In real-world applications, service robots need to locate and identify objects in a scene. A range sensor provides a robust estimate of depth information, which is useful to accurately locate objects in a scene. On the other hand, color information is an important property for object recognition task. The objective of this paper is to detect and localize multiple objects within an image using both range and color features. The proposed method uses 3D shape features to generate promising hypotheses within range images and verifies these hypotheses by using features obtained from both range and color images.
Object detection for service robot using range and color features of an imageIJCSEA Journal
In real-world applications, service robots need to locate and identify objects in a scene. A range sensor
provides a robust estimate of depth information, which is useful to accurately locate objects in a scene. On
the other hand, color information is an important property for object recognition task. The objective of this
paper is to detect and localize multiple objects within an image using both range and color features. The
proposed method uses 3D shape features to generate promising hypotheses within range images and
verifies these hypotheses by using features obtained from both range and color images.
Multimedia content based retrieval in digital librariesMazin Alwaaly
This document provides an overview of content-based image retrieval (CBIR) systems. It discusses early CBIR systems and provides a case study of C-BIRD, a CBIR system that uses features like color histograms, color layout, texture analysis, and object models to perform image searches. It also covers quantifying search results, key technologies in current CBIR systems such as robust image features, relevance feedback, and visual concept search, and the role of users in interactive CBIR systems.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This document presents research on content-based image retrieval using color and texture features. It proposes using both quadratic distance based on color histograms to measure color similarity, and pyramid structure wavelet transforms and gray level co-occurrence matrix (GLCM) to measure texture. For color features, quadratic distance is calculated between color histograms to retrieve similar images based on color. For texture, pyramid structure wavelet transforms are used to decompose images into sub-bands and calculate energy levels, while GLCM extracts texture statistics. The methods are evaluated on a dataset of 10000 images and results show the integrated approach of color and texture features provides more accurate and faster retrieval compared to individual features.
APPEARANCE-BASED REPRESENTATION AND RENDERING OF CAST SHADOWSijcga
This document presents an appearance-based method for representing and rendering cast shadows without explicit geometric modeling. A cubemap-like illumination array is constructed to sample shadow images on a plane. The sampled object and shadow images are represented using Haar wavelets. This allows rendering of shadows onto an arbitrary 3D background by linearly combining the wavelet basis images based on the scene geometry and lighting. Experiments demonstrate soft, realistic shadows can be rendered this way under novel illumination distributions specified by environment maps.
IMAGE SEGMENTATION BY USING THRESHOLDING TECHNIQUES FOR MEDICAL IMAGEScseij
Image binarization is the process of separation of pixel values into two groups, black as background and
white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This
paper describes a locally adaptive thresholding technique that removes background by using local mean
and standard deviation. Most common and simplest approach to segment an image is using thresholding.
In this work we present an efficient implementation for threshoding and give a detailed comparison of
Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is
implemented on medical images. The quality of segmented image is measured by statistical parameters:
Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).
Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retriev...IJERA Editor
This document evaluates the performance of the Euclidean and Manhattan distance metrics in a content-based image retrieval system. It finds that the Manhattan distance metric showed better precision than the Euclidean distance metric. The system uses color histograms and Gabor texture features to represent images. Color is represented in HSV color space and histograms of hue, saturation and value are used. Gabor filters are applied to capture texture at different scales and orientations. Distance between feature vectors is calculated using Euclidean and Manhattan distance formulas to find similar images from the database. The system was tested on a dataset of 1000 Corel images and Manhattan distance produced more relevant search results.
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.
Object detection for service robot using range and color features of an imageIJCSEA Journal
In real-world applications, service robots need to locate and identify objects in a scene. A range sensor
provides a robust estimate of depth information, which is useful to accurately locate objects in a scene. On
the other hand, color information is an important property for object recognition task. The objective of this
paper is to detect and localize multiple objects within an image using both range and color features. The
proposed method uses 3D shape features to generate promising hypotheses within range images and
verifies these hypotheses by using features obtained from both range and color images.
Multimedia content based retrieval in digital librariesMazin Alwaaly
This document provides an overview of content-based image retrieval (CBIR) systems. It discusses early CBIR systems and provides a case study of C-BIRD, a CBIR system that uses features like color histograms, color layout, texture analysis, and object models to perform image searches. It also covers quantifying search results, key technologies in current CBIR systems such as robust image features, relevance feedback, and visual concept search, and the role of users in interactive CBIR systems.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This document presents research on content-based image retrieval using color and texture features. It proposes using both quadratic distance based on color histograms to measure color similarity, and pyramid structure wavelet transforms and gray level co-occurrence matrix (GLCM) to measure texture. For color features, quadratic distance is calculated between color histograms to retrieve similar images based on color. For texture, pyramid structure wavelet transforms are used to decompose images into sub-bands and calculate energy levels, while GLCM extracts texture statistics. The methods are evaluated on a dataset of 10000 images and results show the integrated approach of color and texture features provides more accurate and faster retrieval compared to individual features.
APPEARANCE-BASED REPRESENTATION AND RENDERING OF CAST SHADOWSijcga
This document presents an appearance-based method for representing and rendering cast shadows without explicit geometric modeling. A cubemap-like illumination array is constructed to sample shadow images on a plane. The sampled object and shadow images are represented using Haar wavelets. This allows rendering of shadows onto an arbitrary 3D background by linearly combining the wavelet basis images based on the scene geometry and lighting. Experiments demonstrate soft, realistic shadows can be rendered this way under novel illumination distributions specified by environment maps.
IMAGE SEGMENTATION BY USING THRESHOLDING TECHNIQUES FOR MEDICAL IMAGEScseij
Image binarization is the process of separation of pixel values into two groups, black as background and
white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This
paper describes a locally adaptive thresholding technique that removes background by using local mean
and standard deviation. Most common and simplest approach to segment an image is using thresholding.
