High-resolution remote sensing images offer great possibilities for urban mapping. Unfortunately, shadows cast
by buildings during this some problems occurred .This paper mainly focus to get the high resolution colour
remote sensing image, and also undertaken to remove the shaded region in the both urban and rural areas. The
region growing thresholding algorithm is used to detect the shadow and extract the features from shadow region.
Then determine whether those neighbouring pixels are added to the seed points or not. In the region growing
threshold algorithm, Pixels are placed in the region based on their properties or the properties of nearby pixel
values. Then the pixels containing similar properties are grouped together and distributed throughout the image.
IOOPL matching is used for removing shadow from image. This method proves it can remove 80% shaded
region from image efficiently.
Retrieving Informations from Satellite Images by Detecting and Removing ShadowIJTET Journal
In accordance with the characteristics of remote sensing images, we put forward a color intensity method of
shadow detection and removal. Some approaches for shadow detection and removal use particular color and spectral
properties of shadows. In this method, the input satellite image color plane is calculated and the values of RGB are separated.
Then the chromaticity is calculated to determine the average value of the segmented region. The Color Intensity algorithm is
adopted to remove the shadow and retrieve the corresponding information.
This document provides an introduction to computer graphics. It discusses how 3D scenes are represented internally with geometric models like polygons, primitives, and smooth patches. These models are projected using linear perspective to generate 2D images. Pixels are used to represent digital images. Rendering involves visibility processing, shading based on lighting models, and texture mapping. It allows for realistic images through techniques like shadows, reflections, and global lighting simulations.
Shadow Detection and Removal in Still Images by using Hue Properties of Color...ijsrd.com
This paper involves the review of the Shadow Detection and Removal in still images. No prior information has been used such as background images etc. for finding the shadows. It is a very challenging issue for the computer vision system that shadows effect the perception of artificial intelligence based machines in appropriately detecting the particular object as shadows also picked by them and detected as false positive objects. Also in surveillance, it affects the proper tracking of humans such as at airports. We proposed a method to remove shadows which eliminates the shadow much better than existed methods. RGB space has been used of the images and some morphological operations also applied to get better results.
Shadow Detection and Removal Techniques A Perspective Viewijtsrd
This document discusses techniques for shadow detection and removal in images. It provides an overview of various methods used, including those based on texture analysis, color information, Gaussian mixture models, and deterministic non-model based approaches. The document then reviews several published papers on different shadow detection and removal algorithms. These algorithms are compared based on advantages and disadvantages in terms of accuracy, computational efficiency, and applicability to different image types and conditions. The conclusion is that shadows remain a challenging problem for computer vision tasks and that the most suitable detection and removal technique depends on the specific image type and application.
SHADOW DETECTION USING TRICOLOR ATTENUATION MODEL ENHANCED WITH ADAPTIVE HIST...ijcsit
Shadows create significant problems in many computer vision and image analysis tasks such as object
recognition, object tracking, and image segmentation. For a machine, it is very difficult to distinguish
between a shadow and a real object. As a result, an object recognition system may incorrectly recognize a
shadow region as an object. So the detection of shadows in images will enhance the performance of many
machine vision tasks. This paper implements a shadow detection method, which is based on Tricolor
Attenuation Model (TAM) enhanced with adaptive histogram equalization (AHE). TAM uses the concept of
intensity attenuation of pixels in the shadow region which is different for the three color channels. It
originates from the idea that if the minimum attenuated color channel is subtracted from the maximum
attenuated one, the shadow areas become darker in the resulting TAM image. But this resulting image will
be of low contrast due to the high correlation among R, G and B color channels. In order to enhance the
contrast, adaptive histogram equalization is used. The incorporation of AHE significantly improved the
quality of the detected shadow region.
This document discusses advanced computer graphics and realistic image generation techniques. It covers topics like modeling objects, lighting, rendering, visible surface determination, shading, textures, shadows, transparency, camera models, and anti-aliasing. Realism involves modeling objects and lighting conditions, determining visible surfaces, calculating pixel colors based on light reflection, and supporting animation. Rendering techniques like line drawings, shading, and shadows add information to convey depth. Anti-aliasing reduces jagged edges by using techniques like supersampling and weighted area sampling.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Retrieving Informations from Satellite Images by Detecting and Removing ShadowIJTET Journal
In accordance with the characteristics of remote sensing images, we put forward a color intensity method of
shadow detection and removal. Some approaches for shadow detection and removal use particular color and spectral
properties of shadows. In this method, the input satellite image color plane is calculated and the values of RGB are separated.
Then the chromaticity is calculated to determine the average value of the segmented region. The Color Intensity algorithm is
adopted to remove the shadow and retrieve the corresponding information.
This document provides an introduction to computer graphics. It discusses how 3D scenes are represented internally with geometric models like polygons, primitives, and smooth patches. These models are projected using linear perspective to generate 2D images. Pixels are used to represent digital images. Rendering involves visibility processing, shading based on lighting models, and texture mapping. It allows for realistic images through techniques like shadows, reflections, and global lighting simulations.
Shadow Detection and Removal in Still Images by using Hue Properties of Color...ijsrd.com
This paper involves the review of the Shadow Detection and Removal in still images. No prior information has been used such as background images etc. for finding the shadows. It is a very challenging issue for the computer vision system that shadows effect the perception of artificial intelligence based machines in appropriately detecting the particular object as shadows also picked by them and detected as false positive objects. Also in surveillance, it affects the proper tracking of humans such as at airports. We proposed a method to remove shadows which eliminates the shadow much better than existed methods. RGB space has been used of the images and some morphological operations also applied to get better results.
