The document discusses image representation and feature extraction techniques. It describes how representation makes image information more accessible for computer interpretation using either boundaries or pixel regions. Feature extraction quantifies these representations by extracting descriptors like geometric properties, statistical moments, and textures. Desirable properties for descriptors include being invariant to transformations, compact, robust to noise, and having low complexity. Various boundary and regional descriptors are defined, such as chain codes, shape numbers, and moments.
The document discusses various techniques for image segmentation including discontinuity-based approaches, similarity-based approaches, thresholding methods, region-based segmentation using region growing and region splitting/merging. Key techniques covered include edge detection using gradient operators, the Hough transform for edge linking, optimal thresholding, and split-and-merge segmentation using quadtrees.
This document discusses edge detection and image segmentation techniques. It begins with an introduction to segmentation and its importance. It then discusses edge detection, including edge models like steps, ramps, and roofs. Common edge detection techniques are described, such as using derivatives and filters to detect discontinuities that indicate edges. Point, line, and edge detection are explained through the use of filters like Laplacian filters. Thresholding techniques are introduced as a way to segment images into different regions based on pixel intensity values.
This document discusses various techniques for image segmentation. It describes two main approaches to segmentation: discontinuity-based methods that detect edges or boundaries, and region-based methods that partition an image into uniform regions. Specific techniques discussed include thresholding, gradient operators, edge detection, the Hough transform, region growing, region splitting and merging, and morphological watershed transforms. Motion can also be used for segmentation by analyzing differences between frames in a video.
The document discusses image segmentation techniques. Image segmentation subdivides an image into constituent regions or groups. Segmentation algorithms fall into two categories based on intensity values: discontinuity and similarity. Discontinuity-based algorithms detect points, lines and edges using techniques like gradient operators and Laplacian filters. Similarity-based algorithms include thresholding, region growing, and region splitting/merging.
This document discusses various frequency domain image filtering techniques. It outlines the basic steps for filtering in the frequency domain which includes centering the Fourier transform, computing the discrete Fourier transform, multiplying by a filter function, computing the inverse transform and canceling centering operations. Specific filters are then described including low pass, high pass, ideal filters and Butterworth filters. Examples of applying these filters to images are provided to demonstrate the effects. Homomorphic filtering is also introduced as a technique for illumination correction.
This document discusses texture analysis in image processing. It defines texture as the spatial arrangement of color or intensities in an image that can help with image segmentation and classification. There are two main approaches to texture analysis: structural, which looks at regular patterns of texels, and statistical, which analyzes relationships between pixel intensities using methods like edge detection, co-occurrence matrices, and histograms. Statistical texture analysis captures the degrees of randomness and regularity in textures through metrics calculated from pixel intensity distributions and relationships.
The document discusses edge detection methods including gradient based approaches like Sobel and zero crossing based techniques like Laplacian of Gaussian. It proposes a new algorithm that applies fuzzy logic to the results of gradient and zero crossing edge detection on an image to more accurately identify edges. The algorithm calculates gradient and zero crossings, applies fuzzy rules to classify pixels, and thresholds to determine final edge pixels.
The document discusses image representation and feature extraction techniques. It describes how representation makes image information more accessible for computer interpretation using either boundaries or pixel regions. Feature extraction quantifies these representations by extracting descriptors like geometric properties, statistical moments, and textures. Desirable properties for descriptors include being invariant to transformations, compact, robust to noise, and having low complexity. Various boundary and regional descriptors are defined, such as chain codes, shape numbers, and moments.
The document discusses various techniques for image segmentation including discontinuity-based approaches, similarity-based approaches, thresholding methods, region-based segmentation using region growing and region splitting/merging. Key techniques covered include edge detection using gradient operators, the Hough transform for edge linking, optimal thresholding, and split-and-merge segmentation using quadtrees.
This document discusses edge detection and image segmentation techniques. It begins with an introduction to segmentation and its importance. It then discusses edge detection, including edge models like steps, ramps, and roofs. Common edge detection techniques are described, such as using derivatives and filters to detect discontinuities that indicate edges. Point, line, and edge detection are explained through the use of filters like Laplacian filters. Thresholding techniques are introduced as a way to segment images into different regions based on pixel intensity values.