In this work we present an efficient implementation for threshoding and give a detailed comparison of
Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is
implemented on medical images. The quality of segmented image is measured by statistical parameters:
Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).
Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retriev...IJERA Editor
This document evaluates the performance of the Euclidean and Manhattan distance metrics in a content-based image retrieval system. It finds that the Manhattan distance metric showed better precision than the Euclidean distance metric. The system uses color histograms and Gabor texture features to represent images. Color is represented in HSV color space and histograms of hue, saturation and value are used. Gabor filters are applied to capture texture at different scales and orientations. Distance between feature vectors is calculated using Euclidean and Manhattan distance formulas to find similar images from the database. The system was tested on a dataset of 1000 Corel images and Manhattan distance produced more relevant search results.
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.
Features image processing and ExtactionAli A Jalil
This document discusses various techniques for extracting features and representing shapes from images, including:
1. External representations based on boundary properties and internal representations based on texture and statistical moments.
2. Principal component analysis (PCA) is mentioned as a statistical method for feature extraction.
3. Feature vectors are described as arrays that encode measured features of an image numerically, symbolically, or both.
The document discusses content-based image retrieval (CBIR) systems. It describes how CBIR systems use feature extraction to search large image databases based on visual content. The key components of CBIR systems are feature extraction, indexing, and system design. Feature extraction involves extracting information about images' colors, textures, shapes, and spatial locations. Effective features and indexing techniques are needed to make CBIR scalable for large image collections. Performance is evaluated based on how well systems return relevant images.
APPLYING R-SPATIOGRAM IN OBJECT TRACKING FOR OCCLUSION HANDLINGsipij
Object tracking is one of the most important problems in computer vision. The aim of video tracking is to extract the trajectories of a target or object of interest, i.e. accurately locate a moving target in a video sequence and discriminate target from non-targets in the feature space of the sequence. So, feature descriptors can have significant effects on such discrimination. In this paper, we use the basic idea of many trackers which consists of three main components of the reference model, i.e., object modeling, object detection and localization, and model updating. However, there are major improvements in our system. Our forth component, occlusion handling, utilizes the r-spatiogram to detect the best target candidate. While spatiogram contains some moments upon the coordinates of the pixels, r-spatiogram computes region-based compactness on the distribution of the given feature in the image that captures richer features to represent the objects. The proposed research develops an efficient and robust way to keep tracking the object throughout video sequences in the presence of significant appearance variations and severe occlusions. The proposed method is evaluated on the Princeton RGBD tracking dataset considering sequences with different challenges and the obtained results demonstrate the effectiveness of the proposed method.
Hybrid Technique for Copy-Move Forgery Detection Using L*A*B* Color Space IJEEE
Copy-move forgery is applied on an image to hide a region or an object. Most of the detection techniques either use transform domain or spatial domain information to detect the forgery. This paper presents a hybrid method to detect the forgery making use of both the domains i.e. transform domain in whichSVD is used to extract the useful information from image and spatial domain in which L*a*b* color space is used. Here block based approach and lexicographical sorting is used to group matching feature vectors. Obtained experimental results demonstrate that proposed method efficiently detects copy-move forgery even when post-processing operations like blurring, noise contamination, and severe lossy compression are applied.
The document discusses image segmentation techniques. It begins by defining segmentation as partitioning an image into distinct regions that correlate with objects or features of interest. The goal of segmentation is to find meaningful groups of pixels. Several segmentation techniques are described, including region growing/shrinking, clustering methods, and boundary detection. Region growing uses homogeneity tests to merge neighboring regions, while clustering divides space based on similarity within groups. Boundary detection finds boundaries between objects. The document provides examples and details of applying these segmentation methods.
Comparative study on image segmentation techniquesgmidhubala
This document discusses various image processing and analysis techniques. It describes image segmentation as separating an image into meaningful parts to facilitate analysis. Common segmentation techniques mentioned include thresholding, edge detection, color-based segmentation, and histograms. Thresholding involves separating foreground and background using a threshold value. Edge detection finds edges and contours. Color segmentation extracts information based on color. Histograms locate clusters of pixels to distinguish regions. The document provides examples of applying these techniques and concludes that segmentation partitions an image into homogeneous regions to extract high-level information.
This document summarizes techniques for image segmentation based on global thresholding and gradient-based edge detection. It discusses image segmentation, approaches like thresholding and edge detection in MATLAB. Thresholding is demonstrated on sample images to extract objects at different threshold values. Edge detection is also shown using Sobel filters. Issues like segmenting similar objects and boundary detection in the presence of noise are mentioned.
Automated Colorization of Grayscale Images Using Texture DescriptorsIDES Editor
The document proposes a novel automated process called ACTD for colorizing grayscale images using texture descriptors without human intervention. It analyzes sample color images to extract coherent texture regions, then uses Gabor filtering for texture-based segmentation. Texture descriptors and color information are computed and stored for each region. These are then used to colorize a new grayscale image based on texture matching. The method combines techniques such as Gabor filtering, fuzzy C-means clustering with a new "Gki factor" for noise tolerance, and content-based image retrieval of texture descriptors. Preliminary results found the approach viable but improvements were needed for scale and rotation invariance.
Noise tolerant color image segmentation using support vector machineeSAT 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.
Content-based Image Retrieval Using The knowledge of Color, Texture in Binary...Zahra Mansoori
This document presents a new approach for content-based image retrieval that combines color, texture, and a binary tree structure to describe images and their features. Color histograms in HSV color space and wavelet texture features are extracted as low-level features. A binary tree partitions each image into regions based on color and represents higher-level spatial relationships. The performance of the proposed system is evaluated on a subset of the COREL image database and compared to the SIMPLIcity image retrieval system. Experimental results show the proposed system has better retrieval performance than SIMPLIcity in some categories and comparable performance in others.
