Image Features Extraction

Face recognition is attracting much attention in the society of network multimedia information access.
Areas such as network security, content indexing and retrieval, and video compression benefits from face
recognition technology because people are the center of attention in a lot of video. Network access control
via face recognition not only makes hackers virtually impossible to steal one's password, but also increases
the user-friendliness in human-computer interaction. The data in face images are distributed in a complex
manner due to the variation of light intensity, facial expression and pose. In this paper the Kernel Principal
Component Analysis (KPCA) is used to recognize the faces. A Gaussian model of skin segmentation method
is applied here to exclude the global features such as beard, eyebrow, moustache, etc. both training and
test images are randomly selected from four different data bases to improve the training. The experimental
results show that the proposed framework is efficient for recognizing the humanfaces.

In this paper, we demonstrate the concepts of a prototype of a knowledge-driven content-based information mining system produced to manage and explore large volumes of remote sensing image data. The system consists of a computationally intensive offline part and an online interface. The offline part aims at the extraction of primitive image features, their compression, and data reduction, the generation of a completely unsupervised image content-index, and the ingestion of the catalogue entry in the database management system. Then, the user's interests-semantic interpretations of the image content-are linked with Bayesian networks to the content-index. Since this calculation is only based on a few training samples, the link can be computed online, and the complete image archive can be searched for images that contain the defined cover type. Practical applications exemplified with different remote sensing datasets show the potential of the system.

Spemannstrasse 38 the analysis of the perceived object shape [3-6] and processes of object recognition [7, 8] (Figure 1). We 72076 Tü bingen Germany employed closed contours rendered by similarly oriented local elements and embedded in a background of randomly oriented elements. Such displays yield the perception of a global figure in a randomly textured Summary background (Figure 2) and are thought to emerge from a segmentation process that relies on the integration of The question of how local image features on the retina the similarly oriented elements into global configuraare integrated into perceived global shapes is central tions [9, 10]. to our understanding of human visual perception. Psy-

We propose a novel hashing scheme for image retrieval, clustering and automatic object discovery. Unlike commonly used bag-of-words approaches, the spatial extent of image features is exploited in our method. The geometric information is used both to construct repeatable hash keys and to increase the discriminability of the description. Each hash key combines visual appearance (visual words) with semi-local geometric information.

Many approaches to object recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the distribution of the image features is modelled. Generative and discriminative methods have very different characteristics, as well as complementary strengths and weaknesses. In this paper we introduce new generative and discriminative models for object detection and classification based on weakly labelled training data. We use these models to illustrate the relative merits of the two approaches in the context of a data set of widely varying images of nonrigid objects (animals). Our results support the assertion that neither approach alone will be sufficient for large scale object recognition, and we discuss techniques for combining them.

Understanding the neural mechanisms underlying object recognition is one of the fundamental challenges of visual neuroscience. While neurophysiology experiments have provided evidence for a ''simple-tocomplex'' processing model based on a hierarchy of increasingly complex image features, behavioral and fMRI studies of face processing have been interpreted as incompatible with this account. We present a neurophysiologically plausible, feature-based model that quantitatively accounts for face discrimination characteristics, including face inversion and ''configural'' effects. The model predicts that face discrimination is based on a sparse representation of units selective for face shapes, without the need to postulate additional, ''face-specific'' mechanisms. We derive and test predictions that quantitatively link model FFA face neuron tuning, neural adaptation measured in an fMRI rapid adaptation paradigm, and face discrimination performance. The experimental data are in excellent agreement with the model prediction that discrimination performance should asymptote as faces become dissimilar enough to activate different neuronal populations. Neuron 160

To eliminate the negative impacts of inorganic constituents during biomass thermochemical processes, leaching method by different diluted acid solutions was chosen. The different palm oil biomass samples (palm kernel shell (PKS), empty fruit bunches (EFB) and palm mesocarp fiber (PMF)) were pretreated by various diluted acid solutions (H2SO4, HClO4, HF, HNO3, HCl). Acids with the highest degrees of demineralization were selected to investigate the dematerialization impacts on the biomass thermal characteristics and physiochemical structure. Thermogravimetric analysis coupled with mass spectroscopy (TGA-MS) and Fourier transform infrared spectroscopy (TGA-FTIR) were employed to examine the biomass thermal degradation. TGA and DTG (Derivative thermogravimetry) indicated that the maximum degradation temperatures increased after acid pretreatment due to the minerals catalytic effects. The main permanent evolved gases comprising H 2, CO2, CO were detected online during analysis. The major permanent gases produced at the temperature range of 250-750 °C were attributed to the condensable vapors cracking and probably some secondary reactions. The physiochemical structure change of the acid-treated biomass samples was examined by using Brunauer Emmett Teller (BET) method, Scanning Electron Microscope (SEM) and FTIR. The pyrolysis kinetics of the different palm oil biomasses were investigated using first order reaction model. © 2014 Elsevier Ltd. All rights reserved.