This document discusses various techniques for image segmentation. It describes two main approaches to segmentation: discontinuity-based methods that detect edges or boundaries, and region-based methods that partition an image into uniform regions. Specific techniques discussed include thresholding, gradient operators, edge detection, the Hough transform, region growing, region splitting and merging, and morphological watershed transforms. Motion can also be used for segmentation by analyzing differences between frames in a video.
The document discusses image segmentation techniques. Image segmentation subdivides an image into constituent regions or groups. Segmentation algorithms fall into two categories based on intensity values: discontinuity and similarity. Discontinuity-based algorithms detect points, lines and edges using techniques like gradient operators and Laplacian filters. Similarity-based algorithms include thresholding, region growing, and region splitting/merging.
This document discusses various frequency domain image filtering techniques. It outlines the basic steps for filtering in the frequency domain which includes centering the Fourier transform, computing the discrete Fourier transform, multiplying by a filter function, computing the inverse transform and canceling centering operations. Specific filters are then described including low pass, high pass, ideal filters and Butterworth filters. Examples of applying these filters to images are provided to demonstrate the effects. Homomorphic filtering is also introduced as a technique for illumination correction.
This document discusses texture analysis in image processing. It defines texture as the spatial arrangement of color or intensities in an image that can help with image segmentation and classification. There are two main approaches to texture analysis: structural, which looks at regular patterns of texels, and statistical, which analyzes relationships between pixel intensities using methods like edge detection, co-occurrence matrices, and histograms. Statistical texture analysis captures the degrees of randomness and regularity in textures through metrics calculated from pixel intensity distributions and relationships.
The document discusses edge detection methods including gradient based approaches like Sobel and zero crossing based techniques like Laplacian of Gaussian. It proposes a new algorithm that applies fuzzy logic to the results of gradient and zero crossing edge detection on an image to more accurately identify edges. The algorithm calculates gradient and zero crossings, applies fuzzy rules to classify pixels, and thresholds to determine final edge pixels.
This document provides an overview of mathematical morphology and its applications to image processing. Some key points:
- Mathematical morphology uses concepts from set theory and uses structuring elements to probe and extract image properties. It provides tools for tasks like noise removal, thinning, and shape analysis.
- Basic operations include erosion, dilation, opening, and closing. Erosion shrinks objects while dilation expands them. Opening and closing combine these to smooth contours or fill gaps.
- Hit-or-miss transforms allow detecting specific shapes. Skeletonization reduces objects to 1-pixel wide representations.
- Morphological operations can be applied to binary or grayscale images. Structuring elements are used to specify the neighborhood of pixels
This document summarizes digital image processing techniques including algebraic approaches to image restoration and inverse filtering. It discusses:
1) Unconstrained and constrained restoration, with unconstrained having no knowledge of noise and constrained using knowledge of noise.
2) Inverse filtering which is a direct method that minimizes error between degraded and original images using matrix operations, but can be unstable due to noise or near-zero filter values.
3) Pseudo-inverse filtering which adds a threshold to the inverse filter to avoid instability, working better for noisy images by not amplifying high frequency noise.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
Sree Narayan Chakraborty presented on the Canny edge detection algorithm. The algorithm aims to detect edges with high signal-to-noise ratio while minimizing false detections. It involves smoothing the image, finding gradients, non-maximum suppression to detect local maxima, and hysteresis thresholding to determine real edges. The performance of Canny edge detection depends on adjustable parameters like the Gaussian filter's standard deviation and threshold values, which can be tailored for different environments.
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
This document discusses various techniques for image segmentation. It begins by defining image segmentation as dividing an image into constituent regions or objects based on visual characteristics. There are two main categories of segmentation techniques: edge-based techniques which detect discontinuities, and region-based techniques which partition images into regions of similarity. Popular region-based techniques include region growing, region splitting and merging, and watershed transformation. Edge-based techniques detect edges using methods like edge detection. The document provides an overview of these segmentation techniques and their applications in image analysis tasks.
