Object localization refers to identifying the position of an object within an image. Before detecting multiple objects, we first need to learn how to locate a single object correctly.

### Difference Between Classification and Localization ###

Image classification assigns a single label to an entire image, while localization identifies both the object and its position using a bounding box. Classification tells us **what** is in the image, whereas localization tells us **where** it is.

### Understanding Bounding Boxes

Bounding boxes are rectangular boxes drawn around objects in an image to define their position. These boxes are used as reference points for object detection models.

**Coordinate Representation**
- **(x, y, width, height)**: defines the top-left corner and the dimensions of the box.



### Challenges in Localization

**Object localization faces several challenges**:

- **Scale variations**: objects may appear larger or smaller depending on their distance from the camera.

- **Occlusion**: objects may be partially hidden behind other elements in the image.

- **Background clutter**: complex backgrounds can make object localization difficult.

- **Different aspect ratios**: objects of various shapes may not fit standard bounding boxes well.



Understanding these fundamental concepts is essential before moving on to more complex object detection techniques.

What is the primary difference between image classification and object localization?

Which of the following is NOT a common challenge in object localization?

This course provides a comprehensive introduction to Computer Vision, covering both fundamental and advanced topics. You'll learn how machines process, analyze, and interpret visual data through various techniques such as image processing, feature extraction, object detection, and deep learning models.

Computer Vision enables machines to interpret and analyze visual data, mimicking human perception. This section covers the basics of image representation, color models, and mathematical foundations essential for understanding how computers process images. You'll explore real-world applications, from autonomous vehicles to medical imaging, and learn how Computer Vision integrates with AI and machine learning. 

OpenCV is a powerful library for image manipulation and computer vision tasks. This section covers essential techniques like image filtering, transformations, edge detection, and segmentation. You'll learn how to perform blurring, thresholding, contour detection, and feature extraction to enhance and analyze images efficiently.

CNNs process visual data using convolution, pooling, and activation layers to extract features for tasks like image classification and object detection. Key components include padding, convolution for feature extraction, pooling for complexity reduction, and activation for non-linearity. Popular architectures like AlexNet, VGG, and ResNet power AI in healthcare, autonomy, and security.

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Unlike image classification, which assigns a single label to an entire image, object detection not only classifies objects but also determines their positions using bounding boxes. This section covers key techniques and algorithms used in object detection, ranging from traditional methods to deep learning-based approaches like YOLO and U-Net.

Computer Vision Course Outline

Object Localization

Difference Between Classification and Localization

Understanding Bounding Boxes

Challenges in Localization

1. What is the primary difference between image classification and object localization?

2. Which of the following is NOT a common challenge in object localization?