Image processing is a field of study that involves analyzing, manipulating, and enhancing digital images using various algorithms and techniques. These techniques can be broadly categorized into two main categories: image enhancement and image restoration.
1. Image Enhancement:
o Contrast Adjustment: Techniques like histogram equalization, contrast stretching, and gamma correction are used to enhance the dynamic range of an image.
o Filtering: Filtering techniques such as linear filters (e.g., mean, median, and Gaussian filters) and non-linear filters (e.g., edge-preserving filters) can be applied to suppress noise and enhance image details.
o Sharpening: Techniques like unsharp masking and high-pass filtering can enhance the sharpness and details of an image.
o Color Correction: Methods like color balance, color transfer, and color grading can adjust the color appearance of an image.
2. Image Restoration:
o Denoising: Various denoising algorithms, such as median filtering, wavelet-based methods, and total variation denoising, can be used to remove noise from images.
o Deblurring: Techniques like blind deconvolution and Wiener deconvolution are used to recover the original image from blurred versions.
o Super-resolution: Super-resolution techniques aim to enhance the resolution and details of low-resolution images by utilizing information from multiple images or prior knowledge about the image degradation process.
o Image Inpainting: Inpainting algorithms fill in missing or corrupted regions in an image by estimating the content from the surrounding areas.
Apart from these, there are several other advanced image processing techniques, such as image segmentation, object recognition, image registration, and feature extraction, which are widely used in fields like computer vision, medical imaging, and remote sensing.
Let’s review these in detail:
1. Image Filtering: This algorithm involves modifying the pixel values of an image based on a specific filter or kernel. Filters like Gaussian, median, and Sobel are used for tasks like smoothing, noise reduction, and edge detection.
2. Histogram Equalization: It is a technique used to enhance the contrast of an image by redistributing the pixel intensities. This algorithm is often used to improve the visibility of details in an image.
3. Image Segmentation: This algorithm partitions an image into multiple regions or segments based on specific criteria such as color, texture, or intensity. Segmentation is useful for tasks like object recognition, image understanding, and computer vision applications.
4. Edge Detection: Edge detection algorithms identify and highlight the boundaries between different regions in an image. Commonly used edge detection algorithms include Sobel, Canny, and Laplacian of Gaussian (LoG).
5. Image Compression: Image compression algorithms reduce the file size of an image by removing redundant or irrelevant information. Popular compression algorithms include JPEG, PNG, and GIF.
6. Morphological Operations: These algorithms are used for processing binary or grayscale images, mainly focusing on shape analysis and image enhancement. Operations such as dilation, erosion, opening, and closing are commonly used.
7. Feature Extraction: Feature extraction algorithms extract meaningful information or features from an image, which can be used for tasks like object recognition, pattern matching, and image classification. Techniques like Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) are commonly used.
8. Neural Networks: Deep learning algorithms, such as Convolutional Neural Networks (CNNs), are widely used for image processing tasks. CNNs can automatically learn and extract features from images, making them highly effective for tasks like object detection, image classification, and image generation.
As with most algorithms, the quality of data plays an immense role in the output of the image processing. Image capture, continuous capture, lighting and best match among captures are some of the factors when comparing choices for the same image processing task. The use of lighting for better results in high contrast images is a significant area of research. For example,
• Recently, an embedded system was proposed that leverages image processing techniques for intelligent ambient lighting. The focus is on reference-color-based illumination for object detection and positioning within robotic handling scenarios.
• Key points from this research:
o Objective: To improve object detection accuracy and energy utilization.
o Methodology: The system uses LED-based lighting controlled via pulse-width modulation (PWM). Instead of external sensors, it calibrates lighting based on predetermined red, green, blue, and yellow (RGBY) reference objects.
o Color Choice: Yellow was identified as the optimal color for minimal illumination while achieving successful object detection.
o Illuminance Level: Object detection was demonstrated at an illuminance level of approximately 50 lx.
o Energy Savings: Energy savings were achieved based on ambient lighting conditions.
