Image segmentation: key to visual artificial intelligence?
Image segmentation is a fundamental discipline in visual computing and image annotation in artificial intelligence. It consists in dividing an image into significant and distinct regions. This technique is of paramount importance in the field of visual artificial intelligence, allowing computer systems to understand and analyze visual information accurately and effectively. Image segmentation courses are essential for mastering advanced techniques and their practical applications, especially in scientific disciplines, such as monitoring CO2 sequestration and evaluating rock permeability.
By partitioning an image into coherent segments, image segmentation facilitates various tasks such as object recognition, edge detection and pattern analysis. We tell you everything through this article!
What is image segmentation and what is its role in visual artificial intelligence?
Image segmentation is a technique used in visual computing to divide an image into different regions or segments, facilitating the detection of objects, the tasks of classifying and applications in various fields such as computer vision, medical imaging, robotics, and geological analysis.
Its essential role in visual artificial intelligence lies in its ability to provide a structured and meaningful representation of visual information, allowing computer systems to understand and interact with their visual environment in a more sophisticated manner.
By partitioning an image into coherent segments, image segmentation makes it possible to identify and differentiate the various elements present in a visual scene, such as objects, contours, and textures.
This precise segmentation is fundamental for many visual artificial intelligence applications, including object recognition, pattern detection, video surveillance, autonomous navigation, computer-aided diagnostic medicine, and more.
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What are the different approaches and techniques used in image segmentation?
There are several approaches and techniques used in image segmentation. Each image segmentation technique involves a series of specific operations to process and analyze images. Each is tailored to specific contexts and has distinct advantages and limitations. The choice of method often depends on image characteristics, accuracy and performance requirements, as well as real-time processing constraints where appropriate.
Threshold (”Thresholding“)
Threshold (or”Thresholding“) is one of the simplest and most commonly used methods in image segmentation. Its fundamental principle is based on the definition of a threshold value, beyond which pixels are considered to belong to an object of interest, and below which they are classified as belonging to the background.
- Threshold selection
The first step in thresholding is choosing an appropriate threshold value. This value can be determined empirically by examining the image histogram to identify levels of luminance, color, or intensity that clearly separate object pixels from those in the background. Alternatively, more advanced techniques can be used to automatically set the threshold, such as the Otsu method that minimizes intra-class variance.
- Pixel classification
Once the threshold is set, each pixel in the image is compared to this threshold. Pixels whose value exceeds the threshold are assigned to the object of interest, while those whose value is below the threshold are assigned to the background. This classification process is performed for each pixel in the image, resulting in binary segmentation where the pixels are either “activated” (belonging to the object) or “deactivated” (belonging to the background).
- Threshold types
Thresholding can be applied globally, where a single threshold is used for the whole image, or locally, where different thresholds are applied to different regions of the image according to their local characteristics.
For example, global thresholding can be effective in segmenting images that have a uniform contrast between the object and the background. As for local thresholding, it may be more suitable for images with variations in luminance or contrast.
- Post-processing
After segmentation, post-processing techniques can be used to improve the quality of the results. This may include eliminating noise, merging neighboring regions, or filling gaps in the contours of objects.
Contour-based methods
Contour-based methods in image segmentation are essential for identifying the boundaries between objects and the background in an image. These methods make it possible to highlight abrupt transitions in intensity values and to precisely locate the contours of objects with great precision.
- Detecting abrupt transitions
Contour-based methods take advantage of abrupt transitions or significant changes in image color, luminance, or texture values to locate contours. The contours generally correspond to significant variations in these properties, which makes them distinct and identifiable.
- Using gradient operators
Gradient operators, such as the Sobel filter, the Prewitt filter, or the Roberts filter, are tools that are commonly used to detect contours in an image. These operators calculate image gradients, i.e. changes in luminance or pixel intensity, and highlight the regions where these changes are most pronounced, which generally correspond to the contours.
- Canny contour detector
The Canny edge detector is one of the most popular and efficient algorithms for edge detection. To detect contours with high accuracy and low noise sensitivity, it uses several steps, including:
- noise reduction;
- the calculation of the gradient;
- the suppression of local non-maxima;
- the implementation of a Threshold by hysteresis.
- Selecting contours
Once the contours have been detected, various methods can be used to select those that are most relevant or significant for the specific segmentation task. This may include applying quality criteria, such as contour length, curvature, or consistency, or using blending techniques to combine neighboring contour segments.
