A comparison of stereo correspondence algorithms can be conducted by a quantitative evaluation of
disparity maps. Among the existing evaluation methodologies, the Middlebury’s methodology is commonly
used. However, the Middlebury’s methodology has shortcomings in the evaluation model and the error
measure. These shortcomings may bias the evaluation results, and make a fair judgment about algorithms
accuracy difficult. An alternative, the A* methodology is based on a multiobjective optimisation model that
only provides a subset of algorithms with comparable accuracy. In this paper, a quantitative evaluation of
disparity maps is proposed. It performs an exhaustive assessment of the entire set of algorithms. As
innovative aspect, evaluation results are shown and analysed as disjoint groups of stereo correspondence
algorithms with comparable accuracy. This innovation is obtained by a partitioning and grouping algorithm.
On the other hand, the used error measure offers advantages over the error measure used in the
Middlebury’s methodology. The experimental validation is based on the Middlebury’s test-bed and
algorithms repository. The obtained results show seven groups with different accuracies. Moreover, the topranked
stereo correspondence algorithms by the Middlebury’s methodology are not necessarily the most
accurate in the proposed methodology.
An error measure for evaluating disparity maps is presented. It offers advantages over conventional ground-truth based error measures.
Cabezas, I.; Padilla, V. & Trujillo, M. (2011), A Measure for Accuracy Disparity Maps Evaluation., in César San Martín & Sang-Woon Kim, ed., 'CIARP' , Springer, , pp. 223-231 .
Mutual Information for Registration of Monomodal Brain Images using Modified ...IDES Editor
Image registration has great significance in medicine,
with a lot of techniques anticipated in it. This research work
implies an approach for medical image registration that
registers images of the mono modalities for CT or MRI images
using Modified Adaptive Polar Transform (MAPT). The
performance of the Adaptive Polar Transform (APT) with the
proposed technique is examined. The results prove that MAPT
performs better than APT technique. The proposed scheme not
only reduces the source of errors and also reduces the elapsed
time for registration of brain images. An analysis is presented
for the medical image processing on mutual- information-based
registration.
Hand gesture recognition using support vector machinetheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Multi-feature Fusion Using SIFT and LEBP for Finger Vein RecognitionTELKOMNIKA JOURNAL
In this paper, multi-feature fusion using Scale Invariant Feature Transform (SIFT) and Local
Extensive Binary Pattern (LEBP) was proposed to obtain a feature that could resist degradation problems
such as scaling, rotation, translation and varying illumination conditions. SIFT feature had a capability to
withstand degradation due to changes in the condition of the image scale, rotation and translation.
Meanwhile, LEBP feature had resistance to gray level variations with richer and discriminatory local
characteristics information. Therefore the fusion technique is used to collect important information from
SIFT and LEBP feature.The resulting feature of multi-feature fusion using SIFT and LEBP feature would be
processed by Learning Vector Quantization (LVQ) method to determine whether the testing image could
be recognized or not. The accuracy value could achieve 97.50%, TPR at 0.9400 and FPR at 0.0128 in
optimum condition. That was a better result than only use SIFT or LEBP feature.
An error measure for evaluating disparity maps is presented. It offers advantages over conventional ground-truth based error measures.
Cabezas, I.; Padilla, V. & Trujillo, M. (2011), A Measure for Accuracy Disparity Maps Evaluation., in César San Martín & Sang-Woon Kim, ed., 'CIARP' , Springer, , pp. 223-231 .
Mutual Information for Registration of Monomodal Brain Images using Modified ...IDES Editor
Image registration has great significance in medicine,
with a lot of techniques anticipated in it. This research work
implies an approach for medical image registration that
registers images of the mono modalities for CT or MRI images
using Modified Adaptive Polar Transform (MAPT). The
performance of the Adaptive Polar Transform (APT) with the
proposed technique is examined. The results prove that MAPT
performs better than APT technique. The proposed scheme not
only reduces the source of errors and also reduces the elapsed
time for registration of brain images. An analysis is presented
for the medical image processing on mutual- information-based
registration.
Hand gesture recognition using support vector machinetheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Multi-feature Fusion Using SIFT and LEBP for Finger Vein RecognitionTELKOMNIKA JOURNAL
In this paper, multi-feature fusion using Scale Invariant Feature Transform (SIFT) and Local
Extensive Binary Pattern (LEBP) was proposed to obtain a feature that could resist degradation problems
such as scaling, rotation, translation and varying illumination conditions. SIFT feature had a capability to
withstand degradation due to changes in the condition of the image scale, rotation and translation.
Meanwhile, LEBP feature had resistance to gray level variations with richer and discriminatory local
characteristics information. Therefore the fusion technique is used to collect important information from
SIFT and LEBP feature.The resulting feature of multi-feature fusion using SIFT and LEBP feature would be
processed by Learning Vector Quantization (LVQ) method to determine whether the testing image could
be recognized or not. The accuracy value could achieve 97.50%, TPR at 0.9400 and FPR at 0.0128 in
optimum condition. That was a better result than only use SIFT or LEBP feature.