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
The Hough transform is a feature extraction technique used in image analysis and computer vision to detect shapes within images. It works by detecting imperfect instances of objects of a certain class of shapes via a voting procedure. Specifically, the Hough transform can be used to detect lines, circles, and other shapes in an image if their parametric equations are known, and it provides robust detection even under noise and partial occlusion. It works by quantizing the parameter space that describes the shape and counting the number of votes each parametric description receives from edge points in the image.
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
This document discusses image segmentation techniques. It describes discontinuity-based segmentation which divides an image based on abrupt intensity changes to find isolated points, lines, and edges. Region-based segmentation groups similar pixels using thresholding, region growing, or splitting and merging. Common edge detection operators are also presented, including Sobel, Prewitt, and Laplacian of Gaussian (LoG) filters. Linking detected edge points can be done locally or globally to find object boundaries in the image.
In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions. This lecture teaches you the basics of feature detection.
https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=SlideShare
This document discusses region-based image segmentation techniques. It introduces region growing, which groups similar pixels into larger regions starting from seed points. Region splitting and merging are also covered, where splitting starts with the whole image as one region and splits non-homogeneous regions, while merging combines similar adjacent regions. The advantages of these methods are that they can correctly separate regions with the same properties and provide clear edge segmentation, while the disadvantages include being computationally expensive and sensitive to noise.
Image compression involves reducing the size of image files to reduce storage space and transmission time. There are three main types of redundancy in images: coding redundancy, spatial redundancy between neighboring pixels, and irrelevant information. Common compression methods remove these redundancies, such as Huffman coding, arithmetic coding, LZW coding, and run length coding. Popular image file formats include JPEG for photos, PNG for web images, and TIFF, GIF, and DICOM for other uses.
This document discusses various digital image processing techniques. It covers connected component labeling, intensity transformations including linear, logarithmic and power law functions. It also describes spatial domain vs transform domain processing and examples of enhancement techniques like contrast stretching and intensity-level slicing. Finally, it discusses geometric transformations and image registration to align images.
Spatial filtering using image processingAnuj Arora
(1) Spatial filtering is defined as operations performed on pixels within a neighborhood of an image using a mask or kernel. (2) Filters can be used to blur/smooth an image by reducing noise or sharpen an image by enhancing edges. (3) Common linear filtering methods include averaging, Gaussian, and derivative filters which are implemented using various mask patterns to modify pixels in the filtered image.
Spatial filtering involves applying filters or kernels to images to enhance or modify pixel values based on neighboring pixel values. Linear spatial filtering involves taking a weighted sum of pixel values within the filter window. Common filters include averaging filters for noise reduction, median filters to reduce impulse noise while preserving edges, and sharpening filters like Laplacian filters and unsharp masking to enhance details.
Image segmentation refers to partitioning a digital image into multiple regions or sets of pixels based on characteristics like color or texture. The goal is to simplify the image representation to make it easier to analyze. Some applications in medical imaging include locating tumors, measuring tissue volumes, and computer-guided surgery. Common segmentation techniques include thresholding, edge detection, region growing, and split-and-merge approaches.
This legal document provides several notices and disclaimers regarding the information presented. Specifically:
- The presentation is for informational purposes only and Intel makes no warranties regarding the information or summaries of the information.
- Any performance claims depend on system configuration and hardware/software/service activation. Performance varies depending on system configuration.
- The sample source code is released under the Intel Sample Source Code License Agreement.
- Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and other countries. Other names may belong to other owners.
- Copyright of the content is held by Intel Corporation and all rights are reserved.
SIFT extracts distinctive invariant features from images to enable object recognition despite variations in scale, rotation, and illumination. The algorithm involves:
1) Constructing scale-space images from differences of Gaussians to identify keypoints.
2) Detecting stable local extrema across scales as candidate keypoints.
3) Filtering out low contrast keypoints and those poorly localized along edges.
4) Assigning orientations based on local gradient directions.
5) Computing descriptors by sampling gradients around keypoints for matching between images.
This document provides an overview of mathematical morphology and its applications to image processing. Some key points:
- Mathematical morphology uses concepts from set theory and uses structuring elements to probe and extract image properties. It provides tools for tasks like noise removal, thinning, and shape analysis.
