Wrappers for feature subset selection
... attribute) values and a class label. For example, in medical diagnosis problems the features might include the age, weight, and blood pressure of a patient, and the class label might indicate whether or not a physician determined that the patient was suffering from heart disease. The task of the ind ...
... attribute) values and a class label. For example, in medical diagnosis problems the features might include the age, weight, and blood pressure of a patient, and the class label might indicate whether or not a physician determined that the patient was suffering from heart disease. The task of the ind ...
Ancient Tamil Script Recognition from Stone Inscriptions Using Slant
... slant angles. This is different to conventional slant removal techniques that rely on the average slant angle. In conventional slant correction techniques the average slant angle is estimated and uniformly corrected by a shear transformation. This angle can be estimated by averaging the angles of ne ...
... slant angles. This is different to conventional slant removal techniques that rely on the average slant angle. In conventional slant correction techniques the average slant angle is estimated and uniformly corrected by a shear transformation. This angle can be estimated by averaging the angles of ne ...
leipzip08
... values to RGBA space, defined by colors and opacity (red, green, blue, alpha). Using volume visualization techniques, 2–dimensional projections on different planes can then be displayed. The opacity of voxels depends on cell tissue that the voxels represent. Therefore, distinguishing between differe ...
... values to RGBA space, defined by colors and opacity (red, green, blue, alpha). Using volume visualization techniques, 2–dimensional projections on different planes can then be displayed. The opacity of voxels depends on cell tissue that the voxels represent. Therefore, distinguishing between differe ...
Efficient Histogram Generation Using Scattering on GPUs
... As outlined in section 1, Green’s histogram generation algorithm performs one render pass per bucket, so its asymptotic complexity is O(NB). That of Fluck et al.’s algorithm is also O(NB), because it requires B/4 texture fetches for each input pixel. Assuming we have enough precision in the histogra ...
... As outlined in section 1, Green’s histogram generation algorithm performs one render pass per bucket, so its asymptotic complexity is O(NB). That of Fluck et al.’s algorithm is also O(NB), because it requires B/4 texture fetches for each input pixel. Assuming we have enough precision in the histogra ...
Individual Recognition Based on the Fingerprint of Things Expands
... in mass-production need a system that can acquire the images of a large quantity of components one after another. Fig. 6 shows a registration system for the Fingerprint of Things that automatically captures the fingerprints of a large quantity of bolts and builds an individual recognition database b ...
... in mass-production need a system that can acquire the images of a large quantity of components one after another. Fig. 6 shows a registration system for the Fingerprint of Things that automatically captures the fingerprints of a large quantity of bolts and builds an individual recognition database b ...
An Extension of the ICP Algorithm Considering Scale Factor
... Image registration is a demanding task in computer vision and image process. The Iterative Closest Point (ICP) algorithm [1, 2, 3] is an advanced approach for this problem for its good accuracy and fast speed which has been widely used in a variety of fields such as medical images, document images, ...
... Image registration is a demanding task in computer vision and image process. The Iterative Closest Point (ICP) algorithm [1, 2, 3] is an advanced approach for this problem for its good accuracy and fast speed which has been widely used in a variety of fields such as medical images, document images, ...
A New Approach to Classification with the Least Number of Features
... selection techniques try to approximate the optimal feature set, e.g. by Bayesian inference, gradient descent, genetic algorithms, or various numerical optimisation methods. Commonly, these methods are divided into two classes: filter and wrapper methods. First, filter methods completely separate th ...
... selection techniques try to approximate the optimal feature set, e.g. by Bayesian inference, gradient descent, genetic algorithms, or various numerical optimisation methods. Commonly, these methods are divided into two classes: filter and wrapper methods. First, filter methods completely separate th ...
Local Scale Control for Edge Detection and Blur Estimation
... decreases with time (smoothing), above which the gradient increases with time (edge enhancement). Unfortunately, there is no principled way of choosing this threshold even for a single image, since important edges generate a broad range of gradients, determined by contrast and degree of blur. Sensor ...