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Audio streams, such as news broadcasting, meeting rooms, and special video comprise sound from an extensive variety of sources. The detection of audio events including speech, coughing , gunshots, etc. leads to intelligent audio event detection (AED). With substantial attention geared to AED for various types of applications, such as security, speech recognition, speaker recognition, home care, and health monitoring, scientists are now more motivated to perform extensive research on AED. The deployment of AED is actually a more complicated task when going beyond exclusively highlighting audio events in terms of feature extraction and classification in order to select the best features with high detection accuracy. To date, a wide range of different detection systems based on intelligent techniques have been utilized to create machine learning-based audio event detection schemes. Nevertheless, the preview study does not encompass any state-of-the-art reviews of the proficiency and significances of such methods for resolving audio event detection matters. The major contribution of this work entails reviewing and categorizing existing AED schemes into preprocessing, feature extraction, and classification methods. The importance of the algorithms and methodologies and their proficiency and restriction are additionally analyzed in this study. This research is expanded by critically comparing audio detection methods and algorithms according to accuracy and false alarms using different types of datasets.

The estimation of edge features, such as sub-pixel position, orientation, curvature and change in intensity at both sides of the edge, from the computation of the gradient vector in each pixel is usually inexact, even in ideal images. In this paper, we present a new edge detector based on an edge and acquisition model derived from the partial area effect, which does not assume continuity in the image values. The main goal of this method consists in achieving a highly accurate extraction of the position, orientation, curvature and contrast of the edges, even in difficult conditions, such as noisy images, blurred edges, low contrast areas or very close contours. For this purpose, we first analyze the influence of perfectly straight or circular edges in the surrounding region, in such a way that, when these conditions are fulfilled, the features can exactly be determined. Afterward, we extend it to more realistic situations considering how adverse conditions can be tackled and presenting an iterative scheme for improving the results. We have tested this method in real as well as in sets of synthetic images with extremely difficult edges, and in both cases a highly accurate characterization has been achieved.

There are two main interests in Digital Camera Image Forensics, namely source identification and forgery detection. In this paper, we first briefly provide an introduction to the major processing stages inside a digital camera and then review several methods for source digital camera ...

Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients.

In this paper, we prove that the principal component analysis (PCA) and the linear discriminant analysis (LDA) can be directly implemented in the discrete cosine transform (DCT) domain and the results are exactly the same as the one obtained from the spatial domain. In some applications, compressed images are desirable to reduce the storage requirement. For images compressed using the DCT, e.g., in JPEG or MPEG standard, the PCA and LDA can be directly implemented in the DCT domain such that the inverse DCT transform can be skipped and the dimensionality of the original data can be initially reduced to cut down computational cost.

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmenta-tion of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass–Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.

Background: Keypoint detection is important for many computer vision applications. Existing methods suffer from insufficient selectivity regarding the shape properties of features and are vulnerable to contrast variations and to the presence of noise or texture. Methods: We propose a trainable filter which we call COSFIRE (Combination Of Shifted FIlter REsponses) and use for keypoint detection and pattern recognition. It is automatically configured to be selective for a local contour pattern specified by an example. The configuration comprises selecting given channels of a bank of Gabor filters and determining certain blur and shift parameters. A COSFIRE filter response is computed as the weighted geometric mean of the blurred and shifted responses of the selected Gabor filters. It shares similar properties with some shape-selective neurons in visual cortex, which provided inspiration for this work. Results: We demonstrate the effectiveness of the proposed filters in three applications: the detection of retinal vascular bifurcations (DRIVE data set: 98.50% recall, 96.09% precision), the recognition of handwritten digits (MNIST data set: 99.48% correct classification), and the detection and recognition of traffic signs in complex scenes (100% recall and precision). Conclusions: The proposed COSFIRE filters are conceptually simple and easy to implement. They are versatile keypoint detectors and are highly effective in practical computer vision applications.

