Image Processing: Difference between revisions

Latest revision as of 09:38, 6 July 2025

Image Processing is a method of manipulating digital images through the use of algorithms and software techniques. The primary goal of image processing is to enhance the quality of an image or to extract useful information from it. This field encompasses a range of techniques that can be applied in various domains, including computer vision, medical imaging, remote sensing, and more. As technology progresses, image processing techniques are becoming increasingly sophisticated and widely applied in industries such as entertainment, healthcare, and security.

Background or History

The origins of image processing can be traced back to the early 1960s when researchers began to experiment with the manipulation and analysis of images through computers. Early methods focused primarily on simple geometric transformations and basic enhancements. The introduction of the digital computer played a significant role in the evolution of this field, allowing for more complex algorithms to be developed.

1960s to 1970s

During the 1960s, pioneers like Lawrence Roberts and others began developing mathematical models for image analysis, laying the groundwork for future advancements in image processing. At this time, systems were primarily used in research laboratories and were characterized by their limited capabilities.

The 1970s saw the advent of more sophisticated techniques, including edge detection, filtering, and pattern recognition. Researchers such as David Marr were instrumental in formulating theories about the human visual system, which influenced the development of algorithms for image processing. Notably, the development of computer graphics also bolstered the field, providing new avenues for visual representation.

1980s to 1990s

The 1980s marked a significant shift in image processing with the introduction of digital cameras and the increased availability of digital image data. This era also saw the emergence of specialized software such as Adobe Photoshop, which put image processing tools into the hands of artists and general users. Algorithms that utilized techniques like Fourier transforms became commonplace, facilitating advancements in frequency domain analysis.

By the 1990s, the field matured, leveraging the rise of computers and digital imaging technologies. The introduction of new image formats and standards allowed for greater interoperability and quality in image sharing. Image processing began being integrated into various applications including telecommunications, medical imaging, and automated inspection systems.

2000s and Beyond

With the advent of the 21st century, image processing entered a new era characterized by the explosion of digital content and the availability of powerful computational resources. Machine learning and artificial intelligence began to revolutionize image processing, enabling sophisticated applications such as facial recognition and image classification. Technologies like deep learning and convolutional neural networks (CNNs) paved the way for remarkable advancements in automated image analysis.

In contemporary use, image processing techniques are an integral part of smartphone applications, social media platforms, and autonomous systems. The integration of image processing with machine learning continues to expand its capabilities, leading to new developments in augmented reality, 3D imaging, and beyond.

Architecture and Design

Image processing systems primarily consist of hardware and software components that work together to perform various tasks. The architecture often varies based on the specific application and the complexity of the processing tasks.

Hardware Components

The hardware utilized in image processing includes processors, cameras, sensors, and memory storage systems. High-performance processors, such as Graphics Processing Units (GPUs), are common in modern image processing applications due to their ability to handle parallel processing efficiently. Additionally, specialized hardware like Field Programmable Gate Arrays (FPGAs) may be used for real-time image processing tasks.

Software Components

At the software level, image processing relies on a variety of programming languages and libraries. Languages such as Python, C++, and MATLAB are frequently utilized because of their robust libraries and frameworks like OpenCV and TensorFlow, which provide tools to implement complex algorithms. Image processing software typically includes functionalities like image filtering, segmentation, feature extraction, and compression techniques.

Algorithms and Techniques

Central to image processing are various algorithms that are used to enhance, modify, and analyze images. Common techniques include:

**Image enhancement**: Techniques such as histogram equalization and contrast stretching improve the visual quality of an image.
**Image restoration**: Algorithms that remove noise or correct features for a clearer representation of the original scene, such as deblurring techniques.
**Image segmentation**: Methods that partition an image into sections or objects, which can facilitate later analysis and understanding.
**Feature extraction**: Techniques that identify and isolate significant features or patterns within an image, often used in machine learning tasks.

These algorithms serve as the backbone of image processing applications and can be combined in various ways to achieve desired outcomes.

Implementation and Applications

Image processing has a vast range of applications across different fields. Below are some prominent areas where image processing techniques are effectively utilized.

Medical Imaging

In the field of medicine, image processing plays a crucial role in enhancing diagnostic capabilities. Techniques are employed in MRI (Magnetic Resonance Imaging), CT (Computed Tomography), and ultrasound imaging to improve image clarity and assist in disease detection. Advanced algorithms are utilized to analyze tumor shapes, detect anomalies, and monitor changes in patient conditions over time.