Content based image retrieval using features extracted from halftoning-based ...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Visual Hull Construction from Semitransparent Coloured Silhouettes ijcga
This paper attempts to create coloured semi-transparent shadow images that can be projected onto
multiple screens simultaneously from different viewpoints. The inputs to this approach are a set of coloured
shadow images and view angles, projection information and light configurations for the final projections.
We propose a method to convert coloured semi-transparent shadow images to a 3D visual hull. A
shadowpix type method is used to incorporate varying ratio RGB values for each voxel. This computes the
desired image independently for each viewpoint from an arbitrary angle. An attenuation factor is used to
curb the coloured shadow images beyond a certain distance. The end result is a continuous animated
image that changes due to the rotated projection of the transparent visual hull.
Visual Hull Construction from Semitransparent Coloured Silhouettes ijcga
This paper attempts to create coloured semi-transparent shadow images that can be projected onto multiple screens simultaneously from different viewpoints. The inputs to this approach are a set of coloured shadow images and view angles, projection information and light configurations for the final projections. We propose a method to convert coloured semi-transparent shadow images to a 3D visual hull. A
shadowpix type method is used to incorporate varying ratio RGB values for each voxel. This computes the
desired image independently for each viewpoint from an
arbitrary angle. An attenuation factor is used to
curb the coloured shadow images beyond a certain distance. The end result is a continuous animated image that changes due to the rotated projection of the transparent visual hull.
This document summarizes an evaluation of texture feature extraction methods for content-based image retrieval, including co-occurrence matrices, Tamura features, and Gabor filters. The evaluation tested these methods on a Corel image collection using Manhattan distance as the similarity measure. Co-occurrence matrices performed best with homogeneity as the feature, while Gabor wavelets showed better performance for homogeneous textures of fixed sizes. Tamura features performed poorly with directionality. Overall, co-occurrence matrices provided the best results for general texture retrieval.
1. The document presents an image segmentation algorithm that uses local thresholding in the YCbCr color space.
2. It computes local thresholds for each pixel by calculating the mean and standard deviation of neighboring pixels in a 3x3 mask. The threshold is used to label each pixel as 1 or 0.
3. The algorithm was tested on images with objects indistinct and distinct from the background. It performed well in segmenting objects from the background in both cases. There is potential to improve performance for blurred images.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
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.
An effective RGB color selection for complex 3D object structure in scene gra...IJECEIAES
Our goal of the project is to develop a complete, fully detailed 3D interactive model of the human body and systems in the human body, and allow the user to interacts in 3D with all the elements of that system, to teach students about human anatomy. Some organs, which contain a lot of details about a particular anatomy, need to be accurately and fully described in minute detail, such as the brain, lungs, liver and heart. These organs are need have all the detailed descriptions of the medical information needed to learn how to do surgery on them, and should allow the user to add careful and precise marking to indicate the operative landmarks on the surgery location. Adding so many different items of information is challenging when the area to which the information needs to be attached is very detailed and overlaps with all kinds of other medical information related to the area. Existing methods to tag areas was not allowing us sufficient locations to attach the information to. Our solution combines a variety of tagging methods, which use the marking method by selecting the RGB color area that is drawn in the texture, on the complex 3D object structure. Then, it relies on those RGB color codes to tag IDs and create relational tables that store the related information about the specific areas of the anatomy. With this method of marking, it is possible to use the entire set of color values (R, G, B) to identify a set of anatomic regions, and this also makes it possible to define multiple overlapping regions.
The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pair wise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.
Features image processing and ExtactionAli A Jalil
This document discusses various techniques for extracting features and representing shapes from images, including:
1. External representations based on boundary properties and internal representations based on texture and statistical moments.
2. Principal component analysis (PCA) is mentioned as a statistical method for feature extraction.
3. Feature vectors are described as arrays that encode measured features of an image numerically, symbolically, or both.
The document discusses content-based image retrieval (CBIR) systems. It describes how CBIR systems use feature extraction to search large image databases based on visual content. The key components of CBIR systems are feature extraction, indexing, and system design. Feature extraction involves extracting information about images' colors, textures, shapes, and spatial locations. Effective features and indexing techniques are needed to make CBIR scalable for large image collections. Performance is evaluated based on how well systems return relevant images.
APPLYING R-SPATIOGRAM IN OBJECT TRACKING FOR OCCLUSION HANDLINGsipij
Object tracking is one of the most important problems in computer vision. The aim of video tracking is to extract the trajectories of a target or object of interest, i.e. accurately locate a moving target in a video sequence and discriminate target from non-targets in the feature space of the sequence. So, feature descriptors can have significant effects on such discrimination. In this paper, we use the basic idea of many trackers which consists of three main components of the reference model, i.e., object modeling, object detection and localization, and model updating. However, there are major improvements in our system. Our forth component, occlusion handling, utilizes the r-spatiogram to detect the best target candidate. While spatiogram contains some moments upon the coordinates of the pixels, r-spatiogram computes region-based compactness on the distribution of the given feature in the image that captures richer features to represent the objects. The proposed research develops an efficient and robust way to keep tracking the object throughout video sequences in the presence of significant appearance variations and severe occlusions. The proposed method is evaluated on the Princeton RGBD tracking dataset considering sequences with different challenges and the obtained results demonstrate the effectiveness of the proposed method.
Hybrid Technique for Copy-Move Forgery Detection Using L*A*B* Color Space IJEEE
Copy-move forgery is applied on an image to hide a region or an object. Most of the detection techniques either use transform domain or spatial domain information to detect the forgery. This paper presents a hybrid method to detect the forgery making use of both the domains i.e. transform domain in whichSVD is used to extract the useful information from image and spatial domain in which L*a*b* color space is used. Here block based approach and lexicographical sorting is used to group matching feature vectors. Obtained experimental results demonstrate that proposed method efficiently detects copy-move forgery even when post-processing operations like blurring, noise contamination, and severe lossy compression are applied.