- Basic operations include erosion, dilation, opening, and closing. Erosion shrinks objects while dilation expands them. Opening and closing combine these to smooth contours or fill gaps.
- Hit-or-miss transforms allow detecting specific shapes. Skeletonization reduces objects to 1-pixel wide representations.
- Morphological operations can be applied to binary or grayscale images. Structuring elements are used to specify the neighborhood of pixels
This document summarizes digital image processing techniques including algebraic approaches to image restoration and inverse filtering. It discusses:
1) Unconstrained and constrained restoration, with unconstrained having no knowledge of noise and constrained using knowledge of noise.
2) Inverse filtering which is a direct method that minimizes error between degraded and original images using matrix operations, but can be unstable due to noise or near-zero filter values.
3) Pseudo-inverse filtering which adds a threshold to the inverse filter to avoid instability, working better for noisy images by not amplifying high frequency noise.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
Sree Narayan Chakraborty presented on the Canny edge detection algorithm. The algorithm aims to detect edges with high signal-to-noise ratio while minimizing false detections. It involves smoothing the image, finding gradients, non-maximum suppression to detect local maxima, and hysteresis thresholding to determine real edges. The performance of Canny edge detection depends on adjustable parameters like the Gaussian filter's standard deviation and threshold values, which can be tailored for different environments.
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
This document discusses various techniques for image segmentation. It begins by defining image segmentation as dividing an image into constituent regions or objects based on visual characteristics. There are two main categories of segmentation techniques: edge-based techniques which detect discontinuities, and region-based techniques which partition images into regions of similarity. Popular region-based techniques include region growing, region splitting and merging, and watershed transformation. Edge-based techniques detect edges using methods like edge detection. The document provides an overview of these segmentation techniques and their applications in image analysis tasks.
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
The Hough transform is a feature extraction technique used in image analysis and computer vision to detect shapes within images. It works by detecting imperfect instances of objects of a certain class of shapes via a voting procedure. Specifically, the Hough transform can be used to detect lines, circles, and other shapes in an image if their parametric equations are known, and it provides robust detection even under noise and partial occlusion. It works by quantizing the parameter space that describes the shape and counting the number of votes each parametric description receives from edge points in the image.
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
This document discusses image segmentation techniques. It describes discontinuity-based segmentation which divides an image based on abrupt intensity changes to find isolated points, lines, and edges. Region-based segmentation groups similar pixels using thresholding, region growing, or splitting and merging. Common edge detection operators are also presented, including Sobel, Prewitt, and Laplacian of Gaussian (LoG) filters. Linking detected edge points can be done locally or globally to find object boundaries in the image.
In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions. This lecture teaches you the basics of feature detection.
https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=SlideShare
This document discusses region-based image segmentation techniques. It introduces region growing, which groups similar pixels into larger regions starting from seed points. Region splitting and merging are also covered, where splitting starts with the whole image as one region and splits non-homogeneous regions, while merging combines similar adjacent regions. The advantages of these methods are that they can correctly separate regions with the same properties and provide clear edge segmentation, while the disadvantages include being computationally expensive and sensitive to noise.
Image compression involves reducing the size of image files to reduce storage space and transmission time. There are three main types of redundancy in images: coding redundancy, spatial redundancy between neighboring pixels, and irrelevant information. Common compression methods remove these redundancies, such as Huffman coding, arithmetic coding, LZW coding, and run length coding. Popular image file formats include JPEG for photos, PNG for web images, and TIFF, GIF, and DICOM for other uses.
This document discusses various digital image processing techniques. It covers connected component labeling, intensity transformations including linear, logarithmic and power law functions. It also describes spatial domain vs transform domain processing and examples of enhancement techniques like contrast stretching and intensity-level slicing. Finally, it discusses geometric transformations and image registration to align images.
Spatial filtering using image processingAnuj Arora
(1) Spatial filtering is defined as operations performed on pixels within a neighborhood of an image using a mask or kernel. (2) Filters can be used to blur/smooth an image by reducing noise or sharpen an image by enhancing edges. (3) Common linear filtering methods include averaging, Gaussian, and derivative filters which are implemented using various mask patterns to modify pixels in the filtered image.