... decreases with time (smoothing), above which the gradient increases with time (edge enhancement). Unfortunately, there is no principled way of choosing this threshold even for a single image, since important edges generate a broad range of gradients, determined by contrast and degree of blur. Sensor ...
Learning Low-Rank Representations with Classwise Block
... • We propose a semi-supervised framework to learn lowrank representations with classwise block-diagonal structure, which have strong identification capability. • Our approach learns robust representations of training images and testing images simultaneously, which leverages the global structure over ...
... • We propose a semi-supervised framework to learn lowrank representations with classwise block-diagonal structure, which have strong identification capability. • Our approach learns robust representations of training images and testing images simultaneously, which leverages the global structure over ...
An Information Theoretic Approach to Reflectional Symmetry Detection
... Since it is commonly believed that images are very nonGaussian, high dimensional, continuous signals [15], the most non-Gaussian distribution corresponds to the true tilt angle of the symmetry axis. This distance to non-normality is measured by means of negentropy, a measure previously employed in I ...
... Since it is commonly believed that images are very nonGaussian, high dimensional, continuous signals [15], the most non-Gaussian distribution corresponds to the true tilt angle of the symmetry axis. This distance to non-normality is measured by means of negentropy, a measure previously employed in I ...
On Constrained Optimization Approach to Object
... extract their boundary accurately. The extracted contour is then used in subsequent operations to provide vital information for obtaining quantitative measurements such as area or volume, and for determining qualitative features for latter stage of multi-modality image registration, classification a ...
... extract their boundary accurately. The extracted contour is then used in subsequent operations to provide vital information for obtaining quantitative measurements such as area or volume, and for determining qualitative features for latter stage of multi-modality image registration, classification a ...
A New Feature Selection Method Based on Ant Colony and
... Pattern classification is the task of classifying any given input feature vector into pre-defined set of classes. Irrelevant and redundant features increase the size of search space which consequently led to increase the time of classification and make generalization more difficult. In addition, the ...
... Pattern classification is the task of classifying any given input feature vector into pre-defined set of classes. Irrelevant and redundant features increase the size of search space which consequently led to increase the time of classification and make generalization more difficult. In addition, the ...
Distractor Quality Analyze In Multiple Choice Questions
... language" and "byte code" to the document image of article A, considering them synonymous with the keyword "java". The same rule applies to other keywords: If they have synonyms, then all they need to be added in the document image. But there is another way. Its essence is that before creating infor ...
... language" and "byte code" to the document image of article A, considering them synonymous with the keyword "java". The same rule applies to other keywords: If they have synonyms, then all they need to be added in the document image. But there is another way. Its essence is that before creating infor ...
Learning Visual Representations for Perception
... require different actions. Thus, it seeks to split this state in a way that minimizes the sum of the variances of the TD errors in each of the two new states. This is an adaptation of the splitting rule used by CART for building regression trees (Breiman et al., 1984). To this end, RLVC selects from ...
... require different actions. Thus, it seeks to split this state in a way that minimizes the sum of the variances of the TD errors in each of the two new states. This is an adaptation of the splitting rule used by CART for building regression trees (Breiman et al., 1984). To this end, RLVC selects from ...
Ms.-Vishakha-R.-Bhadane-el-al
... After capturing an image from the image or image enhancement preprocessing applied to the image. Sometimes it can contain image noise while recording process; noise can be removed using filters in treatment / improvement phase. Images must be normalized; this can also be done in the pretreatment ste ...
... After capturing an image from the image or image enhancement preprocessing applied to the image. Sometimes it can contain image noise while recording process; noise can be removed using filters in treatment / improvement phase. Images must be normalized; this can also be done in the pretreatment ste ...
An Analytical Study of the Remote Sensing Image Classification
... Firefly Algorithm etc. Swarm Intelligence is a global research area to improve the optimization of various soft computing and nature inspired techniques. In this paper, we are using these swarm intelligence based techniques for the accurately classification of land cover features. For this classific ...
... Firefly Algorithm etc. Swarm Intelligence is a global research area to improve the optimization of various soft computing and nature inspired techniques. In this paper, we are using these swarm intelligence based techniques for the accurately classification of land cover features. For this classific ...
pixel measurement of thyroid gland by using ultrasound image
... output layer. These layers communicate with one another over a large number of weighted connections. There is no single formal definition of artificial neural network. Though, a class of statistical models may commonly be called "Neural" if they consist of sets of adaptive weights & are capable of e ...