It is well known that a correlation exist between the pattern of partial discharge (PD) behavior and the insulation quality. Since different sources of partial discharge have their own unique effects on the degradation of insulation material, it is vital to investigate the relationship between the defect type and the PD to determine the insulation quality. Numerous work had been done to classify partial discharge patterns with variable success. Past research work in partial discharge classification varies greatly in terms of classification techniques used, choice of feature extraction, denoising method, training process, artificial defects created for training purposes and performance assessment. Therefore it is necessary for a literature survey to access the state of the art development in partial discharge classification. (C) 2015 Elsevier Ltd. All rights reserved.

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Increase in intraocular pressure (IOP) is one of the causes of glaucoma which can lead to blindness if not detected and treated at an early stage. Glaucoma symptoms are not always obvious; hence patients seek treatment only when the condition progressed significantly. Early detection and treatment will decrease the chances of vision loss due to glaucoma. This paper proposes a novel automated glaucoma diagnosis method using various features extracted from Gabor transform applied on digital fundus images. In this work, we have used 510 images to classify into normal and glaucoma classes. Various features namely mean, variance, skewness, kurtosis, energy, and Shannon, Renyi, and Kapoor entropies are extracted from the Gabor transform coefficients. These extracted features are subjected to principal component analysis (PCA) to reduce the dimensionality of the features. Then these features are ranked using various ranking methods namely: Bhattachaiyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC), and entropy. In this work, t-test ranking method yielded the highest performance with an average accuracy of 93.10%, sensitivity of 89.75% and specificity of 96.20% using 23 features with Support Vector Machine (SVM) classifier. Also, we have proposed a Glaucoma Risk Index (GRI) developed using principal components to classify the two classes using just one number. (C) 2014 Elsevier Ltd. All rights reserved.

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This review will discuss the use of manual grading scales, digital photography, and automated image analysis in the quantification of fundus changes caused by age-related macular disease. Digital imaging permits processing of images for enhancement, comparison, and feature quantification, and these techniques have been investigated for automated drusen analysis. The accuracy of automated analysis systems has been enhanced by the incorporation of interactive elements, such that the user is able to adjust the sensitivity of the system, or manually add and remove pixels. These methods capitalize on both computer and human image feature recognition and the advantage of computer-based methodologies for quantification. The histogram-based adaptive local thresholding system is able to extract useful information from the image without being affected by the presence of other structures. More recent developments involve compensation for fundus background reflectance, which has most recently been combined with the Otsu method of global thresholding. This method is reported to provide results comparable with manual stereo viewing. Developments in this area are likely to encourage wider use of automated techniques. This will make the grading of photographs easier and cheaper for clinicians and researchers.

Intracranial bleeding in patients with vertebrobasilar dolichoectasia (VBD) is considered uncommon, but there are no precise data to support this opinion. The purpose of this study was to examine the incidence and characteristics of intracranial hemorrhage in patients with VBD and to evaluate factors that may promote bleeding. We conducted a prospective study of 156 consecutive VDB patients followed-up for an average 9.35 years. The association of demographic, clinical, and imaging features with occurrence of intracranial bleeding was evaluated by multivariate analysis. Survival analysis was used to evaluate rates of incidence. 32 hemorrhagic strokes were observed in 28 patients either as a diagnostic event (n=10) or during follow-up (n=22). Of the 32 hemorrhagic events, 6 were subarachnoid hemorrhage and 26 intraparenchymal hemorrhage. Multivariate analysis found an association between intracranial bleeding and maximum diameter of the basilar artery (OR, 4.29; P=0.009), degree of lateral displacement of the basilar artery (OR, 4.53; P=0.004), hypertension (OR, 4.74; P=0.024), use of antiplatelet or anticoagulant agents (OR, 3.07; P=0.033), and female sex (OR 6.33; P=0.001). The cumulative proportion of survivors free of hemorrhagic stroke was 88.6 at 5 years and 84.4 at 10 years. Our study showed that intracranial bleeding in patients with VBD is not as uncommon as usually believed. Its occurrence is associated with the degree of ectasia and elongation of the basilar artery and may be favored by hypertension and use of antiplatelet or anticoagulant agents.