Spatial filtering involves applying filters or kernels to images to enhance or modify pixel values based on neighboring pixel values. Linear spatial filtering involves taking a weighted sum of pixel values within the filter window. Common filters include averaging filters for noise reduction, median filters to reduce impulse noise while preserving edges, and sharpening filters like Laplacian filters and unsharp masking to enhance details.
Image segmentation refers to partitioning a digital image into multiple regions or sets of pixels based on characteristics like color or texture. The goal is to simplify the image representation to make it easier to analyze. Some applications in medical imaging include locating tumors, measuring tissue volumes, and computer-guided surgery. Common segmentation techniques include thresholding, edge detection, region growing, and split-and-merge approaches.
This legal document provides several notices and disclaimers regarding the information presented. Specifically:
- The presentation is for informational purposes only and Intel makes no warranties regarding the information or summaries of the information.
- Any performance claims depend on system configuration and hardware/software/service activation. Performance varies depending on system configuration.
- The sample source code is released under the Intel Sample Source Code License Agreement.
- Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and other countries. Other names may belong to other owners.
- Copyright of the content is held by Intel Corporation and all rights are reserved.
SIFT extracts distinctive invariant features from images to enable object recognition despite variations in scale, rotation, and illumination. The algorithm involves:
1) Constructing scale-space images from differences of Gaussians to identify keypoints.
2) Detecting stable local extrema across scales as candidate keypoints.
3) Filtering out low contrast keypoints and those poorly localized along edges.
4) Assigning orientations based on local gradient directions.
5) Computing descriptors by sampling gradients around keypoints for matching between images.
Divide the examined window into cells (e.g. 16x16 pixels for each cell).
2- For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, leftmiddle,
left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counterclockwise.
3- Where the center pixel's value is greater than the neighbor's value, write "1". Otherwise,
write "0". This gives an 8-digit binary number (which is usually converted to decimal for
convenience).
4- Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e.,
each combination of which pixels are smaller and which are greater than the center).
Improved Characters Feature Extraction and Matching Algorithm Based on SIFTNooria Sukmaningtyas
The document describes an improved SIFT feature extraction and matching algorithm based on the MSER algorithm. It first uses MSER instead of DOG to detect maximally stable elliptical regions, increasing stability and reducing the number of features. It then divides each elliptical region into fan-shaped subregions instead of square subregions, and constructs a new SIFT descriptor using Gaussian-weighted gradient information. Experimental results showed the new algorithm has affine invariance while maintaining other properties of SIFT, making it faster and better suited for real-time image processing.
This document describes a search engine for images that can take a query image and find similar images from a large database. The search engine uses wavelet transforms and k-means clustering to extract feature vectors from images and group them into regions. It then computes signatures for each image by combining regional features. Distances between query and database image signatures determine similarity. The method was implemented in MATLAB and tested on a database of 1000 images, producing results similar to human perception of image similarity.
This document provides an agenda and overview of topics related to intensity transformations and spatial filtering for image enhancement. It discusses piecewise-linear transformation functions including contrast stretching, intensity-level slicing, and bit-plane slicing. It also covers histogram processing techniques such as histogram equalization, histogram matching, and using histogram statistics. Finally, it outlines fundamentals of spatial filtering including the mechanics of spatial filtering, spatial correlation and convolution, and generating smoothing and sharpening spatial filters.
This document compares three image restoration techniques - Iterated Geometric Harmonics, Markov Random Fields, and Wavelet Decomposition - for removing noise from images. It describes each technique and the process used to test them. Noise was artificially added to images using different noise generation functions. Wavelet Decomposition and Markov Random Fields were then used to detect the noise locations. These noise locations were then used to create versions of the noisy images suitable for reconstruction via Iterated Geometric Harmonics. The reconstructed images were then compared to the original to evaluate the performance of each technique.
The document summarizes a study on fractal image compression of satellite images using range and domain techniques. It discusses fractal image compression methods, including partitioning images into range and domain blocks. Affine transformations are applied to domain blocks to match range blocks. Peak signal-to-noise ratio (PSNR) values are calculated for reconstructed rural and urban satellite images after 4 iterations, showing PSNR of around 17.0 for rural images and 22.0 for urban images. The proposed algorithm partitions the original image into non-overlapping range blocks and selects domain blocks twice the size of range blocks.