Remote Sensing

Remote sensing leverages image processing to extract information about the Earth and its environment. Satellites and drones capture vast amounts of data, which undergo processing to analyze land use, vegetation health, and environmental changes. Image classification algorithms facilitate the monitoring of natural resources, disaster management, and climate change assessments by transforming raw data into actionable information.

Security and Surveillance

The security industry has increasingly adopted image processing technologies for purposes such as surveillance, facial recognition, and license plate recognition. Automated systems analyze real-time footage, aiding in the identification of individuals and monitoring of environments for security threats. Furthermore, image processing contributes to the enhancement of video quality and detail extraction, making surveillance systems more effective.

Entertainment and Media

In the entertainment industry, image processing is essential in visual effects, computer-generated imagery (CGI), and animation. Films and video games leverage these techniques to create immersive experiences, often requiring complex image manipulation for realistic visuals. Additionally, platforms for photo editing and enhancement allow users to apply various filters and modifications to images, enhancing user creativity and expression.

Automotive Industry

Modern vehicles increasingly incorporate image processing for features such as lane detection, obstacle recognition, and parking assistance. Cameras and sensors capture environmental data that algorithms process to provide feedback to drivers or for autonomous driving systems. As automotive technology evolves, the reliance on sophisticated image processing techniques continues to grow.

Research and Development

Ongoing research in image processing explores new methods for improving image quality, data extraction, and usability across applications. Fields such as artificial intelligence, computer vision, and robotics heavily rely on advancements in image processing for developing innovative technologies. Researchers work on optimizing algorithms for real-time processing, expanding the boundaries of what image processing can achieve.

Real-world Examples

To illustrate the impact of image processing, several real-world applications and case studies are discussed below.

Medical Example

In oncology, advanced image processing techniques are employed to analyze MRI scans of patients suffering from cancer. By enhancing the contrast of tumor areas and applying segmentation algorithms, radiologists can more accurately identify and measure tumor growth. These methods have been shown to improve diagnosis and treatment planning for patients, significantly enhancing clinical outcomes.

Remote Sensing Example

A notable application of image processing in remote sensing involves monitoring deforestation rates in the Amazon Rainforest. Satellite images are processed using classification techniques to identify changes in land cover and assess the impact of logging activities. By utilizing image processing, researchers can provide critical insights into environmental changes and support policies aimed at conservation.

Surveillance Example

Facial recognition technology has been implemented by law enforcement agencies in various countries for crime detection and prevention. Image processing algorithms analyze video footage from public spaces to identify individuals from databases. This application enhances public safety while raising important ethical considerations regarding privacy and data use.

Automotive Example

The implementation of autonomous vehicles relies heavily on image processing technologies to function safely. Systems utilize cameras and sensors to capture real-time data about surroundings and road conditions. Image processing algorithms analyze this data, allowing vehicles to navigate, avoid obstacles, and follow traffic rules. This development represents a significant advancement in transportation technology, with ongoing research focused on improving the robustness of these systems.

Criticism or Limitations

Despite its advancements and applications, image processing is not without criticism and limitations. Variations in image quality, data loss, and ethical concerns present ongoing challenges.

Image Quality and Data Loss

Variabilities in lighting conditions, noise, and sensor quality can significantly impact the efficacy of image processing results. In medical imaging, for instance, poor image quality can hinder diagnosis and mislead clinical decisions. Additionally, data loss during compression or transmission may compromise the integrity of the information extracted from images.

Ethical Concerns

The use of image processing technologies, particularly in surveillance and facial recognition, raises important ethical questions surrounding privacy, consent, and bias. Concerns regarding the potential for misuse of images and data have prompted discussions on the need for regulations governing the implementation of such technologies. Furthermore, biases present in training data can lead to inequalities when algorithms are applied in real-world scenarios.

Dependence on Technology

The increasing reliance on automated image processing systems may diminish the role of human oversight in critical applications such as medicine and security. While algorithms can enhance efficiency, they may also introduce new types of errors that are not immediately apparent. The balance between automation and human involvement remains a key consideration.

Processing Speed and Real-time Limitations

Many image processing applications require real-time processing capabilities, which poses a challenge regarding computational power and efficiency. As imaging data continues to grow in size and complexity, the demand for faster algorithms and processing units gains significance. Improvements in hardware and optimization of algorithms are critical for meeting the needs of real-time applications.