Computer vision is one of the most active research fields in information technology today. Giving machines and robots the ability to see and comprehend the surrounding world at the speed of sight creates endless potential applications and opportunities. Feature detection and description algorithms can be indeed considered as the retina of the eyes of such machines and robots. However, these algorithms are typically computationally intensive, which prevents them from achieving the speed of sight real-time performance. In addition, they differ in their capabilities and some may favor and work better given a specific type of input compared to others. As such, it is essential to compactly report their pros and cons as well as their performances and recent advances. This paper is dedicated to provide a comprehensive overview on the state-of-the-art and recent advances in feature detection and description algorithms. Specifically, it starts by overviewing fundamental concepts. It then compares, reports and discusses their performance and capabilities. The Maximally Stable Extremal Regions algorithm and the Scale Invariant Feature Transform algorithms, being two of the best of their type, are selected to report their recent algorithmic derivatives.

Diabetes Mellitus (DM), a chronic lifelong condition, is characterized by increased blood sugar levels. As there is no cure for DM, the major focus lies on controlling the disease. Therefore, DM diagnosis and treatment is of great importance. The most common complications of DM include retinopathy, neuropathy, nephropathy and cardiomyopathy. Diabetes causes cardiovascular autonomic neuropathy that affects the Heart Rate Variability (HRV). Hence, in the absence of other causes, the HRV analysis can be used to diagnose diabetes. The present work aims at developing an automated system for classification of normal and diabetes classes by using the heart rate (HR) information extracted from the Electrocardiogram (ECG) signals. The spectral analysis of HRV recognizes patients with autonomic diabetic neuropathy, and gives an earlier diagnosis of impairment of the Autonomic Nervous System (ANS). Significant correlations with the impaired ANS are observed of the HRV spectral indices obtained by using the Discrete Wavelet Transform (DWT) method. Herein, in order to diagnose and detect DM automatically, we have performed DWT decomposition up to 5 levels, and extracted the energy, sample entropy, approximation entropy, kurtosis and skewness features at various detailed coefficient levels of the DWT. We have extracted relative wavelet energy and entropy features up to the 5th level of DWT coefficients extracted from HR signals. These features are ranked by using various ranking methods, namely, Bhattacharyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC) and entropy. The ranked features are then fed into different classifiers, that include Decision Tree (DT), K-Nearest Neighbor (KNN), Naive Bayes (NBC) and Support Vector Machine (SVM). Our results have shown maximum diagnostic differentiation performance by using a minimum number of features. With our system, we have obtained an average accuracy of 92.02%, sensitivity of 92.59% and specificity of 91.46%, by using DT classifier with ten-fold cross validation. (C) 2015 Elsevier B.V. All rights reserved.

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Most of the currently available image database systems provide a text-based retrieval function called keyword retrieval, where users specify`keywords' such as titles, attributes, and categories of themes. But many times it is not easy for users to specify suitable keywords for a particular retrieval. Besides, building a large image database with complete description of contents is a very dicult task. In this paper, we present a content-based retrieval method which obviates the need to describe certain contents of an image to be archived and retrieved. The proposed method computes image features automatically from a given image and they can be used to archive and/or retrieve images. These features are based on color and its spatial distribution information in an image. We have also developed a similarity measure to compare the color and spatial feature similarity of two images. This technique has been developed and tested for content-based similarity retrieval of images on two databases consisting of: (i) 100 test images and (ii) 800 actual trademarks images. The experimental results show a high eciency of retrieval. Ó

Certain locations attract human gaze in natural visual scenes. Are there measurable features, which distinguish these locations from others? While there has been extensive research on luminance-defined features, only few studies have examined the influence of color on overt attention. In this study, we addressed this question by presenting color-calibrated stimuli and analyzing color features that are known to be relevant for the responses of LGN neurons. We recorded eye movements of 15 human subjects freely viewing colored and grayscale images of seven different categories. All images were also analyzed by the saliency map model (L. Itti, C. Koch, & E. Niebur, 1998). We find that human fixation locations differ between colored and grayscale versions of the same image much more than predicted by the saliency map. Examining the influence of various color features on overt attention, we find two extreme categories: while in rainforest images all color features are salient, none is salient in fractals. In all other categories, color features are selectively salient. This shows that the influence of color on overt attention depends on the type of image. Also, it is crucial to analyze neurophysiologically relevant color features for quantifying the influence of color on attention.