Object tracking with SURF: ARM-Based platform ImplementationEditor IJCATR
This document describes research on implementing the SURF (Speeded Up Robust Features) algorithm for real-time object tracking on a Raspberry Pi mobile platform. The SURF algorithm extracts features from images that can be used for object detection and tracking across multiple frames. The researchers implemented an application on the Raspberry Pi to select an object image and perform SURF feature extraction and matching between the object image and live camera frames to detect and track the object in real-time video. They discuss adapting the SURF algorithm and libraries to optimize performance on the Raspberry Pi's hardware within its computational limitations for real-time tracking. Experimental results demonstrate object detection and tracking on test and live video streams using the Raspberry
This document discusses GPU-based implementations of bilateral filtering for images. Bilateral filtering smooths images while preserving edges by combining pixel values based on both geometric closeness and photometric similarity. It can be applied to color images in a way that is tuned to human color perception. A naïve bilateral filtering implementation iterates over all pixels, but it is well-suited for parallel GPU implementations due to its iterative and local nature. The document provides mathematical definitions of domain filtering, range filtering, and bilateral filtering, and notes that bilateral filtering combines the benefits of both by enforcing both geometric and photometric locality. It describes using Gaussian functions to implement the filters and discusses parameters for controlling the degree of blurring and edge preservation.
This document provides a literature survey on SIFT-based video watermarking. It discusses several interest point detectors such as Harris corner detector, scale invariant feature transform (SIFT) detector, Harris 3D detector, n-SIFT, and MoSIFT. These detectors aim to identify stable feature points in videos that are invariant to geometric transformations. The document also provides an overview of digital watermarking techniques and applications of SIFT for image watermarking. It discusses challenges in video watermarking such as resisting geometric attacks and collusion. The trends in video watermarking include extending techniques from still images and exploiting video compression formats. The document aims to explore applying SIFT to video watermarking.
Spatial domain filtering and intensity transformations are techniques used in image processing. Spatial domain refers to the pixels that make up an image. Spatial domain techniques operate directly on pixels by applying operators to pixels and their neighbors. Common operators include averaging, median filtering, and contrast adjustments. Spatial filtering techniques include smoothing to reduce noise and sharpening to enhance edges through differentiation. Intensity transformations map input pixel values to output values using functions like logarithms, power laws, and piecewise linear approximations to modify image contrast and highlight certain intensity ranges.
The document describes the Scale-invariant feature transform (SIFT) algorithm. It outlines the key steps: 1) constructing scale space by generating blurred images at different scales, 2) calculating difference of Gaussian images to find keypoints, 3) assigning orientations to keypoints, and 4) generating 128-element feature vectors for each keypoint to uniquely describe local image features in a way that is invariant to scale, rotation, and illumination changes. The SIFT algorithm allows for reliable object recognition and image stitching.
EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVALsipij
1) The document describes an efficient region-based image retrieval system that uses discrete wavelet transform and k-means clustering. It segments images into regions, each characterized by features like size, mean, and covariance.
2) The system pre-processes images by resizing, converting to HSV color space, performing DWT, and using k-means clustering on DWT coefficients to generate regions. It extracts features for each region and stores them in a database.
3) For retrieval, it pre-processes the query image similarly and calculates similarities between the query regions and database regions based on their features, returning similar images.
Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection.
The document discusses various techniques for digital image intensity transformations and histogram processing. It begins with an overview of intensity transformations versus geometric transformations. It then covers log transformations, power-law transformations, and piecewise linear transformations in detail. The document also discusses histogram equalization in depth, including its purpose, principles, and specific operations. Additionally, it compares histogram equalization to other enhancement methods like linear stretch and presents examples of when histogram equalization may fail. Finally, the document introduces fundamentals of spatial filtering, including linear spatial filtering operations using different sized box kernels.
This document provides an overview of image segmentation techniques. It begins with an introduction to image analysis and segmentation. It then covers various discontinuity-based techniques like point, line, and edge detection. Next it discusses edge linking and boundary detection methods. Thresholding techniques such as global, optimal, and adaptive thresholding are also covered. Finally, the document discusses region-based segmentation methods including region growing and region splitting/merging. The overall goal of the document is to introduce and explain the main categories and techniques used for image segmentation.