References

@@ Line 1: / Line 1: @@
-== Image Processing ==
+'''Image Processing''' is a method of manipulating digital images through the use of algorithms and software techniques. The primary goal of image processing is to enhance the quality of an image or to extract useful information from it. This field encompasses a range of techniques that can be applied in various domains, including computer vision, medical imaging, remote sensing, and more. As technology progresses, image processing techniques are becoming increasingly sophisticated and widely applied in industries such as entertainment, healthcare, and security.
-Image processing is a method of manipulating and analyzing images in order to enhance their quality, extract meaningful information, or convert them into a more useful format. This field combines concepts and techniques from both computer science and electrical engineering to handle various tasks related to images, such as resizing, filtering, noise removal, sharpening, and recognition. Due to its vast range of applications, image processing has become a critical component in numerous domains including medical imaging, satellite imaging, security and surveillance, and more.
 == Background or History ==
+The origins of image processing can be traced back to the early 1960s when researchers began to experiment with the manipulation and analysis of images through computers. Early methods focused primarily on simple geometric transformations and basic enhancements. The introduction of the digital computer played a significant role in the evolution of this field, allowing for more complex algorithms to be developed.
-The evolution of image processing can be traced back to the early 1960s when the first digital images and processing techniques were developed. The initial focus was primarily on simple tasks such as image enhancement and basic feature extraction. Over the decades, technological advancements and the introduction of high-performance computing have significantly broadened the horizon of image processing techniques.
+=== 1960s to 1970s ===
+During the 1960s, pioneers like Lawrence Roberts and others began developing mathematical models for image analysis, laying the groundwork for future advancements in image processing. At this time, systems were primarily used in research laboratories and were characterized by their limited capabilities.
-In the 1970s and 1980s, researchers began to explore more sophisticated algorithms and methods for image analysis. The implementation of techniques such as Fourier transforms enabled the manipulation of image data in the frequency domain, allowing for more advanced filtering and noise reduction strategies. Moreover, the introduction of digital cameras and imaging technology revolutionized the way images were captured and processed, leading to a surge in research and development in this field.
-The 1990s saw the rise of applications in various industries, including medical imaging where image processing techniques contributed significantly to the advancement of diagnostic tools such as MRI and CT scans. The development of machine learning algorithms and artificial intelligence in the 21st century has further propelled the field forward, enabling more complex tasks such as object detection, image classification, and even image generation.
-== Fundamental Concepts ==
+The 1970s saw the advent of more sophisticated techniques, including edge detection, filtering, and pattern recognition. Researchers such as David Marr were instrumental in formulating theories about the human visual system, which influenced the development of algorithms for image processing. Notably, the development of computer graphics also bolstered the field, providing new avenues for visual representation.
-=== Types of Image Processing ===
+=== 1980s to 1990s ===
+The 1980s marked a significant shift in image processing with the introduction of digital cameras and the increased availability of digital image data. This era also saw the emergence of specialized software such as Adobe Photoshop, which put image processing tools into the hands of artists and general users. Algorithms that utilized techniques like Fourier transforms became commonplace, facilitating advancements in frequency domain analysis.
-Image processing can be broadly categorized into two main types: analog image processing and digital image processing.
+By the 1990s, the field matured, leveraging the rise of computers and digital imaging technologies. The introduction of new image formats and standards allowed for greater interoperability and quality in image sharing. Image processing began being integrated into various applications including telecommunications, medical imaging, and automated inspection systems.
-'''Analog image processing''' involves manipulating images directly in the analog domain using optical tools and techniques such as filters and lenses. This approach is more traditional and typically used in film photography or in scenarios where analog systems are preferred.
+=== 2000s and Beyond ===
+With the advent of the 21st century, image processing entered a new era characterized by the explosion of digital content and the availability of powerful computational resources. Machine learning and artificial intelligence began to revolutionize image processing, enabling sophisticated applications such as facial recognition and image classification. Technologies like deep learning and convolutional neural networks (CNNs) paved the way for remarkable advancements in automated image analysis.
-'''Digital image processing,''' on the other hand, involves the manipulation of images through numerical computations facilitated by digital computers. This modern approach allows for more complex processing tasks, faster execution times, and greater flexibility in handling various types of images, including still images, videos, and 3D images.