Reversible focal lesions in the splenium of the corpus callosum (SCC) have recently been reported.They are circumscribed and located in the median aspect of the SCC. On MRI, they are hyperintense on T2-W and isohypointense on T1-W sequences, with no contrast enhancement. On DWI, SCC lesions are hyperintense with low ADC values, reflecting restricted diffusion due to cytotoxic edema. The common element is the disappearance of imaging abnormalities with time, including normalization of DWI. Clinical improvement is often reported. The most established and frequent causes of reversible focal lesions of the SCC are viral encephalitis, antiepileptic drug toxicity/withdrawal and hypoglycemic encephalopathy. Many other causes have been reported, including traumatic axonal injury. The similar clinical and imaging features suggest a common mechanism induced by different pathological events leading to the same results. Edema and diffusion restriction in focal reversible lesions of the SCC have been attributed to excitotoxic mechanisms that can result from different mechanisms; no unifying relation-ship has been found to explain all the pathologies associated with SCC lesions. In our opinion, the similar imaging, clinical and prognostic aspects of these lesions depend on a high vulnerability of the SCC to excitotoxic edema and are less dependent on the underling pathology. In this review, the relevant literature concerning reversible focal lesions in the SCC is analyzed and hypotheses about their pathogenesis are proposed.

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image 'features' that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe a class of such invariants that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from illuminant-dependent 'subspaces' of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, photometric stereo, materialbased segmentation, and motion estimation.

In this work, we present an approach to jointly segment a rigid object in a 2D image and estimate its 3D pose, using the knowledge of a 3D model. We naturally couple the two processes together into a unique energy functional that is minimized through a variational approach. Our methodology differs from the standard monocular 3D pose estimation algorithms since it does not rely on local image features. Instead, we use global image statistics to drive the pose estimation process. This confers a satisfying level of robustness to noise and initialization for our algorithm, and bypasses the need to establish correspondences between image and object features. Moreover, our methodology possesses the typical qualities of region-based active contour techniques with shape priors, such as robustness to occlusions or missing information, without the need to evolve an infinite dimensional curve. Another novelty of the proposed contribution is to use a unique 3D model surface of the object, instead of learning a large collection of 2D shapes to accommodate for the diverse aspects that a 3D object can take when imaged by a camera. Experimental results on both synthetic and real images are provided, which highlight the robust performance of the technique on challenging tracking and segmentation applications.

The rich spectral information provided by hyperspectral imaging has made this technology very useful in the classification of remotely sensed data. However, classification of hyperspectral data is typically affected by noise and the Hughes phenomenon due to the presence of hundreds of spectral bands and correlation among them, with usually a limited number of samples for training. Linear discriminant analysis (LDA) is a well-known technique that has been widely used for supervised dimensionality reduction of hyperspectral data. However, the use of LDA in hyperspectral remote sensing is limited due to its poor performance on small training datasets and the limited number of features that can be selected i.e., c − 1, where c is the number of classes in the data. To solve these problems, this article presents a folded LDA (F-LDA) for dimensionality reduction of remotely sensed HSI data in small sample size scenarios. The proposed approach allows many more discriminant features to be selected in comparison to the conventional LDA since the selection is no longer bound by the limiting factor, leading to significantly higher accuracy in the classification of pixels under SSS restrictions. The proposed approach is evaluated on five different datasets, where the experimental results demonstrate the superiority of the F-LDA to the conventional LDA in terms of not only higher classification accuracy but also reduced computational complexity, and reduced contiguous memory requirements.

A new approach to the local measurement of residual stress in microstructures is described in this paper. The presented technique takes advantage of the combined milling-imaging features of a focused ion beam (FIB) equipment to scale down the widely known hole drilling method. This method consists of drilling a small hole in a solid with inherent residual stresses and measuring the strains/displacements caused by the local stress release, that takes place around the hole. In the presented case, the displacements caused by the milling are determined by applying digital image correlation (DIC) techniques to high resolution micrographs taken before and after the milling process. The residual stress value is then obtained by fitting the measured displacements to the analytical solution of the displacement fields. The feasibility of this approach has been demonstrated on a micromachined silicon nitride membrane showing that this method has high potential for applications in the field of mechanical characterization of micro/nanoelectromechanical systems.