• This study highlights the importance of color choice and intelligent lighting systems in computer vision applications.
Another topic involves improving energy efficiency of indoor lighting:
• This proposes an intelligent lighting control system based on computer vision. It aims to reduce energy consumption and initial installation costs.
• The system utilizes real-time video stream data from existing building surveillance systems instead of traditional sensors for perception.
• By dynamically adjusting lighting based on visual cues, energy efficiency can be improved.
The book "Active Lighting and Its Application for Computer Vision" covers various active lighting techniques. Photometric stereo and structured light are some examples. Actively controlling lighting conditions helps to enhance the quality of captured images and improve subsequent processing.
Previous articles on data processing: DM.docx Image processing is a field of study that involves analyzing, manipulating, and enhancing digital images using various algorithms and techniques. These techniques can be broadly categorized into two main categories: image enhancement and image restoration.
1. Image Enhancement:
o Contrast Adjustment: Techniques like histogram equalization, contrast stretching, and gamma correction are used to enhance the dynamic range of an image.
o Filtering: Filtering techniques such as linear filters (e.g., mean, median, and Gaussian filters) and non-linear filters (e.g., edge-preserving filters) can be applied to suppress noise and enhance image details.
o Sharpening: Techniques like unsharp masking and high-pass filtering can enhance the sharpness and details of an image.
o Color Correction: Methods like color balance, color transfer, and color grading can adjust the color appearance of an image.
2. Image Restoration:
o Denoising: Various denoising algorithms, such as median filtering, wavelet-based methods, and total variation denoising, can be used to remove noise from images.
o Deblurring: Techniques like blind deconvolution and Wiener deconvolution are used to recover the original image from blurred versions.
o Super-resolution: Super-resolution techniques aim to enhance the resolution and details of low-resolution images by utilizing information from multiple images or prior knowledge about the image degradation process.
o Image Inpainting: Inpainting algorithms fill in missing or corrupted regions in an image by estimating the content from the surrounding areas.
Apart from these, there are several other advanced image processing techniques, such as image segmentation, object recognition, image registration, and feature extraction, which are widely used in fields like computer vision, medical imaging, and remote sensing.
Let’s review these in detail:
1. Image Filtering: This algorithm involves modifying the pixel values of an image based on a specific filter or kernel. Filters like Gaussian, median, and Sobel are used for tasks like smoothing, noise reduction, and edge detection.
2. Histogram Equalization: It is a technique used to enhance the contrast of an image by redistributing the pixel intensities. This algorithm is often used to improve the visibility of details in an image.
3. Image Segmentation: This algorithm partitions an image into multiple regions or segments based on specific criteria such as color, texture, or intensity. Segmentation is useful for tasks like object recognition, image understanding, and computer vision applications.
4. Edge Detection: Edge detection algorithms identify and highlight the boundaries between different regions in an image. Commonly used edge detection algorithms include Sobel, Canny, and Laplacian of Gaussian (LoG).
5. Image Compression: Image compression algorithms reduce the file size of an image by removing redundant or irrelevant information. Popular compression algorithms include JPEG, PNG, and GIF.
6. Morphological Operations: These algorithms are used for processing binary or grayscale images, mainly focusing on shape analysis and image enhancement. Operations such as dilation, erosion, opening, and closing are commonly used.
7. Feature Extraction: Feature extraction algorithms extract meaningful information or features from an image, which can be used for tasks like object recognition, pattern matching, and image classification. Techniques like Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) are commonly used.
8. Neural Networks: Deep learning algorithms, such as Convolutional Neural Networks (CNNs), are widely used for image processing tasks. CNNs can automatically learn and extract features from images, making them highly effective for tasks like object detection, image classification, and image generation.