Different Image Segmentation Techniques for Dental Image ExtractionIJERA Editor
Image segmentation is the process of partitioning a digital image into multiple segments and often used to locate objects and boundaries (lines, curves etc.). In this paper, we have proposed image segmentation techniques: Region based, Texture based, Edge based. These techniques have been implemented on dental radiographs and gained good results compare to conventional technique known as Thresholding based technique. The quantitative results show the superiority of the image segmentation technique over three proposed techniques and conventional technique.
An Automatic Color Feature Vector Classification Based on Clustering MethodRSIS International
In computer vision application, visual features such as
shape, color and texture are extracted to characterize images.
Each of the features is represented using one or more feature
descriptors. One of the important requirements in image
retrieval, indexing, classification, clustering, etc. is extracting
efficient features from images. The color feature is one of the
most widely used visual features. Use of color histogram is the
most common way for representing color feature. One of
disadvantage of the color histogram is that it does not take the
color spatial distribution into consideration. In this paper an
automatic color feature vector classification based on clustering
approach is presented, which effectively describes the spatial
information of color features. The image retrieval results are
compare to improved color feature vector show the acceptable
efficiency of this approach. It propose an automatic color feature
vector classification of satellite images using clustering approach.
The intention is to study cluster a set of satellite images in several
categories on the color similarity basis. The images are processed
using LAB color space in the feature extraction stage. The
resulted color-based feature vectors are clustered using an
automatic unsupervised classification algorithm. Some
experiments based on the proposed recognition technique have
also been performed. More research, however, is needed to
identify and reduce uncertainties in the image processing chain
to improve classification accuracy. The mathematical training
and prediction analysis of a general familiarity with satellite
classifications meet typical map accuracy standards.
SELF-LEARNING AI FRAMEWORK FOR SKIN LESION IMAGE SEGMENTATION AND CLASSIFICATIONijcsit
Image segmentation and classification are the two main fundamental steps in pattern recognition. To perform medical image segmentation or classification with deep learning models, it requires training on large image dataset with annotation. The dermoscopy images (ISIC archive) considered for this work does not have ground truth information for lesion segmentation. Performing manual labelling on this dataset is time-consuming. To overcome this issue, self-learning annotation scheme was proposed in the two-stage deep learning algorithm. The two-stage deep learning algorithm consists of U-Net segmentation model with the annotation scheme and CNN classifier model. The annotation scheme uses a K-means clustering algorithm along with merging conditions to achieve initial labelling information for training the U-Net model. The classifier models namely ResNet-50 and LeNet-5 were trained and tested on the image dataset without segmentation for comparison and with the U-Net segmentation for implementing the proposed self-learning Artificial Intelligence (AI) framework. The classification results of the proposed AI framework achieved training accuracy of 93.8% and testing accuracy of 82.42% when compared with the two classifier models directly trained on the input images.
Colour Object Recognition using Biologically Inspired Modelijsrd.com
Human visual system can categorize objects rapidly and effortlessly despite the complexity and objective ambiguities of natural images. Despite the ease with which we see, visual categorization is an extremely difficult task for computers due to the variability of objects, such as scale, rotation, illumination, position and occlusion. Utilization of characteristics of biological systems for solving practical problems inevitably leads towards reducing the gap between manmade machines and live systems. Biologically motivated information processing has been an important area of scientific research for decades. This paper presents a Biologically Inspired Model which gives a promising solution to object categorization in colour space. The features are extracted in YCbCr colour space and classified by using SVM classifier. The framework has been applied to the image dataset taken from the Amsterdam Library of Object Images (ALOI). The proposed framework can successfully detect and classify the object categories with a good accuracy rate of about 91.3% for the Cb plane.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This paper introduces a new concept for the establishment of human-robot symbiotic relationship. The
system is based on the implementation of knowledge-based image processing methodologies for model
based vision and intelligent task scheduling for an autonomous social robot. This paper aims to develop an
automatic translation of static gestures of alphabets and signs in American Sign Language (ASL), using
neural network with backpropagation algorithm. System deals with images of bare hands to achieve the
recognition task. For each individual sign 10 sample images have been considered, which means in
total300 samples have been processed. In order to compare between the training set of signs and the
considered sample images, are converted into feature vectors. Experimental results reveal that this can
recognize selected ASL signs (accuracy of 92.00%). Finally, the system has been implemented issuing hand
gesture commands for ASL to a robot car, named “Moto-robo”.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Blind Image Quality Assessment with Local Contrast Features ijcisjournal
The aim of this research is to create a tool to evaluate distortion in images without the information about
original image. Work is to extract the statistical information of the edges and boundaries in the image and
to study the correlation between the extracted features. Change in the structural information like shape and
amount of edges of the image derives quality prediction of the image. Local contrast features are effectively
detected from the responses of Gradient Magnitude (G) and Laplacian of Gaussian (L) operations. Using
the joint adaptive normalisation, G and L are normalised. Normalised values are quantized into M and N
levels respectively. For these quantised M levels of G and N levels of L, Probability (P) and conditional
probability(C) are calculated. Four sets of values namely marginal distributions of gradient magnitude Pg,
marginal distributions of Laplacian of Gaussian Pl, conditional probability of gradient magnitude Cg and
probability of Laplacian of Gaussian Cl are formed. These four segments or models are Pg, Pl, Cg and Cl.