Neither of the classical visual servoing approaches, position-based and image-based, are completely satisfactory. In position-based visual servoing the trajectory of the robot is well stated, but the approach suffers mainly from the image features going out of the visual field of the cameras. On the other hand, image-based visual servoing has been found generally satisfactory and robust in the presence of camera and hand-eye calibration errors. However, in some cases, singularities and local minima may arise, and the robot can go further from its joint limits. This paper is a step towards the synthesis of both approaches with their particular advantages, i.e., the trajectory of the camera motion is predictable and the image features remain in the field of view of the camera. The basis is the introduction of three-dimensional information in the feature vector. Point depth and object pose produce useful behavior in the control of the camera. Using the task-function approach, we demonstrate the relationship between the velocity screw of the camera and the current and desired poses of the object in the camera frame. Camera calibration is assumed, at least coarsely. Experimental results on real robotic platforms illustrate the presented approach.

Our objective was to describe the imaging features of hypothenar hammer syndrome using minimally invasive contrast-enhanced MR angiography in comparison with oscillography study. In five patients with hypothenar hammer syndrome Gd-BOPTA-enhanced elliptically reordered 3D pulse sequence MR was compared with oscillography findings and clinical symptoms focusing on angiographic appearance of vessel injury, distribution pattern of hand vasculature and joining branches between the radial and ulnar artery supply. All patients showed segmental occlusion at the site of trauma impact with varying involvement of the superficial palmar arch, common volar digital arteries. Embolic disease was present in 50% of patients and could be clearly identified with MRA. Good correspondence was found between angiographic appearance including the presence of collaterals, clinical symptoms and oscillography. Bilateral comparison was helpful in distinguishing between vessel variants and pathology. Bilateral Gd-BOPTA-enhanced MR angiography is a minimally invasive method to depict clearly the localization and extent of vessel injury in hypothenar hammer syndrome providing valuable information about distribution pattern of hand vasculature and presence of collaterals; however, no flow data can be obtained.

Palmprint verification is an approach for verifying a palmprint input by matching the input to the claimed identity template stored in a database. If the dissimilarity measure between the input and the claimed template is below the predefined threshold value, the palmprint input is verified possessing same identity as the claimed identity template. This paper introduces an experimental evaluation of the effectiveness of utilizing three well known orthogonal moments, namely Zernike moments, pseudo Zernike moments and Legendre moments, in the application of palmprint verification. Moments are the most commonly used technique in character feature extraction. The idea of implementing orthogonal moments as palmprint feature extractors is prompted by the fact that principal features of both character and palmprint are based on line structure. These orthogonal moments are able to define statistical and geometrical features containing line structure information about palmprint. An experimen...

Purpose There is a need for methods which enable precise correlation of histologic sections with in vivo prostate images. Such methods would allow direct comparison between imaging features and functional or histopathological heterogeneity of tumors. Correlation would be particularly useful for validating the accuracy of imaging modalities, developing imaging techniques, assessing imageguided therapy, etc. An optimum prostate slicing method for accurate correlation between the histopathological and medical imaging planes in terms of section angle, thickness and level was sought. Methods Literature review (51 references from 1986-2009 were cited) was done on the various sectioning apparatus or techniques used to slice the prostate specimen for accurate correlation between histopathological data and medical imaging. Technology evaluation was performed with review and discussion of various methods used to section other organs and their possible applications for sectioning prostatectomy specimens.

Myocardial Infarction (MI) or acute MI (AMI),is one of the leading causes of death worldwide. Precise and timely identification of MI and extent of muscle damage helps in early treatment and reduction in the time taken for further tests. MI diagnosis using 2D echocardiography is prone to inter-/intra-observer variability in the assessment. Therefore, a computerised scheme based on image processing and artificial intelligent techniques can reduce the workload of clinicians and improve the diagnosis accuracy. A Computer-Aided Diagnosis (CAD) of infarcted and normal ultrasound images will be useful for clinicians. In this study, the performance of CAD approach using Discrete Wavelet Transform (DWT), second order statistics calculated from Gray-Level Co-Occurrence Matrix (GLCM) and Higher-Order Spectra (HOS) texture descriptors are compared. The proposed system is validated using 400 MI and 400 normal ultrasound images, obtained from 80 patients with MI and 80 normal subjects. The extracted features are ranked based on t-value and fed to the Support Vector Machine (SVM) classifier to obtain the best performance using minimum number of features. The features extracted from DWT coefficients obtained an accuracy of 99.5%, sensitivity of 99.75% and specificity of 99.25%; GLCM have achieved an accuracy of 85.75%, sensitivity of 90.25% and specificity of 81.25%; and HOS obtained an accuracy of 93.0%, sensitivity of 94.75% and specificity of 91.25%. Among the three techniques presented DWT yielded the highest classification accuracy. Thus, the proposed CAD approach may be used as a complementary tool to assist cardiologists in making a more accurate diagnosis for the presence of MI. (C) 2015 Elsevier Ltd. All rights reserved.