+In contemporary use, image processing techniques are an integral part of smartphone applications, social media platforms, and autonomous systems. The integration of image processing with machine learning continues to expand its capabilities, leading to new developments in augmented reality, 3D imaging, and beyond.
-=== Key Techniques ===
+== Architecture and Design ==
+Image processing systems primarily consist of hardware and software components that work together to perform various tasks. The architecture often varies based on the specific application and the complexity of the processing tasks.
-Several fundamental techniques exist within the realm of image processing that serve different purposes. Some of the most significant techniques include:
+=== Hardware Components ===
-* '''Image Enhancement:''' This technique aims to improve the visual appearance of an image through methods such as contrast stretching, histogram equalization, and sharpening techniques. Enhanced images provide better insights and interpretations for viewers.
+The hardware utilized in image processing includes processors, cameras, sensors, and memory storage systems. High-performance processors, such as Graphics Processing Units (GPUs), are common in modern image processing applications due to their ability to handle parallel processing efficiently. Additionally, specialized hardware like Field Programmable Gate Arrays (FPGAs) may be used for real-time image processing tasks.
-* '''Image Restoration:''' This involves recovering an image that has been degraded by various factors such as noise or motion blur. Techniques such as Wiener filtering and various deblurring algorithms are frequently employed in this area.
-* '''Image Segmentation:''' A crucial task within image analysis, segmentation involves partitioning an image into meaningful components or regions. This can be accomplished using methods such as edge detection, thresholding, or region growing.
-* '''Feature Extraction:''' Following segmentation, feature extraction aims to identify and extract relevant features or patterns from an image. Techniques such as blob analysis, corner detection, and histogram features are commonly utilized.
-* '''Image Compression:''' Reducing the size of an image file is essential for efficient storage and transmission. Lossy and lossless compression techniques like JPEG and PNG are widely used to optimize image sizes while preserving as much quality as possible.
-=== Image Representation ===
+=== Software Components ===
+At the software level, image processing relies on a variety of programming languages and libraries. Languages such as Python, C++, and MATLAB are frequently utilized because of their robust libraries and frameworks like OpenCV and TensorFlow, which provide tools to implement complex algorithms. Image processing software typically includes functionalities like image filtering, segmentation, feature extraction, and compression techniques.
-Image representation is a critical concept in image processing. Images are typically represented in discrete digital formats characterized by pixels. Each pixel can have one or more values depending on the color model used, such as grayscale, RGB (Red, Green, Blue), or other color spaces like CMYK (Cyan, Magenta, Yellow, Key/Black).
+=== Algorithms and Techniques ===
+Central to image processing are various algorithms that are used to enhance, modify, and analyze images. Common techniques include:
+* **Image enhancement**: Techniques such as histogram equalization and contrast stretching improve the visual quality of an image.
+* **Image restoration**: Algorithms that remove noise or correct features for a clearer representation of the original scene, such as deblurring techniques.
+* **Image segmentation**: Methods that partition an image into sections or objects, which can facilitate later analysis and understanding.
+* **Feature extraction**: Techniques that identify and isolate significant features or patterns within an image, often used in machine learning tasks.
-The resolution of an image, often defined by the number of pixels in width and height, determines the level of detail that can be captured. Additionally, the bit depth of each pixel, which indicates how many bits are used to represent the pixel value, affects the color precision and the overall quality of the image.
+These algorithms serve as the backbone of image processing applications and can be combined in various ways to achieve desired outcomes.
-== Implementation or Applications ==
+== Implementation and Applications ==
+Image processing has a vast range of applications across different fields. Below are some prominent areas where image processing techniques are effectively utilized.
-Image processing has a plethora of applications across various domains, each utilizing different techniques to achieve specific goals. Below are some notable areas where image processing plays a crucial role.
 === Medical Imaging ===
+In the field of medicine, image processing plays a crucial role in enhancing diagnostic capabilities. Techniques are employed in MRI (Magnetic Resonance Imaging), CT (Computed Tomography), and ultrasound imaging to improve image clarity and assist in disease detection. Advanced algorithms are utilized to analyze tumor shapes, detect anomalies, and monitor changes in patient conditions over time.
-In the field of medicine, image processing is integral in enhancing diagnostic tools. Technologies like MRI, CT, and ultrasound imaging rely heavily on sophisticated algorithms for interpreting the information and producing high-quality images. Image processing aids in visualizing complex structures, identifying abnormalities, and facilitating surgical planning.
+=== Remote Sensing ===
+Remote sensing leverages image processing to extract information about the Earth and its environment. Satellites and drones capture vast amounts of data, which undergo processing to analyze land use, vegetation health, and environmental changes. Image classification algorithms facilitate the monitoring of natural resources, disaster management, and climate change assessments by transforming raw data into actionable information.