The assumption is that the dependencies between features of gradient magnitude and Laplacian of
Gaussian can formulate the level of distortion in the image. To find out them, Spearman and Pearson
correlations between Pg, Pl and Cg, Cl are calculated. Four different correlation values of each image are
the area of interest. Results are also compared with classical tool Structural Similarity Index Measure
Touchless Palmprint Verification using Shock Filter, SIFT, I-RANSAC, and LPD iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Different Image Segmentation Techniques for Dental Image ExtractionIJERA Editor
Image segmentation is the process of partitioning a digital image into multiple segments and often used to locate objects and boundaries (lines, curves etc.). In this paper, we have proposed image segmentation techniques: Region based, Texture based, Edge based. These techniques have been implemented on dental radiographs and gained good results compare to conventional technique known as Thresholding based technique. The quantitative results show the superiority of the image segmentation technique over three proposed techniques and conventional technique.
An Automatic Color Feature Vector Classification Based on Clustering MethodRSIS International
In computer vision application, visual features such as
shape, color and texture are extracted to characterize images.
Each of the features is represented using one or more feature
descriptors. One of the important requirements in image
retrieval, indexing, classification, clustering, etc. is extracting
efficient features from images. The color feature is one of the
most widely used visual features. Use of color histogram is the
most common way for representing color feature. One of
disadvantage of the color histogram is that it does not take the
color spatial distribution into consideration. In this paper an
automatic color feature vector classification based on clustering
approach is presented, which effectively describes the spatial
information of color features. The image retrieval results are
compare to improved color feature vector show the acceptable
efficiency of this approach. It propose an automatic color feature
vector classification of satellite images using clustering approach.
The intention is to study cluster a set of satellite images in several
categories on the color similarity basis. The images are processed
using LAB color space in the feature extraction stage. The
resulted color-based feature vectors are clustered using an
automatic unsupervised classification algorithm. Some
experiments based on the proposed recognition technique have
also been performed. More research, however, is needed to
identify and reduce uncertainties in the image processing chain
to improve classification accuracy. The mathematical training
and prediction analysis of a general familiarity with satellite
classifications meet typical map accuracy standards.
SELF-LEARNING AI FRAMEWORK FOR SKIN LESION IMAGE SEGMENTATION AND CLASSIFICATIONijcsit
Image segmentation and classification are the two main fundamental steps in pattern recognition. To perform medical image segmentation or classification with deep learning models, it requires training on large image dataset with annotation. The dermoscopy images (ISIC archive) considered for this work does not have ground truth information for lesion segmentation. Performing manual labelling on this dataset is time-consuming. To overcome this issue, self-learning annotation scheme was proposed in the two-stage deep learning algorithm. The two-stage deep learning algorithm consists of U-Net segmentation model with the annotation scheme and CNN classifier model. The annotation scheme uses a K-means clustering algorithm along with merging conditions to achieve initial labelling information for training the U-Net model. The classifier models namely ResNet-50 and LeNet-5 were trained and tested on the image dataset without segmentation for comparison and with the U-Net segmentation for implementing the proposed self-learning Artificial Intelligence (AI) framework. The classification results of the proposed AI framework achieved training accuracy of 93.8% and testing accuracy of 82.42% when compared with the two classifier models directly trained on the input images.
