Image Processing Algorithms based on usage

There are many ways to process an image, but they all follow a similar pattern. First, an image's red, green, and blue intensities are extracted. A new pixel is created from these intensities and inserted into a new, empty image at the exact location as the original. In addition, grayscale pixels are created by averaging the intensities of all pixels. Afterward, they can be converted to black or white using a threshold.

Edge Detection

The first thing to note about Canny edge detectors is that they are not substitutes for the human eye. The Canny operator is used to detect edges in different image processing algorithms. This edge detector uses a threshold value of 80. Its original version performs double thresholding and edge tracking through hysteresis. During double thresholding, the edges are classified as strong and weak. Strong edges have a higher gradient value than the high threshold while weak edges fall between the two thresholds. The next phase of this algorithm involves searching for all connected components and selecting the final edge based on the presence of at least one strong edge pixel.

Another improvement to the Canny edge detector is its architecture and computational efficiency. The distributed Canny edge detector algorithm proposes a new block-adaptive threshold selection procedure that exploits local image characteristics. The resulting image will be faster than the CPU implementation. The algorithm is more robust to block size changes, which allows it to support any size image. A new implementation of the distributed Canny edge detector has been developed for FPGA-based systems.

Object localization

The performance of different image processing algorithms for object localization depends on the accuracy of the recognition. While the HOG and SIFT methods use the same dataset, region-based algorithms improve the detection accuracy by more than twofold. The region-based algorithms use a reference marker to enhance matching and edge detection. They use the accurate coordinates in the image sequence to fine-tune the localization process. A geometry-based recognition method eliminates false targets, improves precision, and provides robustness.

The ground test platform is already established and has improved object localization. It can now detect an object with one-tenth of pixel precision. This embedded platform can process an image sequence at 70 frames per second. These works were conducted to make the vision-based system more applicable in dynamic environments. However, the subpixel edge detection method is quite time-consuming and should only be used for fine operations.

Among the popular object detection methods, the Histogram of Oriented Gradients (HOG) was the first algorithm developed. However, it is time-consuming and inefficient when applied to tight spaces. HOG is recommended to be the first method when working in general environments but is ineffective for tight spaces. However, it has decent accuracy for pedestrian detection due to its smooth edges. In addition to general applications, HOG is also suitable for object detection in video games.

YOLO is a popular object detection algorithm and model. It was first introduced in 2016, followed by versions v2 and v3. However, it was not upgraded during 2018 and 2019. Three quick releases of YOLO followed in 2020. YOLO v4 and PP-YOLO were released. These versions can identify objects without using pre-processed images. The speed of these methods makes them popular.


There are various image processing algorithms available for segmentation. These algorithms use the features of the input image to divide the image into regions. A region is a group of pixels with similar properties. Usually, these algorithms use a seed point to start the segmentation process. The seed point may be a small area of the image or a larger region. Once this segmentation is complete, the algorithm adds or removes pixels around it until it merges with other regions.

Discontinuous local features are used to detect edges, which define the boundaries of objects. They work well when the image has few edges and good contrast but are inefficient when the objects are too small. Homogeneous clustering is another method that divides pixels into clusters. It is best suited for small image datasets but may not work well if the clusters are irregular. Some methods use the histogram to segment objects.

In other techniques, pixels may be grouped according to common characteristics, such as the intensity of color or shape. These methods are not limited to color and may use gradient magnitude to classify objects. Some of these algorithms also use local minima as a segmentation boundary. Moreover, they have based on image preprocessing techniques, and many of them use parallel edge detection. There are three main image segmentation algorithms: spatial domain differential operator, affine transform, and inverse-convolution.

A popular implementation of image segmentation is edge-based. It focuses on the edges of different objects in an image, making it easier to find features of these objects. Since edges contain a large amount of information, this technique reduces the size of an image, making it easier to analyze. This method also identifies edges with greater accuracy. The results of both of these methods are highly comparable, although the latter is the more complex approach.

Context navigation

Current navigation systems use multi-sensor data to improve localization accuracy. Context navigation will enhance the accuracy of location estimates by anticipating the degradation of sensor signals. While context detection is the future of navigation, it is not yet widely adopted in the automotive industry.

While most vision-based context indicators deal with place recognition and image segmentation, only a few are dedicated to context-aware navigation. For example, a vehicle in motion can provide information about its surroundings, such as signal quality. However, this information is not widely used in general navigation. Only a few works have focused on context-aware multi-sensor fusion. In addition to addressing these challenges, future research should identify and analyze the best algorithm for a particular situation.

To detect environmental contexts, multi-sensor solutions are needed. GNSS-based solutions can only detect the context of one area, and the underlying data is not reliable enough to extract every context of interest. Other data types, such as vision-based context indicators, are needed for robust classification. The latter is crucial for navigating a complex environment. In the meantime, context navigation needs to be developed for multi-sensor solutions to improve image processing in the automotive industry.

In the future, the goal of context detection will include identifying the environmental context of an image and detecting the corresponding signal properties. GNSS has only provided a basic overview of context detection methods in the past. This research has revealed a few methods that may help improve the efficiency of GNSS-based navigation. However, the performance of these techniques can vary wildly, so it is critical to choose the most appropriate algorithm for the particular situation.

Multiple metric computations

Besides the obvious advantage of reducing computational effort and time, multiple metric computations in different image processing algorithms offer a number of other advantages. Moreover, mining multiple metrics can improve the robustness and validation of image processing algorithms. Below are some of these benefits.

The first of these benefits is measuring more than one metric at once. But how does it work? How can multiple metric computations in different image processing algorithms improve the quality of processed images?

Each metric has its strengths and weaknesses when applied to various categories of image modalities. For example, some metrics have biases toward specific types and cannot encompass the entire range of image modalities in real applications. Thus, multiple metrics may be necessary for proper evaluation. Meta-processing involves the analysis of the results of different image processing algorithms using various metrics. The results of these meta-algorithms can then be compared.

The final data analysis phase of image processing methods can improve performance. For instance, choosing metrics for different problems can help uncover hidden patterns that may affect the outcome of image processing. As a result, researchers can improve their methods by overcoming implicit assumptions about the input data. Another example of multiple metric computations in image processing is the problem of image registration, which involves aligning two images. Generally, image registration requires the optimization of transformation and measurement of similarity. Multiple metric computations are needed in this canonical image processing problem, as it involves a consideration of numerous voxel-level metrics.