The document discusses image segmentation techniques. It begins by defining segmentation as partitioning an image into distinct regions that correlate with objects or features of interest. The goal of segmentation is to find meaningful groups of pixels. Several segmentation techniques are described, including region growing/shrinking, clustering methods, and boundary detection. Region growing uses homogeneity tests to merge neighboring regions, while clustering divides space based on similarity within groups. Boundary detection finds boundaries between objects. The document provides examples and details of applying these segmentation methods.
Comparative study on image segmentation techniquesgmidhubala
This document discusses various image processing and analysis techniques. It describes image segmentation as separating an image into meaningful parts to facilitate analysis. Common segmentation techniques mentioned include thresholding, edge detection, color-based segmentation, and histograms. Thresholding involves separating foreground and background using a threshold value. Edge detection finds edges and contours. Color segmentation extracts information based on color. Histograms locate clusters of pixels to distinguish regions. The document provides examples of applying these techniques and concludes that segmentation partitions an image into homogeneous regions to extract high-level information.
This document summarizes techniques for image segmentation based on global thresholding and gradient-based edge detection. It discusses image segmentation, approaches like thresholding and edge detection in MATLAB. Thresholding is demonstrated on sample images to extract objects at different threshold values. Edge detection is also shown using Sobel filters. Issues like segmenting similar objects and boundary detection in the presence of noise are mentioned.
Automated Colorization of Grayscale Images Using Texture DescriptorsIDES Editor
The document proposes a novel automated process called ACTD for colorizing grayscale images using texture descriptors without human intervention. It analyzes sample color images to extract coherent texture regions, then uses Gabor filtering for texture-based segmentation. Texture descriptors and color information are computed and stored for each region. These are then used to colorize a new grayscale image based on texture matching. The method combines techniques such as Gabor filtering, fuzzy C-means clustering with a new "Gki factor" for noise tolerance, and content-based image retrieval of texture descriptors. Preliminary results found the approach viable but improvements were needed for scale and rotation invariance.
Noise tolerant color image segmentation using support vector machineeSAT 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.
Content-based Image Retrieval Using The knowledge of Color, Texture in Binary...Zahra Mansoori
This document presents a new approach for content-based image retrieval that combines color, texture, and a binary tree structure to describe images and their features. Color histograms in HSV color space and wavelet texture features are extracted as low-level features. A binary tree partitions each image into regions based on color and represents higher-level spatial relationships. The performance of the proposed system is evaluated on a subset of the COREL image database and compared to the SIMPLIcity image retrieval system. Experimental results show the proposed system has better retrieval performance than SIMPLIcity in some categories and comparable performance in others.
Content based image retrieval using features extracted from halftoning-based ...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Visual Hull Construction from Semitransparent Coloured Silhouettes ijcga
This paper attempts to create coloured semi-transparent shadow images that can be projected onto
multiple screens simultaneously from different viewpoints. The inputs to this approach are a set of coloured
shadow images and view angles, projection information and light configurations for the final projections.
We propose a method to convert coloured semi-transparent shadow images to a 3D visual hull. A
shadowpix type method is used to incorporate varying ratio RGB values for each voxel. This computes the
desired image independently for each viewpoint from an arbitrary angle. An attenuation factor is used to
curb the coloured shadow images beyond a certain distance. The end result is a continuous animated
image that changes due to the rotated projection of the transparent visual hull.
Visual Hull Construction from Semitransparent Coloured Silhouettes ijcga
This paper attempts to create coloured semi-transparent shadow images that can be projected onto multiple screens simultaneously from different viewpoints. The inputs to this approach are a set of coloured shadow images and view angles, projection information and light configurations for the final projections. We propose a method to convert coloured semi-transparent shadow images to a 3D visual hull. A
shadowpix type method is used to incorporate varying ratio RGB values for each voxel. This computes the
desired image independently for each viewpoint from an
arbitrary angle. An attenuation factor is used to
curb the coloured shadow images beyond a certain distance. The end result is a continuous animated image that changes due to the rotated projection of the transparent visual hull.
This document summarizes an evaluation of texture feature extraction methods for content-based image retrieval, including co-occurrence matrices, Tamura features, and Gabor filters. The evaluation tested these methods on a Corel image collection using Manhattan distance as the similarity measure. Co-occurrence matrices performed best with homogeneity as the feature, while Gabor wavelets showed better performance for homogeneous textures of fixed sizes. Tamura features performed poorly with directionality. Overall, co-occurrence matrices provided the best results for general texture retrieval.
1. The document presents an image segmentation algorithm that uses local thresholding in the YCbCr color space.
2. It computes local thresholds for each pixel by calculating the mean and standard deviation of neighboring pixels in a 3x3 mask. The threshold is used to label each pixel as 1 or 0.
3. The algorithm was tested on images with objects indistinct and distinct from the background. It performed well in segmenting objects from the background in both cases. There is potential to improve performance for blurred images.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
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.
An effective RGB color selection for complex 3D object structure in scene gra...IJECEIAES
Our goal of the project is to develop a complete, fully detailed 3D interactive model of the human body and systems in the human body, and allow the user to interacts in 3D with all the elements of that system, to teach students about human anatomy. Some organs, which contain a lot of details about a particular anatomy, need to be accurately and fully described in minute detail, such as the brain, lungs, liver and heart. These organs are need have all the detailed descriptions of the medical information needed to learn how to do surgery on them, and should allow the user to add careful and precise marking to indicate the operative landmarks on the surgery location. Adding so many different items of information is challenging when the area to which the information needs to be attached is very detailed and overlaps with all kinds of other medical information related to the area. Existing methods to tag areas was not allowing us sufficient locations to attach the information to. Our solution combines a variety of tagging methods, which use the marking method by selecting the RGB color area that is drawn in the texture, on the complex 3D object structure. Then, it relies on those RGB color codes to tag IDs and create relational tables that store the related information about the specific areas of the anatomy. With this method of marking, it is possible to use the entire set of color values (R, G, B) to identify a set of anatomic regions, and this also makes it possible to define multiple overlapping regions.
