diff --git a/chapters/en/unit1/chapter1/applications.mdx b/chapters/en/unit1/chapter1/applications.mdx index 27a0718c8..954b94cd8 100644 --- a/chapters/en/unit1/chapter1/applications.mdx +++ b/chapters/en/unit1/chapter1/applications.mdx @@ -38,7 +38,7 @@ Medical image analysis involves the application of computer vision and machine l - **Diagnostic Assistance**: Computer vision aids in diagnosing diseases and conditions by analyzing medical images. For instance, in radiology, algorithms can detect abnormalities such as tumors and fractures in X-rays or MRIs. These systems assist healthcare professionals by highlighting areas of concern or providing quantitative data that helps decision-making. -- **Segmentation and Detection:**: Medical image analysis involves segmenting and detecting specific structures or anomalies within the images. This process helps isolate organs, tissues, or pathologies for closer examination. For example, in cancer detection, computer vision algorithms can segment and analyze tumors from MRI or CT scans, assisting in treatment planning and monitoring. +- **Segmentation and Detection**: Medical image analysis involves segmenting and detecting specific structures or anomalies within the images. This process helps isolate organs, tissues, or pathologies for closer examination. For example, in cancer detection, computer vision algorithms can segment and analyze tumors from MRI or CT scans, assisting in treatment planning and monitoring. - **Treatment Planning and Monitoring**: Computer vision contributes to treatment planning by providing precise measurements, tracking changes over time, and assisting in surgical planning. It helps doctors understand the extent and progression of a disease, enabling them to plan and adjust treatment strategies accordingly. Doctors were already capable of doing most of these tasks, but they needed to do them by hand. CV systems can do it automatically, which frees us doctors to do other tasks. diff --git a/chapters/en/unit1/chapter1/definition.mdx b/chapters/en/unit1/chapter1/definition.mdx index 7b649fb8c..7309406c8 100644 --- a/chapters/en/unit1/chapter1/definition.mdx +++ b/chapters/en/unit1/chapter1/definition.mdx @@ -14,7 +14,7 @@ Computer vision is the science and technology of making machines see. It involve The evolution of computer vision has been marked by a series of incremental advancements in and across its interdisciplinary fields, where each step forward gave rise to breakthrough algorithms, hardware, and data, giving it more power and flexibility. One such leap was the jump to the widespread use of deep learning methods. -Initially, to extract and learn information in an image, you extract features through image-preprocessing techniques (chapter 3). Once you have a group of features describing your image, you use a classical machine learning algorithm on your dataset of features. It is a strategy that already simplifies things from the hard-coded rules, but it still relies on domain knowledge and exhaustive feature engineering. A more state-of-the-art approach arises when deep learning methods and large datasets meet. Deep learning (DL) allows machines to automatically learn complex features from the raw data. This paradigm shift allowed us to build more adaptive and sophisticated models, causing a renaissance in the field. +Initially, to extract and learn information in an image, you extract features through image-preprocessing techniques (Pre-processing for Computer Vision Tasks). Once you have a group of features describing your image, you use a classical machine learning algorithm on your dataset of features. It is a strategy that already simplifies things from the hard-coded rules, but it still relies on domain knowledge and exhaustive feature engineering. A more state-of-the-art approach arises when deep learning methods and large datasets meet. Deep learning (DL) allows machines to automatically learn complex features from the raw data. This paradigm shift allowed us to build more adaptive and sophisticated models, causing a renaissance in the field. The seeds of computer vision were sown long before the rise of deep learning models during 1960's, pioneers like David Marr and Hans Moravec wrestled with the fundamental question: Can we get machines to see? Early breakthroughs like edge detection algorithms, object recognition were achived with a mix of cleverness and brute-force which laid the ground work for this developing computer vision systems. Over time, as research and development advanced and hardware capabilities improved, the computer vision community expanded exponentially. This vibrant community is composed of researchers,engineers, data scientists, and passionate hobbyists across the globe coming from a vast arrayof disciplines. With open-source and community driven projects we are witnessing democratized access to cutting-edge tools and technologies helping to create a renaissance in this field. @@ -63,5 +63,5 @@ You will read more about the core tasks of computer vision in the Computer Visio The complexity of a given task in the realm of image analysis and computer vision is not solely determined by how noble or difficult a question or task may seem to an informed audience. Instead, it primarily hinges on the properties of the image or data being analyzed. Take, for example, the task of identifying a pedestrian in an image. To a human observer, this might appear straightforward and relatively simple, as we are adept at recognizing people. However, from a computational perspective, the complexity of this task can vary significantly based on factors such as lighting conditions, the presence of occlusions, the resolution of the image, and the quality of the camera. In low-light conditions or with pixelated images, even the seemingly basic task of pedestrian detection can become exceedingly complex for computer vision algorithms,requiring advanced image enhancement and machine learning techniques. Therefore, the challenge in image analysis and computer vision often lies not in the inherent nobility of a task, but in the intricacies of the visual data and the computational methods required to extract meaningful insights from it. ## Link to computer vision applications -As a field, computer vision has a growing importance in society. There are many ethical considerations regarding its applications. For example, a model that is deployed to detect cancer can have terrible consequences if it classifies a cancer sample as healthy. Surveillance technology, such as models that are capable of tracking people, also raises a lot of privacy concerns. This will be discussed in detail in Chapter 14- Applications of Computer Vision and real-world Use Cases, but we will give you a taste of some of its applications. +As a field, computer vision has a growing importance in society. There are many ethical considerations regarding its applications. For example, a model that is deployed to detect cancer can have terrible consequences if it classifies a cancer sample as healthy. Surveillance technology, such as models that are capable of tracking people, also raises a lot of privacy concerns. This will be discussed in detail in "Unit 12 - Ethics and Biases". We will give you a taste of some of its cool applications in "Applications of Computer Vision". diff --git a/chapters/en/unit1/chapter1/motivation.mdx b/chapters/en/unit1/chapter1/motivation.mdx index 5a5f00186..e8f76f01a 100644 --- a/chapters/en/unit1/chapter1/motivation.mdx +++ b/chapters/en/unit1/chapter1/motivation.mdx @@ -14,7 +14,7 @@ If you ever spontaneously kicked a ball, your brain performs a myriad of tasks u Shockingly, we don't need any formal education for this. We don't attend classes for most of the decisions we make daily. No mental math 101 can estimate the foot strength required for kicking a ball. We learned that from trial and error growing up. And some of us might never have learned at all. This is a striking contrast to the way we built programs. Programs are mostly rule-based. -Let’s try to replicate just the first task that our brain did: detecting that there is a ball. One way to do it is to define what a ball is and then exhaustively search for one in the image. Defining what a ball is is actually difficult. Balls can be as small as tennis balls but as big as Zorb balls, so size won’t help us much. We could try to describe its shape, but some balls, like rugby, are not always perfectly spherical. Not everything spherical is a ball either, otherwise ranges, bubbles, candies, and even our planet would all be considered balls. +Let’s try to replicate just the first task that our brain did: detecting that there is a ball. One way to do it is to define what a ball is and then exhaustively search for one in the image. Defining what a ball is is actually difficult. Balls can be as small as tennis balls but as big as Zorb balls, so size won’t help us much. We could try to describe its shape, but some balls, like rugby, are not always perfectly spherical. Not everything spherical is a ball either, otherwise bubbles, candies, and even our planet would all be considered balls.