Shadow Detection and Removal Techniques A Perspective Viewijtsrd
This document discusses techniques for shadow detection and removal in images. It provides an overview of various methods used, including those based on texture analysis, color information, Gaussian mixture models, and deterministic non-model based approaches. The document then reviews several published papers on different shadow detection and removal algorithms. These algorithms are compared based on advantages and disadvantages in terms of accuracy, computational efficiency, and applicability to different image types and conditions. The conclusion is that shadows remain a challenging problem for computer vision tasks and that the most suitable detection and removal technique depends on the specific image type and application.
SHADOW DETECTION USING TRICOLOR ATTENUATION MODEL ENHANCED WITH ADAPTIVE HIST...ijcsit
Shadows create significant problems in many computer vision and image analysis tasks such as object
recognition, object tracking, and image segmentation. For a machine, it is very difficult to distinguish
between a shadow and a real object. As a result, an object recognition system may incorrectly recognize a
shadow region as an object. So the detection of shadows in images will enhance the performance of many
machine vision tasks. This paper implements a shadow detection method, which is based on Tricolor
Attenuation Model (TAM) enhanced with adaptive histogram equalization (AHE). TAM uses the concept of
intensity attenuation of pixels in the shadow region which is different for the three color channels. It
originates from the idea that if the minimum attenuated color channel is subtracted from the maximum
attenuated one, the shadow areas become darker in the resulting TAM image. But this resulting image will
be of low contrast due to the high correlation among R, G and B color channels. In order to enhance the
contrast, adaptive histogram equalization is used. The incorporation of AHE significantly improved the
quality of the detected shadow region.
This document discusses advanced computer graphics and realistic image generation techniques. It covers topics like modeling objects, lighting, rendering, visible surface determination, shading, textures, shadows, transparency, camera models, and anti-aliasing. Realism involves modeling objects and lighting conditions, determining visible surfaces, calculating pixel colors based on light reflection, and supporting animation. Rendering techniques like line drawings, shading, and shadows add information to convey depth. Anti-aliasing reduces jagged edges by using techniques like supersampling and weighted area sampling.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
1) The document describes generating 3D models of a full ball and deflated ball using photogrammetry. Photos were taken with a Sony Alpha 6300 camera and processed in Agisoft Photoscan.
2) Dense point clouds, mesh models, and textured 3D models were successfully generated but had some distortion at the bottom. More overlapping photos from different angles were needed to fully capture the spherical objects.
3) CloudCompare software aligned and compared the two point clouds, finding a mean deformation of 8.395 mm between the full and deflated balls. The alignment and scale of point clouds affected the accuracy of deformation measurements.
1) The document discusses a method for shadow detection in urban area images. It involves transforming RGB images to HSI color space, then segmenting pixels based on ratios of green to blue values.
2) A color invariant is used to segment blue values and extract shadow regions, which are then thresholded. Morphological operations like erosion and opening are applied to remove noise from the binary shadow mask.
3) The method provides detection results on sample urban images and outlines the graphical user interface for the shadow detection process.
The document discusses various techniques for image segmentation. It begins by defining image segmentation as dividing an image into constituent regions or objects. It then describes several segmentation methods including those based on discontinuity, similarity, grey scale, texture, motion, depth, edge detection, region growing, and thresholding. Thresholding techniques include global thresholding of the image histogram as well as adaptive thresholding which divides an image into sub-images for thresholding. The goal of segmentation is to extract objects of interest from an image.
Threshold Selection for Image segmentationParijat Sinha
1. The document examines different image segmentation techniques and threshold selection methods. It analyzes thresholding applied to images of rice grains and spots.
2. Global and adaptive thresholding techniques are compared, with adaptive thresholding found to better handle non-uniform backgrounds. Histogram peak and valley methods for optimal threshold selection are described.
3. Analyzing a spot image, adaptive thresholding at 50-75% best identified the spot, while other edge detectors like Roberts failed. Adaptive thresholding and spot profile analysis were concluded to best analyze spot images.
This document discusses several topics in image enhancement and processing including:
1. Spatial filtering which involves applying a weighted mask over an image to replace pixel values.
2. Logarithmic transformation which expands darker pixel values more than brighter ones for enhancement.
3. Thresholding, logarithmic transformation, negative transformation, contrast stretching, and grey level slicing as common nonlinear image transformations.
4. Weighted average filtering which applies different coefficients to pixels to give more importance to some over others.
5. High boost filters and unsharp masking as types of high pass sharpening filters used to highlight fine image details.
This document provides an overview of machine vision applications including content-based image retrieval and face recognition. It discusses how content-based image retrieval systems work by extracting image features, calculating distances between images, and returning similar images from a database based on a query image. Examples of content-based image retrieval systems and the features they use are described. The document also covers face detection and recognition techniques, including the use of eigenfaces which represent faces as locations in a lower-dimensional space.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This paper proposes a new method for visual segmentation based on fixation points. The method segments the region of interest in two steps: (1) generating a probabilistic boundary edge map combining multiple visual cues, and (2) finding the optimal closed contour around the fixation point in the transformed polar edge map. The paper shows this fixation-based segmentation approach improves accuracy over previous methods, especially when incorporating motion and stereo cues. It also introduces a region merging algorithm to further refine segmentation results. Evaluation on video and stereo image datasets demonstrates mean F-measures of 0.95 and 0.96 respectively when combining cues, compared to 0.62 and 0.65 without.