-In addition, real-time processing techniques are employed during minimal invasive surgeries where live imaging is crucial. Automated systems can analyze images on-the-fly, assisting surgeons in making informed decisions during procedures.
-=== Surveillance and Security ===
-The implementation of image processing in surveillance systems has transformed security protocols across various sectors. Techniques such as facial recognition, motion detection, and anomaly detection are applied to analyze video feeds for enhanced monitoring and threat identification.
-Image processing algorithms help recognize patterns and identify potentially harmful activities, allowing for prompt responses to security breaches. From urban surveillance to corporate settings, image processing systems have become essential in ensuring safety and security.
-=== Satellite and Aerial Imaging ===
-In the field of remote sensing, satellite and aerial images undergo extensive processing to extract relevant data for environmental monitoring, urban planning, and disaster management. Image analysis techniques assist in land classification, vegetation mapping, and change detection over time.
-The integration of advanced image processing techniques enables researchers and policymakers to make-informed decisions based on accurate geographic information, which is essential for managing natural resources and responding to environmental changes.
+=== Security and Surveillance ===
+The security industry has increasingly adopted image processing technologies for purposes such as surveillance, facial recognition, and license plate recognition. Automated systems analyze real-time footage, aiding in the identification of individuals and monitoring of environments for security threats. Furthermore, image processing contributes to the enhancement of video quality and detail extraction, making surveillance systems more effective.
 === Entertainment and Media ===
+In the entertainment industry, image processing is essential in visual effects, computer-generated imagery (CGI), and animation. Films and video games leverage these techniques to create immersive experiences, often requiring complex image manipulation for realistic visuals. Additionally, platforms for photo editing and enhancement allow users to apply various filters and modifications to images, enhancing user creativity and expression.
-The entertainment industry utilizes image processing extensively for visual effects, video editing, and animation. Techniques such as image stabilization, color correction, and special effects all contribute to enhancing the viewer's experience.
+=== Automotive Industry ===
+Modern vehicles increasingly incorporate image processing for features such as lane detection, obstacle recognition, and parking assistance. Cameras and sensors capture environmental data that algorithms process to provide feedback to drivers or for autonomous driving systems. As automotive technology evolves, the reliance on sophisticated image processing techniques continues to grow.
-Furthermore, advances in real-time image processing allow for interactive applications in video games and augmented reality. Concepts such as object recognition and 3D rendering rely on processing algorithms to create immersive experiences.
+=== Research and Development ===
+Ongoing research in image processing explores new methods for improving image quality, data extraction, and usability across applications. Fields such as artificial intelligence, computer vision, and robotics heavily rely on advancements in image processing for developing innovative technologies. Researchers work on optimizing algorithms for real-time processing, expanding the boundaries of what image processing can achieve.
-=== Robotics and Autonomous Systems ===
+== Real-world Examples ==
+To illustrate the impact of image processing, several real-world applications and case studies are discussed below.
-Autonomous systems, including self-driving cars and drones, leverage image processing for navigation and obstacle detection. These machines utilize techniques such as computer vision to interpret visual input from cameras and sensors, allowing them to make real-time decisions based on their surroundings.
+=== Medical Example ===
+In oncology, advanced image processing techniques are employed to analyze MRI scans of patients suffering from cancer. By enhancing the contrast of tumor areas and applying segmentation algorithms, radiologists can more accurately identify and measure tumor growth. These methods have been shown to improve diagnosis and treatment planning for patients, significantly enhancing clinical outcomes.
-Advanced algorithms are employed to analyze data from multiple sensors simultaneously, enhancing the system's ability to understand complex environments and ensure safe operations.
-== Real-world Examples ==
-Real-world implementations of image processing illustrate the technology's versatility and critical importance in various fields. One prominent example is the use of image processing in the automotive industry for advanced driver-assistance systems (ADAS). These systems employ cameras and sensors to monitor the environment around a vehicle, offering features such as lane departure warnings, adaptive cruise control, and automatic emergency braking.
+=== Remote Sensing Example ===
+A notable application of image processing in remote sensing involves monitoring deforestation rates in the Amazon Rainforest. Satellite images are processed using classification techniques to identify changes in land cover and assess the impact of logging activities. By utilizing image processing, researchers can provide critical insights into environmental changes and support policies aimed at conservation.