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The integration of photogrammetric images and lidar data is becoming a powerful procedure that can be applied in the optimisation of photogrammetric mapping techniques. The complementary nature of lidar and photogrammetric data optimises the performance of many procedures used to extract 3D spatial information from data. For example, photogrammetric imagery enables the accurate extraction of building borders and lidar provides accurate 3D points that give information on the physical surfaces of buildings. These properties demonstrate the usefulness of combining the two types of data to achieve a more robust and complete reconstruction of 3D objects. Photogrammetric procedures require the exterior orientation parameters (EOPs) of the images to extract mapping information. Despite the availability of GPS/INS systems, which greatly assist in direct georeferencing of the imagery, the majority of commercially available photogrammetric systems require control information in order to carry out photogrammetric mapping. Due to improvements in the accuracy of lidar systems in recent years, lidar data is considered a viable source of photogrammetric control. Point features are the principal source of control for photogrammetric triangulation, although linear features and planar patches have also been used. This paper presents a method of georeferencing photogrammetric images using lidar data. The method uses the centroids of rectangular building roofs as control points in the photogrammetric procedure. The centroid of a rectangular building roof derived using lidar data is equivalent to a single control point with 3D coordinates, and can therefore be used in traditional photogrammetric systems. Two photogrammetric experiments were carried out to verify the feasibility of the methodology. The results obtained from these experiments confirm the feasibility of applying the proposed methodology to the georeferencing of photogrammetric images using lidar data.L’intégration des données lidar avec les images photogrammétriques constitue une méthode puissante que l’on peut utiliser pour optimiser les techniques photogrammétriques en cartographie. La nature complémentaire des données photogrammétriques et lidar permet d’optimiser les performances de nombreux processus utilisés pour extraire de ces données des informations localisées et en 3D. Par exemple, l’imagerie photogrammétrique permet d’extraire avec précision les contours des bâtiments tandis que le lidar fournit avec précision des points en 3D qui nous renseignent sur la nature des surfaces de ces bâtiments. On conçoit donc l’utilité d’une combinaison de ces deux types de données pour obtenir une reconstruction plus robuste et plus complète d’objets en 3D. Les techniques photogrammétriques nécessitent de connaître les paramètres d’orientation externe des images pour en extraire les informations cartographiques. En dépit de l’existence des systèmes GPS et inertiels qui apportent une grande aide dans la géolocalisation des images, la plupart des systèmes photogrammétriques disponibles dans le commerce ont besoin d’un canevas de points d’appui pour effectuer une restitution cartographique. Etant donné les progrès réalisés ces dernières années dans la précision des systèmes lidar, on peut considérer que ces données lidar sont une source valable pour former ce canevas d’appui. Le canevas destinéà la triangulation photogrammétrique est essentiellement constitué de détails ponctuels, même si l’on utilise aussi des éléments linéaires ou surfaciques. On présente dans cet article une méthode pour géolocaliser les images photogrammétriques à partir de données lidar. Dans cette méthode on utilise les centroïdes des toits de bâtiments rectangulaires comme points d’appui photogrammétriques. Chaque centroïde issu des données lidar constitue l’un des points d’appui de ce canevas, déterminé par ses trois coordonnées et conséquent utilisable comme tel dans les systèmes photogrammétriques classiques. On a réalisé deux essais photogrammétriques pour s’assurer de la faisabilité de cette méthode. Les résultats obtenus ont confirmé qu’il était possible d’utiliser cette méthode pour géolocaliser les images photogrammétriques avec des données lidar.Die Integration photogrammetrischer Bilddaten und Laserscandaten, zweier komplementärer Datenquellen, bietet die Möglichkeit zur Optimierung der photogrammetrischen Erfassung räumlicher Information hinsichtlich Genauigkeit und Zuverlässigkeit. Die photogrammetrische Auswertung von Bilddaten erlaubt eine genaue Extraktion von Gebäudeumrisslinien, wohingegen Laserscandaten genaue 3D-Punkte auf den Gebäudeoberflächen bereitstellen. Die