The document discusses image segmentation techniques. It defines image segmentation as partitioning a digital image into multiple segments or regions that are similar in characteristics such as color or texture. The main goal of image segmentation is to simplify an image into meaningful parts for analysis. Common techniques discussed include thresholding, clustering, edge detection, region growing, and neural networks. Thresholding uses threshold values to separate pixels into multiple classes or objects. Clustering groups similar image pixels together while edge detection finds boundaries between objects. The document also provides an example of the split and merge segmentation method.
Marker Controlled Segmentation Technique for Medical applicationRushin Shah
Medical image segmentation is a very important field for the medical science. In medical images, edge detection is an important work for object recognition of the human organs such as brain, heart or kidney etc. and it is an essential pre-processing step in medical image segmentation.
Medical images such as CT, MRI or X-Ray visualizes the various information’s of internal organs which is very important for doctors diagnoses as well as medical teaching, learning and research.
It is a tough job to locate the internal organs if images contains noise or rough structure of human body organs.
This document provides an overview of feature detection techniques in machine vision, including edge detection, the Canny edge detector, interest points, and the Harris corner detector. It describes how edge detection works by finding discontinuities in images using masks and correlation. It explains that the Canny edge detector is an optimal method that uses Gaussian smoothing and non-maximum suppression. Interest points are localized features useful for applications like image alignment, and the Harris corner detector computes gradients to find locations with dominant directions, identifying corners.
This document provides an overview of image matching techniques. It defines image matching as geometrically aligning two images so corresponding pixels represent the same scene region. Key aspects covered include detecting invariant local features, describing features in a scale and rotation invariant way using SIFT, and matching features between images. SIFT is highlighted as an extraordinarily robust technique that can handle various geometric and illumination changes. Feature matching is used in many computer vision applications such as image alignment, 3D reconstruction, and object recognition.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
This document discusses line detection in images. It introduces line detection and the problem of filtering lines from other image elements. It then describes several common methods for line detection, including Sobel operators, Laplacian operators, and Laplacian of Gaussian. It discusses using these methods to detect lines and the potential applications of line detection technology.
Efficient fingerprint image enhancement algorithm based on gabor filtereSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Image segmentation involves grouping similar image components, such as pixels, into segments. It has applications in medical imaging, satellite imagery, and video summarization. Common methods include thresholding, k-means clustering, and region-based approaches. Thresholding segments an image based on pixel intensity values, while k-means clustering groups pixels into a specified number of clusters based on color or other feature similarity. Region-based methods grow or merge regions of similar pixels. Watershed segmentation treats an image as a topographic surface and finds boundaries between regions.
This document discusses a new methodology for shadow removal from images based on Strong Edge Detection (SED). The SED method recognizes strong shadow edges by analyzing image patch characteristics and features to classify shadow edges. Spatial smoothing is also used. Entropy and standard deviation results of the SED method and an earlier Patch Based Shadow Edge Detection method are calculated and compared, showing the SED method performs better with lower entropy and higher standard deviation. The SED method detects shadow edges more accurately than the previous patch-based approach.
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.
Modal analysis of a 2-cylinder crankshaft using ANSYSIJERA Editor
Crankshaft is a relevant component in an I.C. engine which transforms the reciprocating motion into rotational
motion. This makes the crankshaft liable to vibrations due to different types of stresses and loads. Modal analysis
when performed on crankshaft, gives the behavior of its structure through different mode shapes at different
frequencies. It also helps to determine resonance frequencies. Modal analysis is the primary stage of many useful
analysis like harmonic analysis and transient dynamic analysis, which give the dynamic behavior of the
crankshaft.
A Noise Tolerant and Low Power Dynamic Logic Circuit Using Finfet TechnologyIJERA Editor
For the improvement of performance and noise tolerance in dynamic logic circuits, a technique is
proposed in this paper. A two-input AND gate is designed and simulated in 32nm technology using FinFET
device. Simulation results indicate that the proposed technique provides improvement in noise tolerance of
about three times and the use of FinFET device reduces the power consumption over the conventional MOSFET
designs.
1) The document describes generating 3D models of a full ball and deflated ball using photogrammetry. Photos were taken with a Sony Alpha 6300 camera and processed in Agisoft Photoscan.
2) Dense point clouds, mesh models, and textured 3D models were successfully generated but had some distortion at the bottom. More overlapping photos from different angles were needed to fully capture the spherical objects.
3) CloudCompare software aligned and compared the two point clouds, finding a mean deformation of 8.395 mm between the full and deflated balls. The alignment and scale of point clouds affected the accuracy of deformation measurements.
1) The document discusses a method for shadow detection in urban area images. It involves transforming RGB images to HSI color space, then segmenting pixels based on ratios of green to blue values.
2) A color invariant is used to segment blue values and extract shadow regions, which are then thresholded. Morphological operations like erosion and opening are applied to remove noise from the binary shadow mask.
3) The method provides detection results on sample urban images and outlines the graphical user interface for the shadow detection process.