EFFECTIVE INTEREST REGION ESTIMATION MODEL TO REPRESENT CORNERS FOR IMAGE sipij
One of the most important steps to describe local features is to estimate the interest region around the feature location to achieve the invariance against different image transformation. The pixels inside the interest region are used to build the descriptor, to represent a feature. Estimating the interest region
around a corner location is a fundamental step to describe the corner feature. But the process is challenging under different image conditions. Most of the corner detectors derive appropriate scales to estimate the region to build descriptors. In our approach, we have proposed a new local maxima-based
interest region detection method. This region estimation method can be used to build descriptors to represent corners. We have performed a comparative analysis to match the feature points using recent corner detectors and the result shows that our method achieves better precision and recall results than
existing methods.
SIFT is a scale-invariant feature transform algorithm used to detect and describe local features in images. It detects keypoints that are invariant to scale, rotation, and partially invariant to illumination and viewpoint changes. The algorithm involves 4 main steps: (1) scale-space extrema detection, (2) keypoint localization, (3) orientation assignment, and (4) keypoint descriptor generation. SIFT descriptors provide a feature vector for each keypoint that is highly distinctive and partially invariant to remaining variations.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
3. Feature
Detection
The Feature detection are of two types :
1)GLOBAL
2)LOCAL
LOCAL is further divided into three types:
1)SINGLE SCALE
2)Affine Invariant
3)Multi Scale
4. Global vs Local
Global representation method produces a single vector with
values that measure various aspects of the image such as
color, texture or shape.
This includes color histograms, texture, edges or even a
specific descriptor extracted from some filters applied to the
image.
While main goal of local feature representation is to
distinctively represent the image based on some salient
regions while remaining invariant to viewpoint and
illumination changes.
image is represented based on its local structures by a set of
local feature descriptors extracted from a set of image
regions called interest regions (i.e., keypoints)
5. SingleScale
Harris corner detection : It basically finds the
difference in intensity for a displacement of in all
directions. This is expressed as below:
We need to maximize the ‘E’(Intensity of displacement )
and hence we need to maximize the “shifted Intensity”
part of the previous equation. On maximizing we get the
following equation:
6. Contd..
We get the Eigen values for the equation lambda_1 and
lambda_2. These will help us calculate the score(R):
If R is small then L1 and L2 both will be small and hence
it indicates flat region.
If R < 0 , now this when L1>>L2 and hence this detects
the edge .
If R is large , Now this indicates L1 &L2 are large and
hence the corner is detected.
7. FASTCorner
detection
FAST(Features from accelerated segment test), In this
method, 16 pixels around the center pixel at a radius r of the
Bresenheim circle are taken. This pixels are further checked
for their intensity values.
Now the pixel is a corner if there exists a set of contiguous
pixels in the circle (of 16 pixels) which are all brighter than
(Ip + t) or darker then (Ip - t) .
8. Contd..
Image represents the corner and hence tried to prove the FAST
algorithm.
Fast is provided with machine learning algorithm ID3 , it is used
to classify the detected points into the classes.
After a set of key-points are formed non-maximal suppression is
applied. Sometimes adjacent points might turnout to be corner
points and hence to avoid it the lower one is excluded.
9. Hessian
operator
This operator looks for the points where it’s determinant has a
local maxima.
Now it looks the points where the derivatives responses are high
in two orthogonal directions.
Non-Maximum suppression is applied over the pixel values and
hence the maximum among them is carried ahead.
Then again among all these values the once with intensity values
higher than threshold are taken ahead.
This all is done because the second order operator is sensitive to
the noise and hence we need the pixel intensities which help in
better description of the image.
10. Multi -Scale
In multi-scaling part the image key points are identified at various
scales of Gaussian kernel’s scale.
LoG : LaplacianOf Gaussian, it is a BLOB(Binary LargeObject)
detection method. Now BLOB detection is important because
BLOB tend to represent a set of pixels sharing similar kind of
texture and hence intensity values.
