fit to the specific computer architecture can often be achieved by using the technique of template decomposition.

Deep learning

Deep learning (DL) is a machine learning method based on artificial neural networks (ANN). Deep learning involves training artificial neural networks on large datasets. These networks consist of many layers of information processing units (neurons) that are loosely inspired by the way the brain works.

Each neuron performs its own simple operation on input from other units and sends its output to other units in subsequent layers until we get an output layer with predicted values. Deep neural networks can have many parameters (more than 10 million in some cases), which allows them to learn complex, non-linear relationships between inputs and outputs.

There are several types of NNs:

• convolutional (CNN)
• recurrent (RNN)
• generative adversarial (GAN)
• recursive

How is deep learning applied to computer vision tasks?

So how exactly is DL used in computer vision? With the help of convolutional neural networks, deep learning is able to perform the following tasks:

• object recognition
• face recognition
• motion detection
• pose estimation
• semantic segmentation

some of tools will be used for image segmention such as OpenCV(YoloV7) and Semantic Segmentation.

Installation guide

git clone https://github.com/Hu-Hesheng/creative_image_optimization
cd creative_image_optimization
pip install -r requirements.txt

Deployment

streamlit https://amanuel3065-creative-image-optimization-app-yu2h7h.streamlit.app/

Contributors

• Natnael Masresha
• Amanuel Zewdu
• Yohans Samuel
• Bekalu Tadele
• Andenet Alexander
• Genet Shanko
• Wangui Gachugu