Segmentation by regions
Segmentation by regions is a powerful and versatile approach to segmenting images into homogeneous regions. This method automatically detects and groups similar pixels into coherent regions. This makes it easy to understand and analyze visual data in a variety of application areas.
- Growth of regions (Region Growing)
This method involves selecting one or more starting pixels, called “seeds,” and then progressively expanding the regions by adding neighboring pixels that share similar characteristics. The process continues until all pixels are assigned to a specific region or until predefined stop criteria are met. Regional growth is sensitive to initial conditions and can be influenced by seed choice and growth criteria.
- Clustering methods
These techniques group image pixels into clusters or homogeneous groups based on their similarities in feature space, such as color, texture, or brightness. The most commonly used clustering algorithm is the K-means algorithm, which partitions data into a predefined number of clusters by minimizing intra-cluster variance. Other clustering methods, such as hierarchical bottom-up classification (CAH) or spectral clustering, can also be used depending on specific segmentation requirements.
- Active region algorithms (Active Contour Models)
Also known as “snakes,” active region algorithms use deformable contours to segment images into homogeneous regions. The active contours are initially placed near the edges of the objects of interest and then they are deformed to fit the actual contours of the object by minimizing a user-defined energy function. Snakes can be used to segment objects with complex or poorly defined boundaries, but they can be sensitive to noise and artifacts in the image.
Segmentation by adaptive threshold
Adaptive threshold segmentation is an effective approach for segmenting images with varying contrast levels or non-uniform lighting conditions. It makes it possible to segment regions with increased precision and better adaptation to local variations. Thus, it is particularly useful in scenarios where image acquisition conditions are variable or unpredictable.
- Breakdown of the image into local areas
First, the image is divided into local areas or blocks of fixed or variable size. Each zone contains a set of pixels that will be processed together to determine the corresponding segmentation threshold.
- Calculation of local thresholds
For each local area, a segmentation threshold is calculated according to the local characteristics of the image. It can be the average or the median of the gray levels of the pixels in the area. This method can also use more sophisticated methods based on local statistical distributions.
- Adaptive segmentation
Once the local thresholds have been calculated, the segmentation of each zone is carried out using its own adaptive threshold. Pixels are classified as belonging to the object or to the background based on their intensity in relation to the threshold of the local area to which they belong.
- Merging results
After the segmentation of each zone, the results are often merged to obtain a coherent segmentation of the entire image. This may involve post-processing steps to eliminate artifacts and inconsistencies between different areas.
Segmentation based on active contours (Active Contour Models)
Active contours are used in a variety of applications including medical image segmentation, object detection in natural images, pattern recognition, and computer vision. Their flexibility and ability to adapt to complex contours make them a valuable tool for image segmentation in cases where other segmentation methods may be ineffective or inaccurate.
- Initializing the active contour
An initial outline is placed near the outline of the object of interest in the image. This outline can be specified manually by the user or automatically initialized using techniques such as border detection or the location of points of interest.
- Contour deformation
Once the initial outline is in place, it is iteratively deformed to fit the actual contours of the object in the image. This is achieved by minimizing a user-defined energy function. The latter takes into account both the coherence of the outline and its adherence to image characteristics, such as luminance gradients or texture properties.
- Optimization of energy
The deformation of the contour is achieved by optimizing the energy function using numerical optimization techniques such as gradient descent or optimization methods based on successive iterations. The objective is to find the contour configuration that minimizes the total energy so that it best fits the contours of the objects in the image.
- Stop the deformation
The deformation of the contour continues until certain predefined stopping criteria are reached, such as the convergence of the algorithm or the stabilization of the contour. At this point, the final outline is obtained and can be used to segment the object of interest in the image.
Segmentation based on machine learning
Segmentation based on machine learning has several advantages, including increased accuracy, an ability to generalize to unseen data, and adaptability to a variety of segmentation tasks. Tools like Python, Pillow, and OpenCV are commonly used for learning computer vision and image segmentation. However, it often requires a large set of training data and significant computational resources to train the model, but it offers exceptional performance in many image segmentation applications.
- Training data collection and preparation
A training data set is formed, including pairs of images and corresponding segmentation masks. The images can be pretreated if needed to normalize pixel values or increase the size of the data set.
- Neural network architecture design
Then, an architecture of convolutional neural network (CNN) is designed to perform the segmentation task. Popular architectures include U-Net, FCN (Fully Convolutional Network), and Mask R-CNN, which are specially designed for image segmentation.