The document discusses various techniques for image segmentation. It begins by defining image segmentation as dividing an image into constituent regions or objects. It then describes several segmentation methods including those based on discontinuity, similarity, grey scale, texture, motion, depth, edge detection, region growing, and thresholding. Thresholding techniques include global thresholding of the image histogram as well as adaptive thresholding which divides an image into sub-images for thresholding. The goal of segmentation is to extract objects of interest from an image.
Threshold Selection for Image segmentationParijat Sinha
1. The document examines different image segmentation techniques and threshold selection methods. It analyzes thresholding applied to images of rice grains and spots.
2. Global and adaptive thresholding techniques are compared, with adaptive thresholding found to better handle non-uniform backgrounds. Histogram peak and valley methods for optimal threshold selection are described.
3. Analyzing a spot image, adaptive thresholding at 50-75% best identified the spot, while other edge detectors like Roberts failed. Adaptive thresholding and spot profile analysis were concluded to best analyze spot images.
This document discusses several topics in image enhancement and processing including:
1. Spatial filtering which involves applying a weighted mask over an image to replace pixel values.
2. Logarithmic transformation which expands darker pixel values more than brighter ones for enhancement.
3. Thresholding, logarithmic transformation, negative transformation, contrast stretching, and grey level slicing as common nonlinear image transformations.
4. Weighted average filtering which applies different coefficients to pixels to give more importance to some over others.
5. High boost filters and unsharp masking as types of high pass sharpening filters used to highlight fine image details.
This document provides an overview of machine vision applications including content-based image retrieval and face recognition. It discusses how content-based image retrieval systems work by extracting image features, calculating distances between images, and returning similar images from a database based on a query image. Examples of content-based image retrieval systems and the features they use are described. The document also covers face detection and recognition techniques, including the use of eigenfaces which represent faces as locations in a lower-dimensional space.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This paper proposes a new method for visual segmentation based on fixation points. The method segments the region of interest in two steps: (1) generating a probabilistic boundary edge map combining multiple visual cues, and (2) finding the optimal closed contour around the fixation point in the transformed polar edge map. The paper shows this fixation-based segmentation approach improves accuracy over previous methods, especially when incorporating motion and stereo cues. It also introduces a region merging algorithm to further refine segmentation results. Evaluation on video and stereo image datasets demonstrates mean F-measures of 0.95 and 0.96 respectively when combining cues, compared to 0.62 and 0.65 without.
The document discusses image segmentation techniques. It defines image segmentation as partitioning a digital image into multiple segments or regions that are similar in characteristics such as color or texture. The main goal of image segmentation is to simplify an image into meaningful parts for analysis. Common techniques discussed include thresholding, clustering, edge detection, region growing, and neural networks. Thresholding uses threshold values to separate pixels into multiple classes or objects. Clustering groups similar image pixels together while edge detection finds boundaries between objects. The document also provides an example of the split and merge segmentation method.
Marker Controlled Segmentation Technique for Medical applicationRushin Shah
Medical image segmentation is a very important field for the medical science. In medical images, edge detection is an important work for object recognition of the human organs such as brain, heart or kidney etc. and it is an essential pre-processing step in medical image segmentation.
Medical images such as CT, MRI or X-Ray visualizes the various information’s of internal organs which is very important for doctors diagnoses as well as medical teaching, learning and research.
It is a tough job to locate the internal organs if images contains noise or rough structure of human body organs.
This document provides an overview of feature detection techniques in machine vision, including edge detection, the Canny edge detector, interest points, and the Harris corner detector. It describes how edge detection works by finding discontinuities in images using masks and correlation. It explains that the Canny edge detector is an optimal method that uses Gaussian smoothing and non-maximum suppression. Interest points are localized features useful for applications like image alignment, and the Harris corner detector computes gradients to find locations with dominant directions, identifying corners.
This document provides an overview of image matching techniques. It defines image matching as geometrically aligning two images so corresponding pixels represent the same scene region. Key aspects covered include detecting invariant local features, describing features in a scale and rotation invariant way using SIFT, and matching features between images. SIFT is highlighted as an extraordinarily robust technique that can handle various geometric and illumination changes. Feature matching is used in many computer vision applications such as image alignment, 3D reconstruction, and object recognition.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
This document discusses line detection in images. It introduces line detection and the problem of filtering lines from other image elements. It then describes several common methods for line detection, including Sobel operators, Laplacian operators, and Laplacian of Gaussian. It discusses using these methods to detect lines and the potential applications of line detection technology.
Efficient fingerprint image enhancement algorithm based on gabor filtereSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Image segmentation involves grouping similar image components, such as pixels, into segments. It has applications in medical imaging, satellite imagery, and video summarization. Common methods include thresholding, k-means clustering, and region-based approaches. Thresholding segments an image based on pixel intensity values, while k-means clustering groups pixels into a specified number of clusters based on color or other feature similarity. Region-based methods grow or merge regions of similar pixels. Watershed segmentation treats an image as a topographic surface and finds boundaries between regions.
This document discusses a new methodology for shadow removal from images based on Strong Edge Detection (SED). The SED method recognizes strong shadow edges by analyzing image patch characteristics and features to classify shadow edges. Spatial smoothing is also used. Entropy and standard deviation results of the SED method and an earlier Patch Based Shadow Edge Detection method are calculated and compared, showing the SED method performs better with lower entropy and higher standard deviation. The SED method detects shadow edges more accurately than the previous patch-based approach.