Operator response is strongly dependent on the relationship
between the size of the blob structures in the image domain and
the size of the smoothing Gaussian kernel. Standard deviation in
Gaussian kernel handles the scaling part.
11. Contd…
It directly searches for scale invariant features due it’s ability to
find 3D Extrema. It is also circular due to which it is rotation
invariant.
12. Difference of
Gaussian
DOG- Difference of Gaussian,The DoG function D(x, y,σ) can be
computed without convolution by subtracting adjacent scale
levels of a Gaussian pyramid separated by a factor k.
We need to select the center pixel and see whether it is greater
than every other pixel around it .
13. GaborWavelet
The Gabor wavelets are biologically motivated convolution kernels
in the shape of plane waves restricted by a Gaussian envelope
function.
The advantage of Gabor wavelets is that they provides
simultaneous optimal resolution in both space and spatial
frequency domains.Additionally, Gabor wavelets have the
capability of enhancing low level features such as peaks, valleys
and ridges.
Sai, represents the wavelet. he coefficients of the convolution,
represent the information in a local image region, which should be
more effective than isolated pixels
14. Feature
Descriptor
SIFT(Scale Invariant FeatureTransform):
SCALE SPACE EXTREMA DETECTION: It is nothing but DoG,
hence we further down-sample the image.
KEYPOINT LOCALIZATION :There are some changes in intensity
values upon changing the brightness and other things and hence
to prevent changes in the keypoints some measures have to be
taken. So we have intensity threshold determined empirically and
similarly we have contrast threshold determined.
We also have hessian matrix for computing the curvature.
15. Contd..
ORIENTATIONASSIGNMENT:We have to have , the keypoints
invariant to rotation.
A set of orientation histograms is created where each histogram
contains samples from a 4×4 sub region of the original
neighborhood region and having eight orientations bins in each.
Hence we have 36 bins of size 10 each (36 X 10 = 360 degrees).
The peak is the direction of the key point.
SIFT DESCRIPTOR:The descriptor is then formed from a vector
containing the values of all the orientation histograms entries.
Since there are 4 × 4 histograms each with 8 bins, the feature
vector has 4 × 4 × 8 = 128 elements for each key point
16. SpeededUp
Robust
Features
Detection
(SURF)
SURF, It uses 2D Box Filter like Hessian Operator unlike SIFT , its
basic idea is to approximate the second order Gaussian derivatives
in an efficient way with the help of integral images using a set of
box filters.
w is a relative weight for the filter response and it is used to
balance the expression for the Hessian’s determinant.
The approximated determinant of the Hessian represents the blob
response in the image.
The SURF descriptor starts by constructing a square region
centered around the detected interest point, and oriented along
its main orientation
17. Contd..
The interest region is further divided into smaller 4 × 4 sub-regions
and for each sub region the Harr wavelet responses in the vertical
and horizontal directions.
The wavelet responses dx and dy are summed up for each sub-
region and entered in a feature vector v.
Computing this for all the 4 × 4 sub-regions, resulting a feature
descriptor of length 4 × 4 × 4 = 64 dimensions.
18. LBP- Local
Binary Pattern
It characterizes the spatial structure of the texture.
The LBP feature descriptor:
LBP has the advantage of tolerance of illumination changes and
computational simplicity.Also, the LBP and its variants achieve
great success in texture description. Unfortunately, the LBP
feature is an index of discrete patterns rather than a numerical
feature.
19. Feature
Matching
Once the features and their descriptors have been extracted from
two or more images, the next step is to establish some preliminary
feature matches between these images.
Basics of Brute-Force Matcher
Brute-Force matcher is simple. It takes the descriptor of one
feature in first set and is matched with all other features in second
set using some distance calculation.And the closest one is
returned.
FLANN based Matcher
FLANN stands for Fast Library for Approximate Nearest
Neighbors. It contains a collection of algorithms optimized for fast
nearest neighbor search in large datasets and for high dimensional
features. It works more faster than BFMatcher for large datasets
20. Contd…
To suppress matching candidates for which the correspondence
may be regarded as ambiguous, the ratio between the distances
to the nearest and the next nearest image descriptor is required to
be less than some threshold