photogrammetrischen Verfahren benötigen die Parameter der Äußeren Orientierung (EOP) der Bilder, die in den meisten Fällen über die Messung von Passpunkten erfolgt, trotz der zunehmenden Verfügbarkeit von GPS/INS Systemen, die die direkte Georeferenzierung der Bilder unterstützen. Durch die Verbesserungen in der Genauigkeit der Laserscansysteme in den letzten Jahren können Laserscandaten als mögliche Grundlage für Passinformation für photogrammetrische Auswertungen eingesetzt werden. In der Photogrammetrie werden meist Punkte als Passinformation verwendet, obwohl auch schon lineare Merkmale oder ebene Flächen für die Bestimmung der äußeren Orientierung eingesetzt worden sind. Dieser Beitrag stellt eine Methode zur Georeferenzierung photogrammetrischer Bilder mit Hilfe von Laserscandaten vor. Die Methode stützt sich auf die Schwerpunkte rechteckiger Dachflächen von Gebäuden als Passpunkte in der photogrammetrischen Orientierung. An Hand von zwei photogrammetrischen Beispielen wird die Anwendbarkeit der Methode nachgewiesen.La integración de imágenes fotogramétricas y datos lidar se está convirtiendo en un procedimiento muy eficaz en la optimización de las técnicas de restitución fotogramétrica. El carácter complementario de los datos lidar y fotogramétricos permite optimizar muchos procedimientos de extracción de información espacial tridimensional de los datos. Por ejemplo, las imágenes fotogramétricas permiten la extracción exacta de contornos de edificios al tiempo que el lidar aporta datos tridimensionales exactos de las superficies físicas de los edificios. Estas propiedades ofrecen la ventaja de combinar los dos tipos de datos para conseguir una reconstrucción más robusta y completa de los objetos tridimensionales. Los procedimientos fotogramétricos requieren de parámetros de orientación externa de las imágenes para la restitución. Pese a la disponibilidad de sistemas integrados de navegación inercial y GPS para la georreferenciación directa de las imágenes, la mayoría de los sistemas fotogramétricos disponibles requieren de información de control para la restitución fotogramétrica. Con la mejora de la exactitud que los sistemas lidar han experimentada en los últimos años, sus datos se han convertido en una fuente asequible de control fotogramétrico. Los objetos puntuales son el principal tipo de información de control para la triangulación fotogramétrica, aunque también se han utilizado objetos lineales y teselas planas. Este artículo presenta un método de georreferenciación de imágenes fotogramétricas a partir de datos lidar utilizando los centroides de los tejados de edificios rectangulares como puntos de apoyo en el proceso fotogramétrico. El centroide de un tejado rectangular obtenido de los datos lidar es equivalente a un punto de apoyo con coordenadas tridimensionales y, por lo tanto, puede utilizarse en los sistemas fotogramétricos tradicionales. Se llevaron a cabo dos experimentos fotogramétricos para verificar la viabilidad de la metodología y los resultados confirman la aplicabilidad de esta metodología propuesta de georreferenciación de imágenes fotogramétricas mediante datos lidar.

The aim of this study is to provide an efficient
way to segment the malignant melanoma images. This
method first eliminates extra hair and scales using edge
detection; afterward, it deduces a color image into an
intensity image and approximately segments the image by
intensity thresholding. Some morphological operations are
used to focus on an image area where a melanoma
boundary potentially exists and then used to localize the
boundary in that area. The distributions of texture and a
new feature known as AIBQ features in the next step
provide a good discrimination of skin lesions to feature
extraction. Finally, we rely on quantitative image analysis
to measure a series of candidate attributes hoped to contain
enough information to differentiate malignant from benign
melanomas. The selected features are applied to a support
vector machine to classify the melanomas as malignant or
benign. By our approach, we obtained 95 % correct classification
of malignant or benign melanoma on real melanoma
images.

Images retrieval from a large database is an important and emerging search area. This retrieval requires the choice of a suitable set of image features, a method to extract them correctly, and a measure of the similarity between features that can be computed in real-time. This paper presents a complete set of Fourier-Mellin descriptors for object storage and retrieval. Our approach is translation, rotation and scale invariant. Seve ral retrieval examples in large image database are presented.

Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification.