- Mahotas Tutorial
- Mahotas - Home
- Mahotas - Introduction
- Mahotas - Computer Vision
- Mahotas - History
- Mahotas - Features
- Mahotas - Installation
- Mahotas Handling Images
- Mahotas - Handling Images
- Mahotas - Loading an Image
- Mahotas - Loading Image as Grey
- Mahotas - Displaying an Image
- Mahotas - Displaying Shape of an Image
- Mahotas - Saving an Image
- Mahotas - Centre of Mass of an Image
- Mahotas - Convolution of Image
- Mahotas - Creating RGB Image
- Mahotas - Euler Number of an Image
- Mahotas - Fraction of Zeros in an Image
- Mahotas - Getting Image Moments
- Mahotas - Local Maxima in an Image
- Mahotas - Image Ellipse Axes
- Mahotas - Image Stretch RGB
- Mahotas Color-Space Conversion
- Mahotas - Color-Space Conversion
- Mahotas - RGB to Gray Conversion
- Mahotas - RGB to LAB Conversion
- Mahotas - RGB to Sepia
- Mahotas - RGB to XYZ Conversion
- Mahotas - XYZ to LAB Conversion
- Mahotas - XYZ to RGB Conversion
- Mahotas - Increase Gamma Correction
- Mahotas - Stretching Gamma Correction
- Mahotas Labeled Image Functions
- Mahotas - Labeled Image Functions
- Mahotas - Labeling Images
- Mahotas - Filtering Regions
- Mahotas - Border Pixels
- Mahotas - Morphological Operations
- Mahotas - Morphological Operators
- Mahotas - Finding Image Mean
- Mahotas - Cropping an Image
- Mahotas - Eccentricity of an Image
- Mahotas - Overlaying Image
- Mahotas - Roundness of Image
- Mahotas - Resizing an Image
- Mahotas - Histogram of Image
- Mahotas - Dilating an Image
- Mahotas - Eroding Image
- Mahotas - Watershed
- Mahotas - Opening Process on Image
- Mahotas - Closing Process on Image
- Mahotas - Closing Holes in an Image
- Mahotas - Conditional Dilating Image
- Mahotas - Conditional Eroding Image
- Mahotas - Conditional Watershed of Image
- Mahotas - Local Minima in Image
- Mahotas - Regional Maxima of Image
- Mahotas - Regional Minima of Image
- Mahotas - Advanced Concepts
- Mahotas - Image Thresholding
- Mahotas - Setting Threshold
- Mahotas - Soft Threshold
- Mahotas - Bernsen Local Thresholding
- Mahotas - Wavelet Transforms
- Making Image Wavelet Center
- Mahotas - Distance Transform
- Mahotas - Polygon Utilities
- Mahotas - Local Binary Patterns
- Threshold Adjacency Statistics
- Mahotas - Haralic Features
- Weight of Labeled Region
- Mahotas - Zernike Features
- Mahotas - Zernike Moments
- Mahotas - Rank Filter
- Mahotas - 2D Laplacian Filter
- Mahotas - Majority Filter
- Mahotas - Mean Filter
- Mahotas - Median Filter
- Mahotas - Otsu's Method
- Mahotas - Gaussian Filtering
- Mahotas - Hit & Miss Transform
- Mahotas - Labeled Max Array
- Mahotas - Mean Value of Image
- Mahotas - SURF Dense Points
- Mahotas - SURF Integral
- Mahotas - Haar Transform
- Highlighting Image Maxima
- Computing Linear Binary Patterns
- Getting Border of Labels
- Reversing Haar Transform
- Riddler-Calvard Method
- Sizes of Labelled Region
- Mahotas - Template Matching
- Speeded-Up Robust Features
- Removing Bordered Labelled
- Mahotas - Daubechies Wavelet
- Mahotas - Sobel Edge Detection
Mahotas - Computer Vision
Computer vision, a subfield of artificial intelligence and computer science, focuses on enabling computers to gain a high−level understanding of visual information from images or videos.
- Inspired by human visual perception, computer vision aims to replicate and surpass human visual capabilities using algorithms, machine learning, and deep learning techniques.
- Computer vision is an interdisciplinary approach that uses computer science, engineering, mathematics, and databases to make a machine understand the visual data over the years.
- The availability of large datasets, deep learning, and training machine learning models made computer vision more functional.
- One such open−source computer vision and image processing library is mahotas.
Mahotas is a computer vision library containing image processing operations such as filtering, morphological operations and classification as well as other modern computer vision functions.
In this tutorial, we will explore the fundamental concepts, key techniques, and real−world applications of computer vision.
Understanding Computer Vision
Computer vision involves developing algorithms and models that allow computers to interpret and understand visual data. It encompasses a wide range of tasks, including image classification, object detection and tracking, image segmentation, facial recognition, scene understanding, and 3D reconstruction.
The ultimate goal is to enable computers to extract meaningful information from visual data and make intelligent decisions based on that information.
Foundations of Computer Vision
Computer Vision draws its inspiration from the human visual system, aiming to replicate and even surpass human visual perception in certain tasks. The field finds its roots in the 1960s when researchers began exploring techniques for image recognition and pattern detection.
Early approaches focused on handcrafted features and rule−based systems, but with the advent of machine learning and deep learning, computer vision experienced a revolutionary shift.
Image Representation− The cornerstone of computer vision lies in how visualinformation is represented and processed. Pixels in images are transformed into numerical data, which can be analyzed and interpreted by algorithms.
Feature Extraction− In image analysis, feature extraction plays a vital role in identifying relevant patterns and structures. Early methods included edge detection and corner detection, while modern approaches utilize deep learning to learn abstract features.
Machine Learning and Deep Learning− Machine learning algorithms, particularly deep learning neural networks, have been instrumental in the rapid progress of computer vision. Convolutional Neural Networks (CNNs) have achieved remarkable success in tasks like image classification, object detection, and segmentation.
Applications of Computer Vision
The applications of computer vision are diverse and continually expanding as technology advances. Here are some key areas where computer vision has made a significant impact −
Image Classification− Computer vision enables machines to classify objects and scenes in images with remarkable accuracy. From identifying everyday objects to recognizing specific species in nature, image classification has a wide range of applications.
Object Detection− Object detection goes beyond classification by not only recognizing objects but also localizing them within the image. It is crucial in tasks such as surveillance, autonomous vehicles, and augmented reality.
Image Segmentation− Image segmentation involves dividing an image into meaningful regions, facilitating further analysis and understanding. It is utilized in medical imaging, scene understanding, and video processing.
Facial Recognition− Facial recognition technology has numerous applications, including biometric authentication, surveillance, and social media tagging.
Optical Character Recognition (OCR)− OCR enables machines to recognize and convert printed or handwritten text in images into editable and searchable digital formats. It is widely used in document digitization and automation.
Challenges in Computer Vision
While computer vision has made tremendous strides, it still faces several challenges, some of which are −
Limited Data− Deep learning models thrive on vast amounts of labeled data, and obtaining annotated datasets for every application can be cumbersome and expensive.
Interpretability− Deep learning models are often considered "black boxes," making it difficult to understand how they arrive at their decisions, which can be crucial in critical applications like healthcare and security.
Robustness− Computer vision algorithms must be robust to variations in lighting conditions, viewpoints, and occlusions to perform reliably in real−world scenarios.