Colour Object Recognition using Biologically Inspired Modelijsrd.com
Human visual system can categorize objects rapidly and effortlessly despite the complexity and objective ambiguities of natural images. Despite the ease with which we see, visual categorization is an extremely difficult task for computers due to the variability of objects, such as scale, rotation, illumination, position and occlusion. Utilization of characteristics of biological systems for solving practical problems inevitably leads towards reducing the gap between manmade machines and live systems. Biologically motivated information processing has been an important area of scientific research for decades. This paper presents a Biologically Inspired Model which gives a promising solution to object categorization in colour space. The features are extracted in YCbCr colour space and classified by using SVM classifier. The framework has been applied to the image dataset taken from the Amsterdam Library of Object Images (ALOI). The proposed framework can successfully detect and classify the object categories with a good accuracy rate of about 91.3% for the Cb plane.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This paper introduces a new concept for the establishment of human-robot symbiotic relationship. The
system is based on the implementation of knowledge-based image processing methodologies for model
based vision and intelligent task scheduling for an autonomous social robot. This paper aims to develop an
automatic translation of static gestures of alphabets and signs in American Sign Language (ASL), using
neural network with backpropagation algorithm. System deals with images of bare hands to achieve the
recognition task. For each individual sign 10 sample images have been considered, which means in
total300 samples have been processed. In order to compare between the training set of signs and the
considered sample images, are converted into feature vectors. Experimental results reveal that this can
recognize selected ASL signs (accuracy of 92.00%). Finally, the system has been implemented issuing hand
gesture commands for ASL to a robot car, named “Moto-robo”.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Blind Image Quality Assessment with Local Contrast Features ijcisjournal
The aim of this research is to create a tool to evaluate distortion in images without the information about
original image. Work is to extract the statistical information of the edges and boundaries in the image and
to study the correlation between the extracted features. Change in the structural information like shape and
amount of edges of the image derives quality prediction of the image. Local contrast features are effectively
detected from the responses of Gradient Magnitude (G) and Laplacian of Gaussian (L) operations. Using
the joint adaptive normalisation, G and L are normalised. Normalised values are quantized into M and N
levels respectively. For these quantised M levels of G and N levels of L, Probability (P) and conditional
probability(C) are calculated. Four sets of values namely marginal distributions of gradient magnitude Pg,
marginal distributions of Laplacian of Gaussian Pl, conditional probability of gradient magnitude Cg and
probability of Laplacian of Gaussian Cl are formed. These four segments or models are Pg, Pl, Cg and Cl.
The assumption is that the dependencies between features of gradient magnitude and Laplacian of
Gaussian can formulate the level of distortion in the image. To find out them, Spearman and Pearson
correlations between Pg, Pl and Cg, Cl are calculated. Four different correlation values of each image are
the area of interest. Results are also compared with classical tool Structural Similarity Index Measure
Touchless Palmprint Verification using Shock Filter, SIFT, I-RANSAC, and LPD iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Andrea Stark of IMS Expert Services presents on using SEO in your content marketing strategy for the #GulfCoastHUG, a HubSpot User Group in Pensacola, Florida.
A quantitative evaluation methodology for disparity maps includes the selection of an error measure. Among existing measures, the percentage of bad matched pixels is commonly used. Nevertheless, it requires an error threshold. Thus, a score of zero bad matched pixels does not necessarily imply that a disparity map is free of errors. On the other hand, we have not found publications on the evaluation process where different error measures are applied. In this paper, error measures are characterised in order to provide the bases to select a measure during the evaluation process. An analysis of the impact on results of selecting different error measures on the evaluation of disparity maps is conducted based on the presented characterisation. The evaluation results showed that there is a lack of consistency on the results achieved by considering different error measures. It has an impact on interpreting the accuracy of stereo correspondence algorithms.
A quantitative evaluation methodology for disparity maps includes the selection of an error measure. Among existing measures, the percentage of bad matched pixels is commonly used. Nevertheless, it requires an error threshold. Thus, a score of zero bad matched pixels does not necessarily imply that a disparity map is free of errors. On the other hand, we have not found publications on the evaluation process where different error measures are applied. In this paper, error measures are characterised in order to provide the bases to select a measure during the evaluation process. An analysis of the impact on results of selecting different error measures on the evaluation of disparity maps is conducted based on the presented characterisation. The evaluation results showed that there is a lack of consistency on the results achieved by considering different error measures. It has an impact on interpreting the accuracy of stereo correspondence algorithms.
Coutinho A Depth Compensation Method For Cross Ratio Based Eye TrackingKalle
Traditional cross-ratio methods (TCR) project a light pattern and use invariant properties of projective geometry to estimate the gaze position. Advantages of the TCR methods include robustness to large head movements and in general requires just a one time per user calibration. However, the accuracy of TCR methods decay significantly for head movements along the camera optical axis, mainly due to the angular difference between the optical and visual axis of the eye. In this paper we propose a depth compensation cross-ratio (DCR) method that improves the accuracy of TCR methods for large head depth variations. Our solution compensates the angular offset using a 2D onscreen vector computed from a simple calibration procedure. The length of the 2D vector, which varies with head distance, is adjusted by a scale factor that is estimated from relative size variations of the corneal reflection pattern. The proposed DCR solution was compared to a TCR method using synthetic and real data from 2 users. An average improvement of 40% was observed with synthetic data, and 8% with the real data.