The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pair wise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.
Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Lev...CSCJournals
The document describes an image segmentation algorithm that uses both color and depth features extracted from RGBD images captured by a Kinect sensor. The algorithm clusters pixels into segments based on their color, texture, 3D spatial coordinates, surface normals, and the output of a graph-based segmentation algorithm. Depth features help resolve illumination issues and occlusion that cannot be handled by color-only methods. The algorithm was tested on commercial building images and showed potential for real-time applications.
Implementation of High Dimension Colour Transform in Domain of Image ProcessingIRJET Journal
This document discusses implementing a high-dimensional color transform method for salient region detection in images. It aims to extract salient regions by designing a saliency map using global and local image features. The creation of the saliency map involves mapping colors from RGB space to a high-dimensional color space to find an optimal linear combination of color coefficients. This allows composing an accurate saliency map. The performance is further improved by using relative location and color contrast between superpixels as features and resolving the saliency estimation from an initial trimap using a learning-based algorithm. The method is analyzed on a dataset of training images.
Semi-Supervised Method of Multiple Object Segmentation with a Region Labeling...sipij
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Object Detection for Service Robot Using Range and Color Features of an Image
1. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.3, June 2014
DOI : 10.5121/ijcsea.2014.4302 17
OBJECT DETECTION FOR SERVICE ROBOT USING
RANGE AND COLOR FEATURES OF AN IMAGE
Dipankar Das
Department of Information and Communication Engineering, University of Rajshahi,
Rajshahi-6205, Bangladesh
ABSTRACT
In real-world applications, service robots need to locate and identify objects in a scene. A range sensor
provides a robust estimate of depth information, which is useful to accurately locate objects in a scene. On
the other hand, color information is an important property for object recognition task. The objective of this
paper is to detect and localize multiple objects within an image using both range and color features. The
proposed method uses 3D shape features to generate promising hypotheses within range images and
verifies these hypotheses by using features obtained from both range and color images.
KEYWORDS
Object Detection, Range Image, Generative Learning, Discriminative Learning
1. INTRODUCTION
The field of robot vision is developing rapidly as robots become more capable of operating with
people in natural human environments. For robots to be accepted in the home and in offices, they
need to identify specific or a general category of objects requested by the user. For this purpose,
most of the existing object recognition methods use either color image information [1] or range
image information [2]. Although the color information is useful for generic object recognition
purposes, the depth information is very useful for robots to accurately localize objects. In this
paper we propose a detection and localization technique for robot vision, which integrates both
range and color information of images to detect and localize multiple objects simultaneously. Our
approach consists of first generating a set of hypotheses for each object using a generative model
pLSA [10] with bag of visual words (BOVW) [15] representing 3D shape of each range image.
To generate more reliable hypotheses within a range image, we use a singular value
decomposition filter on the normal enhanced range images. Then, the discriminative part of our
system verifies each hypothesis using an SVM classifier with merging feature that combines both
3D shape and color appearance of the object. The color feature is extracted from the
corresponding hypothesis location in the color images.
Most of the previous 3D object recognition methods compare unknown range surface with the
models in the database and recognize as the one with the smallest metric distance [3, 4, 14].
These methods require a global model, which needs an extra surface modeling process from
multiple range images. In this paper, we propose an alternative solution using both generative and
discriminative learning, which measures the similarity between images using local and global
feature sets.
2. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.3, June 2014
18
2. RELATED WORKS
Object recognition with local and global feature characteristics has been an active research topic
in computer vision community [5]. Recent studies show that local surface descriptors are useful
tools for object recognition when occlusion happens [6, 7]. In [6], Li and Guskov proposed a
method to find salient keypoints on local surface of 3D objects and similarity has measured using
the pyramid kernel function. In [2], Chen and Bhanu have introduced an integrated local surface
descriptor for surface representation and 3D object recognition. However, previous methods have
been applied for only some 3D free-form objects with a great deal of surface variation in an
image without any background clutter. On the other hand, we propose a practical and reliable
detection and localization approach that can be operated on simple as well as on well defined 3D
objects in a real world background. Moreover, in our approach, we use local keypoints of range
images to generate hypotheses of objects and a combination of range and color features to verify
each hypothesis. In object extraction, some segmentation approaches have been proposed that
utilize range and/or color features of objects [8, 9]. The edge-region-based range image
segmentation approach proposed in [9] was limited to only three simple objects and is not
applicable for multiple complex objects within an image. On the other hand, in order to extract
the desired target object for robots Shibuya et al. [8] have proposed a method that uses both color
and range images. They use color-distance (CD) matting approach to segment the target object
from the scene. The method requires explicit user interaction to indicate the desired target object
through a touch sensitive panel. However, our approach automatically detects and localizes all
target objects in a scene using both generative and discriminative classifiers without any user
interaction.
In object extraction from range images, some segmentation based approaches have been proposed
that utilize edge features of objects. The edge-region-based range image segmentation approaches
proposed by the authors of [9,16], were limited to only three simple objects and are not applicable
to multiple complex objects within an image. A model based edge detection in range images of
piled box-like objects using modified scan line approximation technique has been proposed by
Katsoulas and Weber [17]. However, all of the previous methods use global edges of 3D range
images for objects extraction and obtain good results on some regular objects. Their reliance on
global edge properties makes them vulnerable to clutter and occlusion. In this paper, we propose a
new method to detect and localize multiple objects in a range image using local features, which
can tolerate partial occlusion and cluttered background.