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.
Modal analysis of a 2-cylinder crankshaft using ANSYSIJERA Editor
Crankshaft is a relevant component in an I.C. engine which transforms the reciprocating motion into rotational
motion. This makes the crankshaft liable to vibrations due to different types of stresses and loads. Modal analysis
when performed on crankshaft, gives the behavior of its structure through different mode shapes at different
frequencies. It also helps to determine resonance frequencies. Modal analysis is the primary stage of many useful
analysis like harmonic analysis and transient dynamic analysis, which give the dynamic behavior of the
crankshaft.
A Noise Tolerant and Low Power Dynamic Logic Circuit Using Finfet TechnologyIJERA Editor
For the improvement of performance and noise tolerance in dynamic logic circuits, a technique is
proposed in this paper. A two-input AND gate is designed and simulated in 32nm technology using FinFET
device. Simulation results indicate that the proposed technique provides improvement in noise tolerance of
about three times and the use of FinFET device reduces the power consumption over the conventional MOSFET
designs.
Improved Reliability Memory’s Module Structure for Critical Application SystemsIJERA Editor
For critical application systems, which control nuclear power plants and other energy facilities, air, sea and
ground vehicles, the needs to ensure their operability are increased. To fulfill this requirement, it is necessary to
increase the technical readiness coefficient, the value of which increases with decreasing recovery time control
system in case of fault of its constituent units. The main control system components critical applications are
memory devices, which store programs and used for performing algorithms control. Semiconductor memory
modules with automatic recovery functionality at multiple faults can be used in systems of critical applications
protection and management where the use of fault-tolerant digital devices is a necessity due to the inability of
traditional methods of repair by replacing the failed elements.
Shortest Tree Routing With Security In Wireless Sensor NetworksIJERA Editor
We propose STR to resolve the main reasons of overall network performance degradation of ZTR, which are the detour path problem and the traffic concentration problem. Second, we prove that the 1-hop neighbor nodes used by STR improve the routing path efficiency and alleviate the traffic load concentrated on tree links in ZTR. Third, we analyze the performance of ZTR, STR, and AODV by differentiating the network conditions such as network density, ZigBee network constraints, traffic types, and the network traffic. For modification security purpose we are also encrypting the data packets during transmission. So that the intermediate nodes are not able to view the data during transmission. For Encryption process, we are using RC4 Algorithm. Short cut tree routing is used for minimizing the routing path from source to destination.
An Approach for Object and Scene Detection for Blind Peoples Using Vocal Vision.IJERA Editor
This system help the blind peoples for the navigation without the help of third person so blind person can perform its work independently. This system implemented on android device in which object detection and scene detection implemented, so after detection there will be text to speech conversion so user or blind person can get message from that android device with the help of headphone connected to that device. Our project will help blind people to understand the images which will be converted to sound with the help of webcam. We shall capture images in front of blind peoples .The captured image will be processed through our algorithms which will enhances the image data. The hardware component will have its own database. The processed image is compare with the database in the hardware component .The result after processing and comparing will be converted into speech signals. The headphones guide the blind peoples.
Evaluation of Energy Contribution for Additional Installed Turbine Flow in Hy...IJERA Editor
The paper presents the methodology for analysis of energy value of existing hydro power plant (HPP) comparing
with the same HPP upgraded with additional power. The additional power can be added with installed turbine
flows of existing unit(s) or with installing the new additional unit(s). The paper treated the energy evaluation of
the storage HPP Spilje which is under installed HPP comparing the technical parameters, water reservoir, turbine
discharge and water inflow. The analyses have been made based on the statistical analysis of the input
parameters of hydrology through a series of hydrological inflow data for a long period of years with monthly
distribution. The hydrological data (from 1970 until 2014) which covers extremely wet and extremely dry
hydrological periods, which is important in order to get statistics of expected values with appropriate
probabilities of occurrence – data from monthly inflow values [1]. The results from analysis can be applied in
determining the expected hydro production, or appropriate expected revenue of the electricity generated from the
HPP. The additional turbine flow in existing units or installing the new unit(s) can benefit with increased income
coming as the result of avoiding spilling water and generating additional peak energy instead of base one.
Analysis of soil arching effect with different cross-section anti-slide pileIJERA Editor
Although the knowledge of soil-arching effect of anti-slide pile become common, the analysis of soil-arching effect with different cross-section anti-slide pile is little. Therefore,this paper choose three typical cross-section piles (rectangular, square, circular), Then do some study about the Mechanism of mechanism, frictional arch and end bearing arch of three cross-section piles and the form of soil arch with different cross-section and the soil arch zone under same condition in order to define the best cross-section, The result show that rectangular section pile and square section pile are composed by frictional arch and end bearing arch, while circular section pile is made up by united arch, finally rectangular section pile has a more effective retaining effect than square section pile and circular section pile through compared soil arch zone with three type section piles.
Laboratory Performance Of Evaporative Cooler Using Jute Fiber Ropes As Coolin...IJERA Editor
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Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/25127/shadow-detection-and-removal-using-tricolor-attenuation-model-based-on-feature-descriptor/rakesh-dangi
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International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
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International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
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A Method of Survey on Object-Oriented Shadow Detection & Removal for High Resolution Urban Aerial Colour Images
1. A.Amareswar Kumar Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 12, (Part - 1) December 2015, pp.89-94
www.ijera.com 89|P a g e
A Method of Survey on Object-Oriented Shadow Detection &
Removal for High Resolution Urban Aerial Colour Images
1
A.Amareswar Kumar, 2
B.Mohan Reddy
1
PG Schlor. Dept. of ECE, Chiranjeevi Reddy Institute of Technology and Science, A.P.