As with most algorithms, the quality of data plays an immense role in the output of the image processing. Image capture, continuous capture, lighting and best match among captures are some of the factors when comparing choices for the same image processing task. The use of lighting for better results in high contrast images is a significant area of research. For example,
• Recently, an embedded system was proposed that leverages image processing techniques for intelligent ambient lighting. The focus is on reference-color-based illumination for object detection and positioning within robotic handling scenarios.
• Key points from this research:
o Objective: To improve object detection accuracy and energy utilization.
o Methodology: The system uses LED-based lighting controlled via pulse-width modulation (PWM). Instead of external sensors, it calibrates lighting based on predetermined red, green, blue, and yellow (RGBY) reference objects.
o Color Choice: Yellow was identified as the optimal color for minimal illumination while achieving successful object detection.
o Illuminance Level: Object detection was demonstrated at an illuminance level of approximately 50 lx.
o Energy Savings: Energy savings were achieved based on ambient lighting conditions.
• This study highlights the importance of color choice and intelligent lighting systems in computer vision applications.
Another topic involves improving energy efficiency of indoor lighting:
• This proposes an intelligent lighting control system based on computer vision. It aims to reduce energy consumption and initial installation costs.
• The system utilizes real-time video stream data from existing building surveillance systems instead of traditional sensors for perception.
• By dynamically adjusting lighting based on visual cues, energy efficiency can be improved.
The book "Active Lighting and Its Application for Computer Vision" covers various active lighting techniques. Photometric stereo and structured light are some examples. Actively controlling lighting conditions helps to enhance the quality of captured images and improve subsequent processing.
Previous articles on data processing: DM.docx Image processing is a field of study that involves analyzing, manipulating, and enhancing digital images using various algorithms and techniques. These techniques can be broadly categorized into two main categories: image enhancement and image restoration.
3. Image Enhancement:
o Contrast Adjustment: Techniques like histogram equalization, contrast stretching, and gamma correction are used to enhance the dynamic range of an image.
o Filtering: Filtering techniques such as linear filters (e.g., mean, median, and Gaussian filters) and non-linear filters (e.g., edge-preserving filters) can be applied to suppress noise and enhance image details.
o Sharpening: Techniques like unsharp masking and high-pass filtering can enhance the sharpness and details of an image.
o Color Correction: Methods like color balance, color transfer, and color grading can adjust the color appearance of an image.
4. Image Restoration:
o Denoising: Various denoising algorithms, such as median filtering, wavelet-based methods, and total variation denoising, can be used to remove noise from images.
o Deblurring: Techniques like blind deconvolution and Wiener deconvolution are used to recover the original image from blurred versions.
o Super-resolution: Super-resolution techniques aim to enhance the resolution and details of low-resolution images by utilizing information from multiple images or prior knowledge about the image degradation process.
o Image Inpainting: Inpainting algorithms fill in missing or corrupted regions in an image by estimating the content from the surrounding areas.
Apart from these, there are several other advanced image processing techniques, such as image segmentation, object recognition, image registration, and feature extraction, which are widely used in fields like computer vision, medical imaging, and remote sensing.
Let’s review these in detail:
9. Image Filtering: This algorithm involves modifying the pixel values of an image based on a specific filter or kernel. Filters like Gaussian, median, and Sobel are used for tasks like smoothing, noise reduction, and edge detection.
10. Histogram Equalization: It is a technique used to enhance the contrast of an image by redistributing the pixel intensities. This algorithm is often used to improve the visibility of details in an image.
11. Image Segmentation: This algorithm partitions an image into multiple regions or segments based on specific criteria such as color, texture, or intensity. Segmentation is useful for tasks like object recognition, image understanding, and computer vision applications.
12. Edge Detection: Edge detection algorithms identify and highlight the boundaries between different regions in an image. Commonly used edge detection algorithms include Sobel, Canny, and Laplacian of Gaussian (LoG).
13. Image Compression: Image compression algorithms reduce the file size of an image by removing redundant or irrelevant information. Popular compression algorithms include JPEG, PNG, and GIF.