- Neural network training
The neural network is then trained on the training data set to learn how to automatically segment the images. During training, the network adjusts its weights and parameters. To do this, it uses optimization techniques such as error backpropagation to minimize the difference between the segmentation masks predicted by the network and the real segmentation masks.
- Model validation and adjustment
After training, the model is evaluated on a set of validation data to assess its performance and adjust hyperparameters if necessary. This may include techniques such as adjusting the learning rate,Increase in data, or regularization to improve model performance.
- Using the model for segmentation
Once trained, the model can be used to segment new images in real time. By feeding an image into the model, the model automatically generates a segmentation mask that identifies regions of interest in the image.
Semantic segmentation
La semantic segmentation offers a fine and accurate understanding of the content of images. This is very useful in many fields, including computer vision, artificial intelligence, and image analysis.
- Data preparation and annotation
A training data set is formed, including annotated images where each pixel is labeled with its corresponding semantic class. These annotations can be done manually by human annotators or automatically using computer-aided image processing techniques.
- Segmentation network design
A convolutional neural network (CNN), specially designed for semantic segmentation, is then built. Popular architectures include fully convolutional segmentation networks (FCNs), residual deep neural networks (ResNet), or encoders/decoders.
- Neural network training
The neural network is trained on the annotated training data set to learn how to associate each pixel in the image with its corresponding semantic class. During training, the network adjusts its weights and parameters using optimization techniques such as gradient descent to minimize the difference between network predictions and actual annotations.
- Validation and evaluation of the model
After training, the model is evaluated on a set of validation data to assess its performance in terms of accuracy, recall, and other segmentation performance measures. Optimization techniques can be applied to improve model performance if needed.
- Using the model for semantic segmentation
Once trained, the model can be used to segment new images in real time by assigning each pixel in the image a predicted semantic class. This allows for accurate and detailed segmentation of image content, which is useful in many applications, such as autonomous driving, video surveillance, mapping, and many more.
What are the main areas of application of image segmentation in artificial intelligence?
Image segmentation has a multitude of applications in various fields of artificial intelligence:
Object recognition
Image segmentation is used for distinguish and isolate different objects in an image. This ability is crucial for automatic object recognition, where artificial intelligence systems need to identify specific objects in a complex scene.
For example, in video surveillance applications, image segmentation makes it possible to detect and track moving objects, such as vehicles or people, which is critical for security and surveillance.
Computer-aided medical and diagnostic imaging
In medicine, image segmentation is used for the analysis of medical imagery, including scanners and MRIs. Image segmentation helps health professionals diagnose diseases, plan treatments, and assess patient outcomes with greater precision. In particular by identifying and differentiating anatomical structures, lesions or anomalies.
In addition, several articles on the basics of image processing and industrial and robotic vision are available, with the possibility of commenting on these articles on a dedicated forum.
Computer vision and image processing
In the field of computer vision, image segmentation is used to extract important visual characteristics from images, such as contours, textures, or areas of interest. This information can then be used for tasks such as facial recognition, 3D object reconstruction, or augmented reality.
Mapping and remote sensing
In cartography and remote sensing, image segmentation is used to analyze satellite or aerial images in order to map and monitor specific geographic areas. For example, image segmentation can be used to identify and monitor environmental changes, such as deforestation, soil erosion, or urban expansion.
Industry and robotics
In industry and robotics, image segmentation is used to guide robots and machines in tasks such as assembly, quality inspection, or object manipulation. By segmenting images of the work scene, artificial intelligence systems can identify and precisely locate the elements that robots need to interact with, effectively automating industrial processes.
Analysis of an image or video for social networks and marketing
On social networks and on the web, image segmentation is used to visually analyze content shared by users, such as images, videos or the ads. By segmenting this content, artificial intelligence systems can extract relevant information for advertising targeting, trend analysis, or personalized content recommendation, which is essential for online marketing and advertising.
Conclusion
In conclusion, image segmentation plays a leading role in many areas of visual artificial intelligence, offering solutions for effectively analyzing, understanding, and interpreting visual information. We explored various segmentation approaches and techniques, each with its own advantages and limitations, but all contributing to the creation of more accurate and better performing artificial intelligence models.
From traditional methods such as thresholding and edge detection to modern approaches based on machine learning and convolutional neural networks, image segmentation has evolved significantly. It offers solutions adapted to a wide variety of tasks and applications.
It is clear that image segmentation will continue to play an essential role in the evolution of visual artificial intelligence. This is even though new advances, such as semantic segmentation based on deep neural networks, continue to emerge.