Fast nas rif algorithm using iterative conjugate gradient methodsipij
Many improvements on image enhancemen have been achieved by The Non-negativity And Support
constraints Recursive Inverse Filtering (NAS-RIF) algorithm. The Deterministic constraints such as non
negativity, known finite support, and existence of blur invariant edges are given for the true image. NASRIF
algorithms iterative and simultaneously estimate the pixels of the true image and the Point Spread
Function (PSF) based on conjugate gradients method. NAS-RIF algorithm doesn’t assume parametric
models for either the image or the blur, so we update the parameters of conjugate gradient method and the
objective function for improving the minimization of the cost function and the time for execution. We
propose a different version of linear and nonlinear conjugate gradient methods to obtain the better results
of image restoration with high PSNR.
SINGLE IMAGE SUPER RESOLUTION: A COMPARATIVE STUDYcsandit
The majority of applications requiring high resolution images to derive and analyze data
accurately and easily. Image super resolution is playing an effective role in those applications.
Image super resolution is the process of producing high resolution image from low resolution
image. In this paper, we study various image super resolution techniques with respect to the
quality of results and processing time. This comparative study introduces a comparison between
four algorithms of single image super-resolution. For fair comparison, the compared algorithms
are tested on the same dataset and same platform to show the major advantages of one over the
others.
An Assessment of Image Matching Algorithms in Depth EstimationCSCJournals
Computer vision is often used with mobile robot for feature tracking, landmark sensing, and obstacle detection. Almost all high-end robotics systems are now equipped with pairs of cameras arranged to provide depth perception. In stereo vision application, the disparity between the stereo images allows depth estimation within a scene. Detecting conjugate pair in stereo images is a challenging problem known as the correspondence problem. The goal of this research is to assess the performance of SIFT, MSER, and SURF, the well known matching algorithms, in solving the correspondence problem and then in estimating the depth within the scene. The results of each algorithm are evaluated and presented. The conclusion and recommendations for future works, lead towards the improvement of these powerful algorithms to achieve a higher level of efficiency within the scope of their performance.
Passive Image Forensic Method to Detect Resampling Forgery in Digital Imagesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IMAGE TYPE WATER METER CHARACTER RECOGNITION BASED ON EMBEDDED DSP cscpconf
In the paper, we combined DSP processor with image processing algorithm and studied the method of water meter character recognition. We collected water meter image through camera at a fixed angle, and the projection method is used to recognize those digital images. The experiment results show that the method can recognize the meter characters accurately and artificial meter reading is replaced by automatic digital recognition, which improves working efficiency.
Image type water meter character recognition based on embedded dspcsandit
In the paper, we combined DSP processor with image processing algorithm and studied the
method of water meter character recognition. We collected water meter image through camera
at a fixed angle, and the projection method is used to recognize those digital images. The
experiment results show that the method can recognize the meter characters accurately and
artificial meter reading is replaced by automatic digital recognition, which improves working
efficiency.
A Review Paper on Stereo Vision Based Depth EstimationIJSRD
Stereo vision is a challenging problem and it is a wide research topic in computer vision. It has got a lot of attraction because it is a cost efficient way in place of using costly sensors. Stereo vision has found a great importance in many fields and applications in today’s world. Some of the applications include robotics, 3-D scanning, 3-D reconstruction, driver assistance systems, forensics, 3-D tracking etc. The main challenge of stereo vision is to generate accurate disparity map. Stereo vision algorithms usually perform four steps: first, matching cost computation; second, cost aggregation; third, disparity computation or optimization; and fourth, disparity refinement. Stereo matching problems are also discussed. A large number of algorithms have been developed for stereo vision. But characterization of their performance has achieved less attraction. This paper gives a brief overview of the existing stereo vision algorithms. After evaluating the papers we can say that focus has been on cost aggregation and multi-step refinement process. Segment-based methods have also attracted attention due to their good performance. Also, using improved filter for cost aggregation in stereo matching achieves better results.
Matching algorithm performance analysis for autocalibration method of stereo ...TELKOMNIKA JOURNAL
Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.
Similar to An Evaluation Methodology for Stereo Correspondence Algorithms (20)
Matching algorithm performance analysis for autocalibration method of stereo ...
An Evaluation Methodology for Stereo Correspondence Algorithms
1. An Evaluation Methodology for
Stereo Correspondence Algorithms
Ivan Cabezas, Maria Trujillo and Margaret Florian
ivan.cabezas@correounivalle.edu.co
February 25th 2012
International Conference on Computer Vision Theory and Applications, VISAPP 2012, Rome - Italy
2. Multimedia and Vision Laboratory
MMV is a research group of the Universidad del Valle in Cali, Colombia
Ivan Maria et al.