-Another example is in the realm of social media where image processing powers features like automatic tagging and filtering. Platforms utilize algorithms to identify faces within images, suggesting tags to users based on their contacts. Similarly, image enhancement tools allow users to apply filters and adjustments to their photos instantly.
+=== Surveillance Example ===
+Facial recognition technology has been implemented by law enforcement agencies in various countries for crime detection and prevention. Image processing algorithms analyze video footage from public spaces to identify individuals from databases. This application enhances public safety while raising important ethical considerations regarding privacy and data use.
-Furthermore, in the realm of agriculture, drone technology combined with image processing enables precision farming. Farmers can use aerial images to monitor crops, assess plant health, and optimize resource distribution, ultimately leading to improved yields and reduced costs.
+=== Automotive Example ===
+The implementation of autonomous vehicles relies heavily on image processing technologies to function safely. Systems utilize cameras and sensors to capture real-time data about surroundings and road conditions. Image processing algorithms analyze this data, allowing vehicles to navigate, avoid obstacles, and follow traffic rules. This development represents a significant advancement in transportation technology, with ongoing research focused on improving the robustness of these systems.
 == Criticism or Limitations ==
+Despite its advancements and applications, image processing is not without criticism and limitations. Variations in image quality, data loss, and ethical concerns present ongoing challenges.
-Despite the advancements in image processing, there are inherent limitations and criticisms associated with this field. One significant concern is the ethical implications of surveillance systems that leverage image processing techniques, particularly regarding privacy and consent. The use of facial recognition technology has sparked debates and raised questions about potential misuse and societal impacts.
+=== Image Quality and Data Loss ===
+Variabilities in lighting conditions, noise, and sensor quality can significantly impact the efficacy of image processing results. In medical imaging, for instance, poor image quality can hinder diagnosis and mislead clinical decisions. Additionally, data loss during compression or transmission may compromise the integrity of the information extracted from images.
-Another limitation is the challenge of image quality and data integrity. Images captured in suboptimal conditions may not yield accurate results even with sophisticated processing algorithms. Issues related to noise, distortion, and resolution can significantly affect the reliability of image analyses.
+=== Ethical Concerns ===
+The use of image processing technologies, particularly in surveillance and facial recognition, raises important ethical questions surrounding privacy, consent, and bias. Concerns regarding the potential for misuse of images and data have prompted discussions on the need for regulations governing the implementation of such technologies. Furthermore, biases present in training data can lead to inequalities when algorithms are applied in real-world scenarios.
-The computational demands of advanced image processing techniques also pose a challenge. Real-time processing requires robust hardware and optimized algorithms to maintain performance, which can be a barrier for smaller organizations or applications with limited resources.
+=== Dependence on Technology ===
+The increasing reliance on automated image processing systems may diminish the role of human oversight in critical applications such as medicine and security. While algorithms can enhance efficiency, they may also introduce new types of errors that are not immediately apparent. The balance between automation and human involvement remains a key consideration.
-Finally, the dependence on large datasets for training machine learning algorithms in image processing can introduce biases and inaccuracies, perpetuating existing inequalities and challenges in automated recognition systems.
+=== Processing Speed and Real-time Limitations ===
+Many image processing applications require real-time processing capabilities, which poses a challenge regarding computational power and efficiency. As imaging data continues to grow in size and complexity, the demand for faster algorithms and processing units gains significance. Improvements in hardware and optimization of algorithms are critical for meeting the needs of real-time applications.
 == See also ==
 * [[Computer Vision]]
 * [[Machine Learning]]
-* [[Pattern Recognition]]
+* [[Digital Image Processing]]
 * [[Optical Character Recognition]]
-* [[Color Spaces]]
+* [[Computer Graphics]]
-* [[Feature Extraction]]
+* [[Image Analysis]]
-* [[Medical Imaging]]
 == References ==
-* [https://www.tutorialspoint.com/dip/index.htm Digital Image Processing Tutorial]
+* [https://www.imageprocessing.org/ International Society for Image Processing]
-* [https://www.mathworks.com/solutions/image-processing.html Image Processing with MATLAB]
+* [https://www.computer.org/ Institute of Electrical and Electronics Engineers (IEEE)]
-* [https://opencv.org OpenCV: Open Source Computer Vision Library]
+* [https://www.opencv.org/ OpenCV – Open Source Computer Vision Library]
-* [https://www.imageprocessingplace.com Image Processing Place - General Resources]
+* [https://www.mathworks.com/products/matlab.html MATLAB – The Language of Technical Computing]
-* [https://www.w3schools.com/graphics/default.asp W3Schools Graphics Tutorials]
+* [https://www.adobe.com/products/photoshop.html Adobe Photoshop – Image Editing Software]
 [[Category:Image processing]]
-[[Category:Signal processing]]
 [[Category:Computer science]]
+[[Category:Digital media]]