2
Asst. Professor, Dept. of ECE, Chiranjeevi Reddy Institute of Technology and Science, A.P.
Abstract
High-resolution remote sensing images offer great possibilities for urban mapping. Unfortunately, shadows cast
by buildings during this some problems occurred .This paper mainly focus to get the high resolution colour
remote sensing image, and also undertaken to remove the shaded region in the both urban and rural areas. The
region growing thresholding algorithm is used to detect the shadow and extract the features from shadow region.
Then determine whether those neighbouring pixels are added to the seed points or not. In the region growing
threshold algorithm, Pixels are placed in the region based on their properties or the properties of nearby pixel
values. Then the pixels containing similar properties are grouped together and distributed throughout the image.
IOOPL matching is used for removing shadow from image. This method proves it can remove 80% shaded
region from image efficiently.
Keywords -region growing thresholding, IOOPL (inner-outer outline profile line)
I. INTRODUCTION
Now a days, the man survive large area of the
world, so to monitor land area satellite imaging is
used to detect the earth locality from the satellites of
IKONOS, QUICKBIRD and RESOURSE3. The
shadow is occurred by interfacing of building and
sun. A shadow occurs when an object partially or
totally occludes direct light from a source of
illumination [1]. Shadows can be divided into two
classes: self and cast shadows. A self-shadow occurs
in the portion of an object which is not illuminated by
direct light.A cast shadow is projected by the object
in the direction of the light source. The part of a cast
shadow where direct light is completely blocked by
an object is called the umbra, while the part where
direct light is partially blocked is called the
penumbra.The shadow formation is shown in Fig.1.
This paper mainly focuses on the shadows in the cast
shadow area of the remote sensing images by using
region growing thresholding.
Fig.1. Principle of shadow formation.
In this process some of the problems occurred,
due to the shadow of an urban and rural area. Many
effective algorithms have been proposed for shadow
detection.Many seminal papers proposed some
methods to detect& eliminate shaded region by using
edge detection methods, but it has some drawbacks
are no image calibration for the intensity and,
illumination adjustment. So this paper mainly focus
to get the high resolution colour remote sensing
image, and also undertaken to remove the shaded
region in both urban and rural areas.
Because of the ambient light, the ratios of the
two pixels are not same in all three colour channels.
These two pixels will be different not only in
intensity, but also in hue and saturation. Thus,
correcting just the intensity of the shadowed pixels
does not remove the shadow, and we need to correct
the chromaticity values as well. Using the shadow
density, the shadow area is segmented into sunshine,
penumbra and umbra regions. Since the lighting
colour of the umbra region is not always the same as
that of the sunshine region, colour adjustment is
performed between them. Then, the colour average
and variance of the umbra region are adjusted to be
the same as those of sunshine region. In the
penumbra, colour and brightness adjustments for
small Deb et al.: Shadow Detection and Removal
Based on regions are performed the same as they are
for umbra region. Finally, all boundaries between
shadowed regions and neighbouring regions are
smoothed by convolving them with a Gaussian mask.
II. METHODOLOGY
Seeded region growing thresholding method is
used for detecting shadows in image. This algorithm
follows the below processes to detect the shadows.
RESEARCH ARTICLE OPEN ACCESS
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A. Pre-processing:
In pre-processing, the input image is under gone
for RGB into grayscale image conversion process.
The converted grayscale image is resized into
specific resolution; hence the resolution is maintained
till further process. If any noise is added to image,
then the image is not proper visible, so a filter is used
to eliminate noise present in the image. In this
method, Pre-processing is carried out by resizing the
image. Then the resized image is converted into
grayscale image. The grayscale image is then
converted into the noise free image by using
medianfilter. This filtered image is then converted
into binary image. The object region is considered as
„1‟ and dark area is considered as „0‟.After Pre-
processing image look likes as shown in fig 2.
Fig.2. Pre-processed image
B. Feature Extraction:
Feature extraction is the process of extracting
required data‟s from the region of interest. In this
method, shadow feature is extracted by region
growing threshold method. Then the object properties
such as spectral features and geometric features are
combined with a spatial relationship, in which the
false shadows are detected and eliminated. In this
technique, the feature extraction is carried to extract
the features of five major categories.
Extract average grayscale value of Pre-processed
image.
Extract histogram peak of shadow area.
Calculatethreshold value of image.
Extract frequency of image also.
Extract nearby pixel values from same image.
C. Segmentation:
Segmentation is the process of separating
required part from the cluster of unwanted
background region. To separate required object,
shape factor and colour factor is considered to
remove the shadow, but dark region of image should
not be eliminated. The parameter of each image is
recorded and then variation in the shadow and dark
area is noted.
By using the method of region growing threshold
algorithm, Pixels are placed in the region based on
their properties or the properties of the nearby pixel
values. Then the pixels containing the similar
properties are grouped together and then the large
number of pixels is distributed throughout the image.
The aim of region detection is to provide the
possibility to characterize the detected object by
parameter analysis (shape, position, size...) Edge-
based segmentation: borders between regions.
