Key Steps in Image Preprocessing
- Image Resizing:
- Resizing the image to a new window size to meet the input dimensions expected by the detection model.
- Assists in decoding the data into a standard format, thus reducing the computational burden.
- Normalization:
- Normalizing pixel values by transforming the pixel intensities of digital images to a desired range of values, commonly [0, 1] or [-1, 1].
- Ensures consistent architecture and aids in the training process of the models.
- Noise Reduction:
- Eliminating unwanted background noise in an image using filters like Gaussian, median, or bilateral filters.
- Improves image brightness, enabling more efficient feature extraction.
- Contrast Adjustment:
- Adjusting contrast to enhance essential characteristics.
- Techniques include histogram equalization and contrast-limited adaptive histogram equalization (CLAHE).
- Color Space Conversion:
- Applying mathematical and geometrical transformations for object deformation in images, such as scaling, rotation, or translation.
- Beneficial in tasks focused on color differentiation and segmentation.
- Image Augmentation:
- Generating images through variations to enhance the diversity of the training data set.
- Operations include rotation, scale change, mirror reflection, displacement, and adding random noise.
- Edge Detection:
- Processing the contour of subjects in the image and defining the area containing objects.
- Methods include Canny, Sobel, and Laplacian edge detection.
- Thresholding:
- Binarizing images to divide them into segments where the mean of the pixel intensity is calculated.
- Effective for segmenting objects and separating them from the background.
- Blurring and Sharpening:
- Smoothing and denoising to minimize contrast and enhance sharpness along object boundaries.
- Brightening to increase contrast and exposure of details on edges and necessary portions of images.
- Morphological Operations:
- Using operations such as dilation, erosion, opening, and closing to alter the size and shape of objects and eliminate small unwanted noise.
- Enhances object boundary information by post-processing the reconstructed volume.
Introduction to Object Detection Using Image Processing
Object detection is a crucial task in computer vision that involves identifying and locating objects within an image or video. This task is fundamental for various applications, including autonomous driving, video surveillance, and medical imaging. This article delves into the techniques and methodologies used in object detection, focusing on image processing approaches.
Table of Content
- Understanding Object Detection?
- Key Steps in Image Preprocessing
- Techniques in Object Detection Using Image Processing
- 1. Traditional Image Processing Techniques
- 2. Neural Network-Based Techniques
- Image Preprocessing Using OpenCV
- Applications of Object Detection
- Challenges in Object Detection