14. Morphological Operations: These algorithms are used for processing binary or grayscale images, mainly focusing on shape analysis and image enhancement. Operations such as dilation, erosion, opening, and closing are commonly used.
15. Feature Extraction: Feature extraction algorithms extract meaningful information or features from an image, which can be used for tasks like object recognition, pattern matching, and image classification. Techniques like Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) are commonly used.
16. Neural Networks: Deep learning algorithms, such as Convolutional Neural Networks (CNNs), are widely used for image processing tasks. CNNs can automatically learn and extract features from images, making them highly effective for tasks like object detection, image classification, and image generation.
As with most algorithms, the quality of data plays an immense role in the output of the image processing. Image capture, continuous capture, lighting and best match among captures are some of the factors when comparing choices for the same image processing task. The use of lighting for better results in high contrast images is a significant area of research. For example,
• Recently, an embedded system was proposed that leverages image processing techniques for intelligent ambient lighting. The focus is on reference-color-based illumination for object detection and positioning within robotic handling scenarios.
• Key points from this research:
o Objective: To improve object detection accuracy and energy utilization.
o Methodology: The system uses LED-based lighting controlled via pulse-width modulation (PWM). Instead of external sensors, it calibrates lighting based on predetermined red, green, blue, and yellow (RGBY) reference objects.
o Color Choice: Yellow was identified as the optimal color for minimal illumination while achieving successful object detection.
o Illuminance Level: Object detection was demonstrated at an illuminance level of approximately 50 lx.
o Energy Savings: Energy savings were achieved based on ambient lighting conditions.
• This study highlights the importance of color choice and intelligent lighting systems in computer vision applications.
Another topic involves improving energy efficiency of indoor lighting:
• This proposes an intelligent lighting control system based on computer vision. It aims to reduce energy consumption and initial installation costs.
• The system utilizes real-time video stream data from existing building surveillance systems instead of traditional sensors for perception.
• By dynamically adjusting lighting based on visual cues, energy efficiency can be improved.
The book "Active Lighting and Its Application for Computer Vision" covers various active lighting techniques. Photometric stereo and structured light are some examples. Actively controlling lighting conditions helps to enhance the quality of captured images and improve subsequent processing.
Previous articles on data processing: DM.docx
Subarray Sum equals K
Given an array of integers nums and an integer k, return the total number of subarrays whose sum equals to k.
A subarray is a contiguous non-empty sequence of elements within an array.
Example 1:
Input: nums = [1,1,1], k = 2
Output: 2
Example 2:
Input: nums = [1,2,3], k = 3
Output: 2
Constraints:
• 1 <= nums.length <= 2 * 104
• -1000 <= nums[i] <= 1000
• -107 <= k <= 107
class Solution {
public int subarraySum(int[] nums, int k) {
if (nums == null || nums.length == 0) return -1;
int[] sums = new int[nums.length];
int sum = 0;
for (int i = 0; i < nums.length; i++){
sum += nums[i];
sums[i] = sum;
}
int count = 0;
for (int i = 0; i < nums.length; i++) {
for (int j = i; j < nums.length; j++) {
int current = nums[i] + (sums[j] - sums[i]);
if (current == k){
count += 1;
}
}
}
return count;
}
}
[1,3], k=1 => 1
[1,3], k=3 => 1
[1,3], k=4 => 1
[2,2], k=4 => 1
[2,2], k=2 => 2
[2,0,2], k=2 => 4
[0,0,1], k=1=> 3
[0,1,0], k=1=> 2
[0,1,1], k=1=> 3
[1,0,0], k=1=> 3
[1,0,1], k=1=> 4
[1,1,0], k=1=> 2
[1,1,1], k=1=> 3
[-1,0,1], k=0 => 2
[-1,1,0], k=0 => 3
[1,0,-1], k=0 => 2
[1,-1,0], k=0 => 3
[0,-1,1], k=0 => 3
[0,1,-1], k=0 => 3