... output layer. These layers communicate with one another over a large number of weighted connections. There is no single formal definition of artificial neural network. Though, a class of statistical models may commonly be called "Neural" if they consist of sets of adaptive weights & are capable of e ...
Using Natural Image Priors
... applied to linear filters learned specifically for this task (similar to the exponential family form in equation 1.2) where the local filters and the nonlinear functions are learned using contrastive divergence. Given a noisy image, the MAP image can be inferred using gradient ascent on the posterio ...
... applied to linear filters learned specifically for this task (similar to the exponential family form in equation 1.2) where the local filters and the nonlinear functions are learned using contrastive divergence. Given a noisy image, the MAP image can be inferred using gradient ascent on the posterio ...
Human Facial Expression Recognition using Gabor
... problems. Some of them only give good result for persondependent test. If the test input was not from the trained database then the results were different. ...
... problems. Some of them only give good result for persondependent test. If the test input was not from the trained database then the results were different. ...
Fundamentals of Multimedia, Chapter 18
... • To deal with illumination change from the query image to different database images, each color channel band of each image is first normalized, and then compressed to a 36-vector. • A 2-dimensional color histogram is then created by using the chromaticity, which is the set of band ratios {R,G}/(R+G ...
... • To deal with illumination change from the query image to different database images, each color channel band of each image is first normalized, and then compressed to a 36-vector. • A 2-dimensional color histogram is then created by using the chromaticity, which is the set of band ratios {R,G}/(R+G ...
Fundamentals of Multimedia, Chapter 18
... • To deal with illumination change from the query image to different database images, each color channel band of each image is first normalized, and then compressed to a 36-vector. • A 2-dimensional color histogram is then created by using the chromaticity, which is the set of band ratios {R,G}/(R+G ...
... • To deal with illumination change from the query image to different database images, each color channel band of each image is first normalized, and then compressed to a 36-vector. • A 2-dimensional color histogram is then created by using the chromaticity, which is the set of band ratios {R,G}/(R+G ...
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
... Leaf Identification Using Feature Extraction and Neural Network image was not used then the features of the same leaf vary with different sizes images. Since, it was the same leaf, the value of all the features were expected to be same. The values for these features vary for different scaled versio ...
... Leaf Identification Using Feature Extraction and Neural Network image was not used then the features of the same leaf vary with different sizes images. Since, it was the same leaf, the value of all the features were expected to be same. The values for these features vary for different scaled versio ...
Recognizing solid objects by alignment with an image
... transformation method. First, the position and orientation of a rigid, solid object is determined up to a reflective ambiguity by the position and orientation of some plane of the object (under the affine imaging model). This plane need not be a surface of the object, but rather can be any "virtual" ...
... transformation method. First, the position and orientation of a rigid, solid object is determined up to a reflective ambiguity by the position and orientation of some plane of the object (under the affine imaging model). This plane need not be a surface of the object, but rather can be any "virtual" ...
Basic Principles of Image Processing
... Image processing is the manipulation of pictorial information to enhance and evaluate maximally the visual qualities of the original image. In this way, it is possible to exaggerate certain details in the digitized image not appreciated in the original form. Until recently, the computer and statisti ...
... Image processing is the manipulation of pictorial information to enhance and evaluate maximally the visual qualities of the original image. In this way, it is possible to exaggerate certain details in the digitized image not appreciated in the original form. Until recently, the computer and statisti ...
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Paper []
... have similar images, so the dominant problem is perceptual aliasing. With richer sensors such as vision or laser rangefinders, discriminating features are more likely to be present in the image, but so are noise and dynamic changes, so the dominant problem for recognition becomes image variability. ...
... have similar images, so the dominant problem is perceptual aliasing. With richer sensors such as vision or laser rangefinders, discriminating features are more likely to be present in the image, but so are noise and dynamic changes, so the dominant problem for recognition becomes image variability. ...