3. OVERVIEW OF THE PROPOSED APPROACH
Our proposed approach for multiple object detection and localization is shown in Figure 1. In the
training stage, all the labeled training datasets containing multiple objects per image are presented
to the system. In the generative part, the pLSA model [10] is learned for multiple topics using the
bag-of-visual-words (BOVW) detected on the 3D range images. At the same time the SVM
classifier is learned using both color feature (3D color histogram) and range image’s features
(BOVW). During the testing phase, when a new test image is given, the system generates a set of
promising hypotheses with a bag of visual words using the pLSA model. Then we extract the 3D
color histogram from hypothesis locations and combine it with the BOVW. These merging
features and their corresponding locations are verified using the multi-class SVM classifier to
detect and localize multiple objects within an image.
3. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.3, June 2014
19
4. IMAGE DATA COLLECTION
We have constructed the sensor system for obtaining color and range images as shown in Figure
2(a). The acquisition devices consist of a Logicool USB camera for acquiring color images and a
Swiss laser ranger (SR- 4000) for obtaining range images. In our system, we have created a
common interface for acquiring both range and color images. Images were collected in complex
real-world backgrounds that contain multiple objects (Figure 2(b)-(c)). All range images were
acquired using the Swiss ranger, SR-4000, at a resolution 176(horizontal) × 144(vertical) in an
office environments.
Figure 1: Abstract view of the proposed approach
Figure 2: Experimental setup and example images: (a) image acquisition devices, (b) distance image, (c)
color image.
(a) (b) (c)
Range Sensor
USB Camera
4. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.3, June 2014
20
The resolution of the Logicool color camera was set to the same value as in the lager ranger. All
training images were annotated using our annotation tools. We determine the transformed
hypotheses locations on color images using the coordinate transformation technique as described
in section 5.
5. RANGE IMAGE PRE-PROCESSING
In the images of MESA Swiss ranger, SR-4000, the saturation noise occurred when the signal
amplitude or ambient light is too great as shown in Figure 3(a). In this case, the most significant
bit (MSB) of the measured pixel is flagged as ‘saturated’. This can be avoided by reducing the
integration time, increasing the distance or changing the angle of faces of reflective objects or if
necessary by shielding the scene from ambient light. However, in an image of a scene that
contains multiple objects, it is difficult to completely eliminate saturation noise. In the
preprocessing stage, our filtering method removes the saturation noise from the image (as shown
in Figure 3(b). Since the saturation noise sometimes occurs contiguously, the conventional
averaging or median filter is not appropriate for this purpose. In our filtering approach, instead of
searching over all the image regions, we only concentrate on the surroundings of the flagged
positions due to saturation noise. If (x, y) be a flagged position due to saturation noise, then our
weighted averaging filter of size m× n is given by:
∑ ∑
∑ ∑
−
= −
=
−
= −
=
+
+
∗
= s
s
i
t
t
j
s
s
i
t
t
j
j
i
w
j
y
i
x
f
j
i
w
y
x
g
)
,
(
)
,
(
)
,
(
)
,
( , (1)
where,
−
=
otherwise
pixel
flagged
is
pixel
th
j
i
if
j
i
w
1
)
,
(
0
)
,
( ,
m = 2s + 1, n = 2t + 1, and f is the pixel value at (x, y).
Figure 3: Image corrupted by saturation noise and result of filtering. (a) Range image with saturation noise,
and (b) filtered image.
(a) (b)
5. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.3, June 2014
21
6. 3D FEATURE EXTRACTION AND GENERATIVE LEARNING
In order to learn the generative model, pLSA, we first compute a co-occurrence table, where each
of the range images is represented by a collection of visual words, provided from a visual
vocabulary. This visual vocabulary is obtained by vector quantizing keypoint descriptors
computed from the training images using the k-means clustering algorithm. To extract visual
words, we first determine keypoints on the range image that are insensitive to changes in depth,
viewpoint, and rotation. These keypoints are detected on the corner points and uniformly sampled
object’s edges taking the edge strength as the weight of the sample. For this purpose, we construct
3D edges from range images and use singular value decomposition technique to eliminate noisy
edges from the edge map.
6.1. 3D Edge Map Construction
Here we summarize the technique to build the 3D edge map from range images. The edges of 3D
images are classified in two main categories [9, 13], representing depth value discontinuities, and
surface normal vector discontinuities. The edges corresponding to pixels where depth
discontinuity occurs are called jump edges or boundary edges. The second type of edges where
surface normal vector discontinuity occurs are called fold edges, or crease edges or roof edges. In
this research, edges of 3D range images are formed by combining both jump and fold edges as
shown in Figure 4.
Figure 4: 3D edge map construction from jump and fold edges.
Jump edges are detected using depth discontinuity of the images as shown in Figure 5(b).
Suppose Nx, Ny, and Nz be the surface normals for the surface defined by X, Y, and Z coordinates
of the 3D range image. These surface normals can be used to enhance a range image in particular
the edges. An image represented by the normal component is referred to as the normal enhanced
image. For example, Figure 5(c) shows the normal enhanced image represented by the normal
component Ny. Fold edges are detected using normal enhanced images with surface normal
discontinuity. However, the normal-based approach is sensitive to noise in the range data and
may corrupt the object surface while enhancing the edges. Thus, we use a singular value
decomposition filter (SVDF) to generate more reliable noise free fold edges.
Figure 5: 3D edge map for the object teapot. (a) An example range image represented by the
depth values, (b) detected jump edges, (c) image represented by the normal component Ny, (d) Ny
edge map, (e) Ny edge map after applying SVDF on (c), and (f) combined 3D edge map (b + e).
Range image Jump edges Fold edges 3D edge map
6. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.3, June 2014
22
Figure 6: 3D edge map for the object teapot. (a) An example range image represented by the depth values,
(b) detected jump edges, (c) image represented by the normal component Ny, (d) Ny edge map, (e) Ny edge
map after applying SVDF on (c), and (f) combined 3D edge map (b + e).