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 2
3. Ayax Inc.
Ayax Inc. offers informatics solutions for decision analysis
Margaret
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 3
4. Content
Stereo Vision
Canonical Stereo Geometry and Disparity
Ground-truth Based Evaluation
Quantitative Evaluation Methodologies
Middlebury’s Methodology
A* Methodology
A* Groups Methodology
Experimental Results
Final Remarks
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 4
5. Stereo Vision
The stereo vision problem is to recover the 3D structure of the scene using
two or more images
3D World
Optics
Problem
Camera Inverse
System Problem
Disparity Map Reconstruction
Algorithm
2D Images
Left Right
Correspondence
Stereo Images Algorithm 3D Model
Yang Q. et al., Stereo Matching with Colour-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling, IEEE PAMI 2009
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 5
6. Canonical Stereo Geometry and Disparity
Disparity is the distance between corresponding points
Accurate Estimation Inaccurate Estimation
P P
P’
Z Z’
pl pr pl pr
πl πr πl πr
pr ’
f f
Cl B Cr Cl B Cr
Trucco, E. and Verri A., Introductory Techniques for 3D Computer Vision, Prentice Hall 1998
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 6
7. Ground-truth Based Evaluation
Ground-truth based evaluation is based on the comparison using disparity
ground-truth data
Scharstein, D. and Szeliski, R., High-accuracy Stereo Depth Maps using Structured Light, CVPR 2003
Tola, E., Lepetit, V. and Fua, P., A Fast Local Descriptor for Dense Matching, CVPR 2008
Strecha, C., et al. On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery, CVPR 2008
http://www.zf-usa.com/products/3d-laser-scanners/
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 7
8. Quantitative Evaluation Methodologies
The use of a methodology allows to:
Assert specific components and procedures
Tune algorithm's parameters
Support decision for researchers and
practitioners
Measure the progress on the field
Szeliski, R., Prediction Error as a Quality Metric for Motion and Stereo, ICCV 2000
Kostliva, J., Cech, J., and Sara, R., Feasibility Boundary in Dense and Semi-Dense Stereo Matching, CVPR 2007
Tomabari, F., Mattoccia, S., and Di Stefano, L., Stereo for robots: Quantitative Evaluation of Efficient and Low-memory Dense Stereo Algorithms, ICCARV 2010
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 8
9. Middlebury’s Methodology
Select Test Bed Images Select Error Criteria
nonocc all disc
Select and Apply Stereo Algorithms Select Error Measures
ObjectStereo GC+SegmBorder PUTv3
Compute Error Measures
PatchMatch ImproveSubPix OverSegmBP
Scharstein, D. and Szeliski, R., High-accuracy Stereo Depth Maps using Structured Light, CVPR 2003
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 9
11. Middlebury’s Methodology (iii)
Apply Evaluation Model Interpret Results
The ObjectStereo algorithm
produces accurate results
Middlebury’s
Evaluation Model
Algorithm Average Final
Rank Ranking
ObjectStereo 1.33 1
PatchMatch 3.00 2
PUTv3 3.33 3
GC+SegmBorder 4.00 4
ImproveSubPix 4.00 5
OverSegmBP 5.33 6
Scharstein, D. and Szeliski, R., A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, IJCV 2002
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 11
12. Middlebury’s Methodology (iv): Weaknesses
The Middlebury’s evaluation model have some shortcomings
In some cases, the ranks are assigned arbitrarily
The same average ranking does not imply the same performance (and
vice versa)
The cardinality of the set of top-performer algorithms is a free parameter
It operates values related to incommensurable measures
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 12
13. Middlebury’s Methodology (v): Weaknesses
The BMP percentage measures the quantity of disparity estimation errors
exceeding a threshold
The BMP measure have some shortcomings:
It is sensitive to the threshold selection
It ignores the error magnitude
It ignores the inverse relation between depth and disparity
It may conceal estimation errors of a large magnitude, and, also it may
penalise errors of small impact in the final 3D reconstruction
Cabezas, I., Padilla, V., and Trujillo M., A Measure for Accuracy Disparity Maps Evaluation, CIARP 2011
Gallup, D., et al. Variable Baseline/Resolution Stereo, CVPR, 2008
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 13
14. A* Methodology
The A* evaluation methodology brings a theoretical background for the
comparison of stereo correspondence algorithms
The set of algorithms under evaluation