Region-based segmentation [6]: direct construction of
regions. It is an easy way to construct regions from
their borders and it is also easy to detect borders of
existing regions. Segmentations resulting from edge-
based method and region growing method are not
usually exactly the same. Region growing is simplest
in region-base image segmentation method. The
concept of region growing algorithm is check the
neighbouring pixels of the initial seed points, then
determine whether those neighbouring pixels are
added to the seed points or not.The resultant image is
as shown in fig.3
Fig.3. segmented image
D. Shadow Detection
Here main focus is only on the shadows in
the cast shadow area ofremote sensing images.
Therelationship between shadows and it regions is
formalized in order to derive relevant shadow
properties.
i. Spectral properties of shadows
To describe the spectral appearance [7] of a
surface in shadow, let us consider the physics of
colour generation. The appearance of a surface is the
result of interaction among illumination, surface
reflectance properties, and responses of a chromatic
mechanism. This chromatic mechanism is composed
of three colour filters in a colour camera. Ambient
light can have different spectral characteristics with
respect to direct light. The case of outdoor scenes,
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where the diffuse light from the sky differs in spectral
composition with respect to the direct light from sun.
ii. Geometrical properties of shadows:
The geometric appearance [7] of a shadow
depends on objects and scene layout. However, it is
possible to identify some geometrical characteristics
of shadows, theshadow boundaries, without any
knowledge of the structure of the object or of the
scene. Shadow boundaries can be classified into four
classes: shadow-making lines, shadow lines,
occluding lines, and hidden shadow lines. These lines
are depicted in Fig.4.
Fig.4.Shadow lines definition.
iii. Detection of Shadow Areas:
In this method,shadow feature is extracted with
region growing threshold method.Then object
properties such as spectral features and geometric
features are combined with a spatial relationship, in
which the false shadows are detected and eliminated.
Researchers have used several different methods
to find the threshold to accurately separate shadow
and non-shadow areas.By the method region growing
thresholding, detection of shadow and non-shadow
areas and correctly separating the regions that have
same properties are easily carried out.In the
Histogram splitting provides a feasible way to find
the threshold for shadow. In the histogram
averageof the two peaks is adopted as the threshold.
The shadow objects are found by comparing the
threshold and grayscale average of each object
obtained in segmentation. In addition, atmospheric
molecules scatter the blue wavelength most among
the visible rays (Rayleigh scattering). So for the same
object, when in shadow and non-shadow, its
grayscale difference at the red and green wavebands
is more noticeable than at the blue waveband. Thus, a
suspected shadow is retrieved with the threshold
method at the red and green wavebands. Specifically,
an object is determined to be a suspected shadow if
itsgrayscale average is less than the thresholds in
both red and green wavebands. The resultant image
as shown in fig.5.
Fig.5. shadow detection
E. ELIMINATION OF FALSE SHADOWS
Dark objects may be included in the suspected
shadows, so more accurate shadow detection results
are needed to eliminate these dark objects. Rayleigh
scattering results in a smaller grayscale difference
between a shadow area and a non-shadow area in the
blue (B) waveband than in the red (R) and green (G)
wavebands. Consequently, for the majority of
shadows, the grayscale average at the blue waveband
Gbis slightly larger than the grayscale average at the
green waveband Gg. Also, the properties of green
vegetation itself make Ggsignificantly larger than Gb,
so false shadows from vegetation can be ruled out by
comparing the Gb and Ggof all suspected shadows.
Namely, for the object i, when Gb+ Ga <Gg, ican be
defined to be vegetation and be ruled out. Ga is the
correction parameter determined by the image type.
After the elimination of false shadows from
vegetation, spatial information of objects, i.e.,
geometrical characteristics and the spatial
relationship between objects is used to rule out other
dark objects from the suspected shadows. Lakes,
ponds, and rivers all have specific areas, shapes, and
other geometrical characteristics. Most bodies of
water can be ruled out due to the area and shape of
the suspected shadows of the object that they
produce. However, the aforementioned method still
cannot separate shadows from some other dark
objects. Spatial relationship features are used to rule
out dark objects in the suspected shadows. Dark
objects are substantive objects, while shadows are
created by taller objects which block the light sources
and may be linked together with the objects that
result in the shadows. An obscured area (i.e., a
shadow) forms a darker area in an image. The object
blocking the light forms a lighter area in an image. At
the same time, the sun has a definite altitude angle,
and a shadow boundary reflects the boundary of a
building and the position of a light source. Buildings,
trees, and telegraph poles are the main objects
creating shadows in urban remote sensing images.
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Their shadow boundaries usually have a certain
direction. To retrieve shadows using spatial
relationships, the linear boundaries of suspected
shadows are first analysed to predict the probable
trend of a shadow, according to which the
approximate position of a large object is predicted.
To determine whether it is a shadow, the proximity of
a dark object to a light object within this azimuth is
measured. An average spectral difference can be used
to decide whether there are light objects linked
around a shadow.After elimination of false shadows
the image is as shown in fig.6
Fig.6. elimination of false shadows
F. Inner and outer outlines:
To recover the shadow areas in an image, we use
a shadow removal method based on IOOPL [3]
matching. There is a large probability that both
shadow and non-shadow areas in close range on both
sides of the shadow boundary belong to the same
type of object. The inner and outer outlines can be
obtained by contacting the shadow boundary inward
and expanding it outward, respectively. Then, the
inner and outer outline profile lines are generated
along the inner and outer outline lines to determine
the radiation features of the same type of object on
both sides. As shown in Fig. 7, R is the vector line of
the shadow boundary obtained from shadow
detection, R1 is the outer outline in the non-shadow
area after expanding R outward, and R2 is the inner
outline in the shadow area after contracting R inward.