6.2. Singular Value Decomposition Filter (SVDF)
When we use a normal enhanced image to determine the edges, the edge map includes a great
deal of false edges as shown in Figure 5(d). In order to eliminate this effect, we use SVDF on the
normal enhanced images. To construct SVDF, we first create a normal enhanced image from the
range data and obtain the singular values }
,
,
,
{ 2
1 k
s
s
s
S
∈ . Since the resolution of the range
sensor is 176 (horizontal) × 144 (vertical), the matrix of normal enhanced image is of size 144
(row)×176 (column) and has full rank due to noise. Thus we have 144 singular values giving k =
144 in our experiment. The singular value decomposition technique produces singular values in
decreasing order: k
s
s
s >
>
>
2
1 . Then we normalize i
s by 1
/ s
si . The filtered image is
formed by taking the first n singular values such that
≥
n
s . It can be shown that 1
.
0
=
produces approximately noise free fold edges for our experiments (Figure 5(e)).
6.3. Keypoint Detection and Description
For 3D edge map distinct corner points are detected using the corner detection algorithm and all
of the corner points are selected as a set of keypoints (n1). The second set of keypoints (n2) is
obtained by sampling the 3D edge map. We use weighted uniform edge sampling technique to
determine another set of the keypoints (n2). Our total number of keypoints is n = n1 + n2. Figure 7
shows detected keypoints on the image, where blue and red points indicate the corner and edge
sampling keypoints, respectively. Each of the generated keypoint is described by: (i) the normal
components, and (ii) the orientation of edge map within the patch. The normal components Nx, Ny,
and Nz of the 3D surface normal are defined by X, Y , and Z coordinates of the range image. The
descriptor is a N × N square patch around each keypoint pixel for all of the three normal
(a) (b) (c)
(d) (e) (f)
7. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.3, June 2014
23
components and edge orientations. Thus, our descriptors represent the local 3D surface patch and
edge orientations around each of the keypoint. It is row reordered to form a vector in
2
4N dimensional feature space. The patch size tested for our experiment is N = 5 and 7.
Figure 7: Detected edges and keypoints on a range image.
6.4. Learning the pLSA Model
To learn generative model pLSA, we construct a codebook of feature types by clustering a set of
training keypoints according to their descriptors. For clustering, we use k-mean clustering
algorithm with codebook size of 300. The codebook is necessary to generate a BOVW histogram
for each training or testing image. Then the pLSA model is learned for a number of topics given
to the system using BOVW histograms constructed from the training dataset. The model
associates each observation of a visual word, w, within an object, o, with a topic variable
}
,
,
,
{ 2
1 k
z
z
z
Z
z
=
∈ . Here, our goal is to determine P(w|z) and P(z|o) by using the
maximum likelihood principle. The model is fitted for the entire training object without
knowledge of labels of bounding boxes. Then the topics are assigned based on the object specific
topic probability, )
|
( j
k o
z
P under each category.
7. COLOR FEATURE EXTRACTION AND DISCRIMINATIVE LEARNING
In our learning approach, along with pLSA, a multiclass SVM classifier is also learned using both
3D shape and color features. The 3D shape is represented by the histogram of visual words
extracted from the range image. Although shape representation is a good measure of objects
similarity for some objects, shape features are not sufficient enough to distinguish among all
types of objects. In this case, color appearance is a better feature to find the similarity between
them.
7.1. Color Feature Extraction
In order to determine color feature from color image, we compute 3D HSV global color
histograms from all training images collected from different environments in varying lighting
conditions. In the HSV color space, the quantization of hue requires the most attention. The hue
circle consists of the primary colors red, green and blue separated by
120 . A circular
quantization by
20 steps sufficiently separates the hues such that the three primaries and yellow,
magenta, and cyan are represented each with three sub-divisions. The saturation and value are
each quantized to three levels yielding greater perceptual tolerance along these dimensions. Thus
H is quantized to 18 levels, and S and V are quantized to 3 levels. The quantized HSV space has
18 × 3 × 3 = 162 histogram bins. The final histogram is normalized to sum to unity in order to fit
appropriately in our SVM kernel.
3D edge map Detected keypoints
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24
7.2. SVM Learning
We use the SVM to learn the discriminative classifier using merging feature. The combination of
both shape and color appearance features for an image I, are merged as:
)
(
)
(
)
( C
A
R
S I
H
I
H
I
H
+
= , (2)
where both α and β are weights for the shape histogram, HS(IR) and color appearance histogram,
HA(IC), respectively. The multi-class SVM classifier is learned using the above merging feature
giving the higher weight to the more discriminative feature. We use the LIBSVM [11] package
for our experiments in a multi-class mode with the rbf exponential kernel.
8. HYPOTHESIS GENERATION AND VERIFICATION
In recognition stage, promising hypotheses are generated within the range image for probable
object’s locations. For this purpose, visual words are extracted from the range image using the
same technique as described in subsection 4.3. Each visual word is classified under the topic with
the highest topic specific probability )
|
( k
i z
w
P . Then promising hypotheses are generated for
each of the object using the hypothesis generation algorithm of Das et al. [12]. In the verification
step, merging features are extracted from the regions of the image bounded by the windows of
promising hypotheses. The shape feature is obtained from the generated hypothesis area on the
range image. However, in order to obtain the color feature the corresponding hypothesis location
on the color image is detected using the following coordinate transformation
algorithm:
1. Using the ground truth bounding boxes of both range and color images of the training
data, calculate the X and Y coordinates of the range image for which X and Y coordinates
of the corresponding color image are equal and denote these coordinate by XCR and YCR,
respectively.
2. From both range and color images, determine the overlapping starting coordinates of the
color image with the range image and denote these coordinates by XSC and YSC.