The set of estimated maps to be compared
The function that produces a vector of error measures
The set of vectors of error measures
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 14
15. A* Methodology (ii)
The evaluation model of the A* methodology addresses the comparison of
stereo correspondence algorithms as a multi-objective optimisation problem
It defines a partition over the set A (the decision space)
Subject to:
where ≺ denotes the Pareto Dominance relation:
Let p and q be two algorithms
Let Vp and Vq be a pair of vectors belonging to the objective space
Thus, three possible relations are considered
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 15
16. A* Methodology (iii): Pareto Dominance
The Pareto Dominance defines a partial order relation
VGC+SegmBorder = < 4.99, 5.78, 8.66 >
VPatchMatch = < 2.47, 7.80, 7.11 >
VImproveSubPix = < 2.96, 8.22, 8.55 >
VGC+SegmBorder VPatchMatch
< 4.99, 5.78, 8.66 > < 2.47, 7.80, 7.11 >
GC+SegmBorder ~ PatchMatch
VPatchMatch VImproveSubPix
< 2.47, 7.80, 7.11 > < 2.96, 8.22, 8.55 >
Patchmatch ≺ ImproveSubPix
Van Veldhuizen, D., et al., Considerations in Engineering Parallel Multi-objective Evolutionary Algorithms, Trans in Evolutionary Computing 2003
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 16
17. A* Methodology (iv): Illustration
Select Test Bed Images Select Error Criteria
nonocc all disc
Select and Apply Stereo Algorithms Select Error Measures
ObjectStereo GC+SegmBorder PUTv3
Compute Error Measures
PatchMatch ImproveSubPix OverSegmBP
Scharstein, D. and Szeliski, R., High-accuracy Stereo Depth Maps using Structured Light, CVPR 2003
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 17
18. A* Methodology (v): Illustration
The evaluation model performs the partitioning and the grouping of stereo
algorithms under evaluation, based on the Pareto Dominance relation
Compute Error Measures Apply Evaluation Model
Algorithm nonocc all disc
ObjectStereo 2.20 6.99 6.36
ObjectStereo , GC+SegmBorder
GC+SegmBorder 4.99 5.78 8.66
PUTv3 2.40 9.11 6.56 PatchMatch , , ,
PUTv3 ImproveSubPix OverSegmBP
PatchMatch 2.47 7.80 7.11
ImproveSubPix 2.96 8.22 8.55 Algorithm nonocc all disc Set
OverSegmBP 3.19 8.81 8.89 ObjectStereo 2.20 6.99 6.36 A*
GC+SegmBorder 4.99 5.78 8.66 A*
PUTv3 2.40 9.11 6.56 A’
PatchMatch 2.47 7.80 7.11 A’
ImproveSubPix 2.96 8.22 8.55 A’
OverSegmBP 3.19 8.81 8.89 A’
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 18
19. A* Methodology (vi): Illustration
Interpretation of results is based on the cardinality of the set A*
Apply Evaluation Model Interpret Results
A* Evaluation Model
The Objectstereo and the
GC+SegmBorder algorithms
Algorithm nonocc all disc Set are, comparable among them, and have a
ObjectStereo 2.20 6.99 6.36 A* superior performance to the rest of
algorithms
GC+SegmBorder 4.99 5.78 8.66 A*
PUTv3 2.40 9.11 6.56 A’
PatchMatch 2.47 7.80 7.11 A’
ImproveSubPix 2.96 8.22 8.55 A’
OverSegmBP 3.19 8.81 8.89 A’
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 19
20. A* Methodology (vii): Strength and Weakness
Strength: It allows a formal interpretation of results, based on the cardinality
of the set A*, and in regard to considered imagery test-bed
Weakness: It does not allow an exhaustive evaluation of the entire set of
algorithms under evaluation
It computes the set A* just once, and does not bring information about A’
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 20
21. A* Groups Methodology
It extends the evaluation model of the A* methodology, incorporating the
capability of performing an exhaustive evaluation
subject to:
It introduces the partitioningAndGrouping algorithm
A = Set ( { } );
A.load( “Algorithms.dat” );
A* = Set ( { } );
A’ = Set ( { } );
group = 1;
do {
computePartition( A, A*, A’, g, ≺ );
A*.save ( “A*_group_”+group );
group++;
A.update ( A’ ); // A = A / A*
A*.removeAll ( ); // A* = { }
A’.removeAll ( ); // A’ = { }
}while ( ! A.isEmpty ( ) );
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 21
22. A* Groups Methodology (ii): Sigma-Z-Error
The A* Groups methodology uses the Sigma-Z-Error
(SZE) measure
The SZE measure has the following properties:
It is inherently related to depth reconstruction in a stereo system
It is based on the inverse relation between depth and disparity
It considers the magnitude of the estimation error
It is threshold free
Cabezas, I., Padilla, V., and Trujillo M., A Measure for Accuracy Disparity Maps Evaluation, CIARP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 22
23. A* Groups Methodology (iii): Illustration