There is a one-to-one correspondence between nodes
on R1 and R2. When the correlation between R1 and
R2 is close enough, there is a large probability that
this location belongs to the same type of object. The
grayscale value of the corresponding nodes along R1
and R2 at each waveband is collected to obtain the
IOOPL. The outer profile lines (OPLs) in the shadow
area are marked as inner OPLs; OPLs in the non-
shadow area are marked as outer OPLs (Fig. 7). The
objects on both sides of the shadow boundary linked
with a building forming a shadow are usually not
homogeneous, and the corresponding inner and outer
outline profile line sections are not reliable. In
addition, the abnormal sections on the inner and outer
outlines that cannot represent homogeneous objects
need to be ruled out. Consequently, similarity
matching needs to be applied to the IOOPL section
by section to rule out the two kinds of non-
homogeneous sections mentioned previously. The
parameters for shadow removal are obtained by
analysing the grayscale distribution characteristics of
the inner and outer homogeneous IOOPL sections.
Fig.7. Diagram of shadow boundary, inner, and outer
outline lines.
Fig.8. IOOPL Matching
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G. IOOPL Matching:
IOOPL matching is a process of obtaining
homogeneous sections by conducting similarity
matching to the IOOPL section by sectionis shown in
fig.8. Gaussian smoothing is used to simplify the
view of IOOPL. The Gaussian smoothing template
parameters were σ = 2 and n = 11.To rule out the
non-homogeneous sections, IOOPL is divided into
average sections with the same standard, and then,
the similarity of each line pair is calculated section by
section. If correlation coefficient is large, it means
that the shade and light fluctuation features of the
IOOPL line pair at this section are consistent. If
consistent, then this line pair belongs to same type of
object, with different illuminations, and thus is
considered to be matching. If correlation coefficient
is small, then some abnormal parts representing some
different types of objects exist in this section.
III. Implementation of shadow removal
Shadows are removed by using the homogeneous
sections obtained by line pair matching. There are
two approaches for shadow removal. One approach
calculates radiation parameter according to the
homogeneous points of each object and then applies
the relative radiation correction to each object. The
other approach collects and analyses all
homogeneous sections for polynomial fitting (PF)
and retrieves all shadows directly with the obtained
fitting parameters.
Evaluation Criteria
Relative radiation correction generally assumes
that a linear relationship exists between the grayscale
value digital number (DN) of the image to be
corrected and the DN of the reference image
DNref = a × DNrect + b.
DNref is the DN of the object in the reference image,
DNrect is the DN of the object in the image to be
corrected, and a and b are the gain and offset
respectively.
The gain and offset of the linear function can be
estimated by the DN of the homogeneous sections.
DNrect is the DN of the outer homogeneous sections,
and DNrect is the DN of inner homogeneous
sections. The radiation correction coefficients of the
mean and variance method are
where is the grayscale average of the inner
homogeneous sections at the waveband k, is the
grayscale average of the outer homogeneous sections
at the waveband k, is the standard deviation of the
inner homogeneous sections at the corresponding
waveband, and is the standard deviation of the
outer homogeneous sections at the corresponding
waveband. All points of the shadow are corrected
according to
DNnonshadow = × DNshadow + ,
where DNnonshadow stands for the pixel gray scale
of the Shadow after correction, DNshadow stands for
the pixel gray scale of the shadow before correction,
and and are the coefficients of the minimum and
maximum method or mean variance method
calculated with the homogeneous points of the object.
In PF, the grayscale value of the shadow area is
directly obtained with the fitting parameters, as
shown in
f(x) = ax3 + bx2 + cx + d
After transforming the grey scale of the shadow
area through f(x), the shadow removal result can be
obtainedand is shown in fig.9.
Fig.9. Resultant image
Analyse shadow and removal using mean and
standard deviation as shown in below.
Table(1) sample analysis
Mean
Standard
deviation
Original image 115.05 59.3875
Recovered image 139.03 48.0605
IV. CONCLUSION
This paper proposed a forward method for
shadow detection and removal in a single urban high
resolution remote sensing images is REGION
GROWING ALGORITHM for detecting shadow in
images. Based on this methodto check neighbouring
pixels of the initial seed points and then determine
whether those neighbouring pixels are added to the
region or not by this we can eliminate false shadows.
For shadow removal based on IOOPLmatching can
effectively restore the information in a shadow area.
The homogeneous sections obtained by IOOPL
matching can show the radiation grey scale of the
same object in a shadow area and a non-shadow area.
The parameters calculated by using the radiation
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difference between inner and outer homogeneous
sections can retrieve a shadow very effectively.
Further improvements are needed in the
following ways.
Region growing algorithm is better than previous
methods for clear edges with good segmentation
results. But still exist it power consumption and time
consuming.Because of the filming environment or
some other reasons, obvious colour cast can be seen
in some parts of a shadow area. IOOPL matching
could relieve this case to a certain extent but not
completely resolve the problem.
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