3. Calculate the hypothesis coordinates location on the color image (XHC, YHC) using the
hypothesis coordinate of the range image (XHR, YHR) as:
if XHR ≤ XCR then
XHC ← XHR + XSC − (XSC/XCR) * XHR
else
XHC ← XHR − (XSC/XCR) * (XHR − XCR)
end if
4. Similarly calculate the YHC coordinate for hypothesis location of the color image.
The above algorithm determines the hypothesis windows on the color image using the detected
hypothesis windows on the range image as shown in Figure 8. Then, the merging features are
extracted from both range and color images and fed into the multi-class SVM classifier in the
recognition mode. Only the hypotheses for which a positive confidence measurement is returned
are kept for each object. Objects with the highest confidence level are detected as the correct
objects. The confidence level is measured using the probabilistic output of the SVM classifier.
9. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.3, June 2014
25
Figure 8: (a) Detected regions on a range image, and (b) corresponding regions on a color image.
9. EXPERIMENTAL RESULTS
The proposed technique has been used to carry out the detection and localization of multiple
objects within the range image. Since there is no standard database suitable for color-distance
feature based object recognition, we evaluated our system using our own database.
9.1. Database
Our color-distance database consists of images of ten specific objects grouped into two datasets.
The dataset-1 comprises of 251 images of five objects, namely, coffee-jar, spray-cleaner, toy-1,
video-camera, and stapler. Among 251 images 150 images (single object per image) are used for
training the model and the rest 101 images of 312 objects are used for testing the system. The
dataset-2 contains 230 images of another five objects, namely, coffee-mug, tape-holder, toy-2,
teapot and can. Among 230 images 150 images are again used for training the system and the
remaining 80 images are used for testing purposes.
9.2. Classification Results
We performed experiments to detect and localize multiple objects against complex real-world
background. In the training period, both the pLSA and SVM models are fitted for all training
objects. During recognition stage, given an unlabeled image of multiple objects, our main
objective is to automatically detect and localize all objects within the image. In our approach
object presence detection means determining if one or more objects are present in an image and
localization means finding the locations of objects in that image. The localization performance is
measured by comparing the detected window area with the ground truth object window. Based on
the object presence detection and localization, an object is counted as a true positive object if the
detected object boundary overlaps by 50% or more with the ground truth bounding box for that
object. Otherwise, the detected object is counted as false positive. In the experimental evaluation,
the detection and localization rate (DLR) is defined as:
objects
annotated
of
objects
positive
true
of
DLR
#
#
= (3)
objects
annotated
of
objects
positive
false
of
FPR
#
#
= (4)
(a) (b)
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26
Table 1. Experimental results on dataset-1.
Objects Detection rate for merging
feature without SVDF
Detection rate for merging
feature with SVDF
DLR FPR DLR FPR
Coffee jar 0.72 0.36 0.77 0.44
Spray cleaner 0.88 0.13 0.90 0.15
Toy-1 0.95 0.00 0.98 0.03
Video camera 0.69 0.31 0.79 0.40
Stapler 0.65 0.26 0.70 0.31
Average Rate 0.78 0.21 0.83 0.27
Table 2. Experimental results on dataset-2.
Objects Detection rate for merging
feature without SVDF
Detection rate for merging
feature with SVDF
DLR FPR DLR FPR
Coffee mug 0.74 0.05 0.79 0.13
Tape holder 0.74 0.26 0.79 0.23
Toy-2 0.97 0.62 0.97 0.70
Teapot 0.67 0.43 0.80 0.35
Can 0.84 0.14 0.96 0.12
Average Rate 0.79 0.30 0.87 0.30
Tables 1~2 show the detection and localization rates on our datasets for the ten objects. In the
experiments, the effect of Singular Value Decomposition Filter (SVDF) on the normal enhanced
image is also evaluated in terms recognition rate. It can be shown that the proposed method is
able to produce the average recognition accuracy of 78% and 79% for dataset-1 and dataset-2,
respectively. However, the method generates more accurate hypothesis on the image using the
SVDF. As a consequence, final recognition rate has significantly increased. On dataset-1 and
dataset-2 the final recognition accuracy are 83% and 87%, respectively.
The detection and localization results on datasets are shown in Figure 9. The blue rectangular
bounding box indicates the location of each object within an image. The first column of the
Figure 9 shows the detected target objects with their locations on the depth images and the
second column illustrates the corresponding object’s locations on the color images. The locations
on the color images are detected by our algorithm as discussed in section 5.
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10. CONCLUSIONS
Our system has shown the ability to accurately detect and localize multiple objects using both
range and color information. For this purpose, in the experiment, an optical camera and a laser
ranger are integrated. Our method is useful for service robots, because it can use 3D information
to know the exact position and pose of the target objects. Our local descriptors on edges generate
accurate hypotheses, which are then verified using more discriminative features with chi-square
merging kernel. Since we use scale invariant local properties, it is robust for partial occlusion,
background clutter, and significant depth changes. The detection rate and computational
efficiency suggest that our technique is suitable for real time use.
Current range sensors have problems with transparent and reflecting materials. Although our
approach can remove saturation noise due to reflection properties, our system does not work well
for transparent objects. In future, we will explore the possibility of detecting transparent objects
by integrating both range and color sensors, and using a combination of more robust features
from both sensors.
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Figure 9: Detection and localization results on range and color images.
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Authors
Dipankar Das received his B.Sc. and M.Sc. degree in Computer Science and
Technology from the University of Rajshahi, Rajshahi, Bangladesh in 1996 and 1997,
respectively. He also received his PhD degree in Computer Vision from Saitama
University Japan in 2010. He was a Postdoctoral fellow in Robot Vision from October
2011 to March 2014 at the same university. He is currently working as an ass ociate
professor of the Department of Information and Communication Engineering,
University of Rajshahi. His research interests include Object Recognition and Human
Computer Interaction.