The evaluation process of selected algorithms by using the proposal
Select Test Bed Images Select Error Criteria
nonocc all disc
Select and Apply Stereo Algorithms Select Error Measures
ObjectStereo GC+SegmBorder PUTv3
Compute Error Measures
PatchMatch ImproveSubPix OverSegmBP
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 23
24. A* Groups Methodology (iv): Illustration
The evaluation model performs the partitioning and the grouping of stereo
algorithms under evaluation, based on the Pareto Dominance relation
Compute Error Measures Apply Evaluation Model
Algorithm nonocc all disc
ObjectStereo 73.88 117.90 36.25
GC+SegmBorder ,, PatchMatch
GC+SegmBorder 50.48 64.90 24.33
PUTv3 99.67 333.37 53.79 ObjectStereo , PUTv3 , ImproveSubPix , OverSegmBP
PatchMatch 49.95 261.84 32.85
Algorithm nonocc all disc Group
ImproveSubPix 50.66 97.94 32.01
OverSegmBP 58.65 108.60 34.58 GC+SegmBorder 50.48 64.90 24.33 1
PatchMatch 49.95 261.84 32.85 1
PUTv3 99.67 333.37 53.79
ImproveSubPix 50.66 97.94 32.01
OverSegmBP 58.65 108.60 34.58
ObjectStereo 73.88 117.90 36.25
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 24
25. A* Groups Methodology (v): Illustration
Apply Evaluation Model
ObjectStereo , PUTv3, ImproveSubPix , OverSegmBP
Algorithm nonocc all disc
ObjectStereo, PUTv3 , OverSegmBP
PUTv3 99.67 333.37 53.79 Algorithm nonocc all disc
ImproveSubPix 50.66 97.94 32.01 PUTv3 99.67 333.37 53.79
OverSegmBP 58.65 108.60 34.58 OverSegmBP 58.65 108.60 34.58
ObjectStereo 73.88 117.90 36.25 ObjectStereo 73.88 117.90 36.25
OverSegmBP
ImproveSubPix
PUTv3 , ObjectStereo
ObjectStereo , PUTv3 , OverSegmBP
Algorithm nonocc all disc Group
Algorithm nonocc all disc Group OverSegmBP 58.65 108.60 34.58 3
PUTv3 99.67 333.37 53.79
ImproveSubPix 50.66 97.94 32.01 2
ObjectStereo 73.88 117.90 36.25
PUTv3 99.67 333.37 53.79
ObjectStereo 73.88 117.90 36.25
OverSegmBP 58.65 108.60 34.58
And so on …
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 25
26. A* Groups Methodology (vi): Illustration
Interpretation of results is based on the cardinality of each group
Apply Evaluation Model Interpret Results
A* Groups
Evaluation Model
There are 5 groups of different performance
The GC+SegmBorder and the PatchMatch
algorithms are, comparable among them,
and have a superior performance to the rest
Algorithm nonocc all disc Group of algorithms
GC+SegmBorder 50.48 64.90 24.33 1
The ImproveSubPix algorithm is superior to
PatchMatch 49.95 261.84 32.85 1 the OverSegmBP, the ObjectStereo, and
ImproveSubPix 50.66 97.94 32.01 2 the PUTv3 algorithms
OverSegmBP 58.65 108.60 34.58 3
…
ObjectStereo 73.88 117.90 36.25 4
PUTv3 99.67 333.37 53.79 5 The PUTv3 algorithm has the lowest
performance
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 26
27. Experimental Results
The conducted evaluation involves the following elements:
Test Bed Images
Error Criteria nonocc , all , disc
Error Measures SZE , BMP
Stereo Algorithms 112 algorithms from the Middlebury’s repository
Evaluation Models A* Groups Middlebury
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 27
28. Experimental Results (ii)
Algorithm Group Middlebury’s
Ranking
ADCensus 2 1
AdaptingBP 2 2
CoopRegion 2 3
DoubleBP 1 4
RDP 2 5
OutlierConf 2 6
Algorithm Strategy Group Middlebury’s SubPixDoubleBP 2 7
Ranking SurfaceStereo 2 8
DoubleBP Global 1 4 WarpMat 2 9
PatchMatch Local 1 11 ObjectStereo 2 10
GC+SegmBorder Global 1 13 PatchMatch 1 11
FeatureGC Global 1 18 Undr+OverSeg 2 12
Segm+Visib Global 1 29 GC+SegmBorder 1 13
MultiresGC Global 1 30 InfoPermeable 2 14
DistinctSM Local 1 34 CostFilter 2 15
GC+occ Global 1 67
MultiCamGC Global 1 68
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 28
29. Final Remarks
The use of the A* Groups methodology allows to perform an exhaustive
evaluation, as well as an objective interpretation of results
Innovative results in regard to the comparison of stereo correspondence
algorithms were obtained using proposed methodology and the SZE error
measure
The introduced methodology offers advantages over the conventional
approaches to compare stereo correspondence algorithms
Authors are already working in order to provide to the research community an
accessible way to use the introduced methodology
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 29
30. An Evaluation Methodology for
Stereo Correspondence Algorithms
Ivan Cabezas, Maria Trujillo and Margaret Florian
ivan.cabezas@correounivalle.edu.co
February 25th 2012
International Conference on Computer Vision Theory and Applications, VISAPP 2012, Rome, Italy