Gabor filters

This paper presents a frame work for hardware acceleration for post video processing system implemented on FPGA. The deblocking filter algorithms ported on SOC having Altera NIOS-II soft core processor.SOC designed with the help of SOPC builder .Custom instructions are chosen by identifying the most frequently used tasks in the algorithm and the instruction set of NIOS-II processor has been extended. Deblocking filter new instruction added to the processor that are implemented in hardware and interfaced to the NIOS-II processor. New instruction added to the processor to boost the performance of the deblocking filter algorithm. Use of custom instructions the implemented tasks have been accelerated by 5.88%. The benefit of the speed is obtained at the cost of very small hardware resources.

An access control system by fingerprints is one of the most widely spread biometric techniques linked with ensuring the security of the enterprise. The uniqueness of fingerprints is something staying unchanged during the whole life. For example, such features as the face shape, manner of walking may change over time time but fingerprints will not. However, the fingerprint itself can give an image of a bad quality because of the state of the skin, bad impression, dirty surface of the finger, etc. That's why, the methods for image enhancement should be implemented to get more accurate results. This study discusses reliable techniques and propose a reliable algorithm of the fingerprint image enhancement for the access control system to be applied as a part of the security system in the company.

Automatic Fingerprint authentication for personal identification and verification has received considerable attention over the past decades among various biometric techniques because of the distinctiveness and persistence properties of fingerprints. Now fingerprints are set to explode in popularity as they are being used to secure smart phones and to authorize payments in online stores. The main objective of this paper is to review the extensive research work that has been done over the past decade and discuss the various approaches proposed for fingerprint matching. The proposed methods were based on 2D correlation in the spatial and frequency domains, Artificial Neural Networks, Hough transform, Fourier transform, graphs, local texture, ridge geometry etc. All these different techniques have their pros and cons. This paper also provides the performance comparison of several existing methods proposed by researchers in fingerprint matching.

This paper presents an overview of the methodologies and algorithms for statistical texture analysis of 2D images. Methods for digital-image texture analysis are reviewed based on available literature and research work either carried out or supervised by the authors.

Automatic Fingerprint authentication for personal identification and verification has received considerable attention over the past decades among various biometric techniques because of the distinctiveness and persistence properties of fingerprints. Now fingerprints are set to explode in popularity as they are being used to secure smart phones and to authorize payments in online stores. The main objective of this paper is to review the extensive research work that has been done over the past decade and discuss the various approaches proposed for fingerprint matching. The proposed methods were based on 2D correlation in the spatial and frequency domains, Artificial Neural Networks, Hough transform, Fourier transform, graphs, local texture, ridge geometry etc. All these different techniques have their pros and cons. This paper also provides the performance comparison of several existing methods proposed by researchers in fingerprint matching.

Automatic Fingerprint authentication for personal identification and verification has received considerable attention over the past decades among various biometric techniques because of the distinctiveness and persistence properties of fingerprints. Now fingerprints are set to explode in popularity as they are being used to secure smart phones and to authorize payments in online stores. The main objective of this paper is to review the extensive research work that has been done over the past decade and discuss the various approaches proposed for fingerprint matching. The proposed methods were based on 2D correlation in the spatial and frequency domains, Artificial Neural Networks, Hough transform, Fourier transform, graphs, local texture, ridge geometry etc. All these different techniques have their pros and cons. This paper also provides the performance comparison of several existing methods proposed by researchers in fingerprint matching.

Automation of plant recognition is an important process for the fields working with plants. This presents an approach for plant recognition using Vegetable images. In this study, the proponents demonstrated the development of the system that gives users the ability to identify vegetables based on photographs of the leaves taken with a high definition camera. The output parameters are used to compute well documented metrics for the statistical and shape. Base on the study, the following conclusion are drawn: The system can extract the physical parameters from the Vegetable image that will be used in identifying Vegetables. The aim is to build an accurate, fast and reliable vegetable detection system. From the extracted Vegetable parameters, the system provides the statistical analysis and general information of the identified Vegetable. At the heart of this system is a modernize process of identification, so as to automate the way of identifying the vegetable plants through Vegetable image and digital image processing. The system used the Gabor Filter, Edge Detection, RGB Color and Gray scale Image to acquire the physical parameter of the leaves.

Selamat datang di situs Pusat Filter Udara dan Filter Industri. distributor filter, filter udara, filter industri,bahan filter udara,bahan filter industri,jual filter udara,jual filter industri, Filter Udara Filter Udara atau lazim disebut 'Air Filter' adalah bagian dari perangkat kendaraan bermotor yang bertugas menyuplai oksigen ke ruang bakar di sebuah mesin. 'Air Filter' juga memiliki tugas menjaga kualitas udara agar tetap bersih, bebas dari debu dan material kasar (Padat). Saringan udara berbahan selat halus yang tahan terhadap perubahan suhu, memiliki kerapatan pori pori yang cukup ideal untuk menyaring kotoran, serta dilengkapi dengan minyak khusus yang berfungsi sebagai penahan dan menangkap partikel debu yang lebil kecil, sehingga filter bekerja maksimal. 'Air Filter' tidak berfungsi baik dan normal apabila kondisinya sudah kotor. Banyak ditemui kotoran yang menempel di permukaan filter dengan ciri-ciri kusam dan banyak endapan. Saringan udara juga dirancang cukup efektif agar bisa berfungsi maksimal, maka dari itu konstruksi dan desain dibuat untuk sekali pakai. Karena material tersebut sudah terakumulasi dengan kotoran yang sudah menempel. Pori pori saringan bekerja menahan kotoran sekaligus mengendapkan kotoran dibantu dengan minyak fiter agar kotoran yang sudah menempel tidak lepas. Maka dari itu filter udara yang sudah digunakan tidak dapat dibersihkan baik dengan pencucian maupun peniupan di karenakan endapan akan tetap di dalam serat filter dan akan menutupi seluruh pori pori sehingga filter tersebut tidak bisa dilalui udara (mampet).

The PhD (Pretty helpful Development functions for) face recognition toolbox is a collection of Matlab functions and scripts intended to help researchers working in the field of face recognition. The toolbox was produced as a byproduct of my research work and is freely available for download.

The PhD face recognition toolbox includes implementations of some of the most popular face recognition techniques, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA), Kernel Fisher Analysis (KFA). It features functions for Gabor filter construction, Gabor filtering, and all other tools necessary for building Gabor-based face recognition techniques.

In addition to the listed techniques there are also a number of evaluation tools available in the toolbox, which make it easy to construct performance curves and performance metrics for the face recognition you are currently assessing. These tools allow you to compute ROC (Receiver Operating Characteristics) curves, EPC (Expected performance curves) curves, and CMC (cumulative match score curves) curves.

Most importantly (especially for beginners in this field), the toolbox also contains several demo scripts that demonstrate how to build and evaluate a complete face recognition system. The demo scripts show how to align the face images, how to extract features from the aligned, cropped and normalized images, how to classify these features and finally how to evaluate the performance of the complete system and present the results in the form of performance curves and corresponding performance metrics.

This document describes the basics of the toolbox, from installation to usage. It contains some simple examples of the usage of each function and the corresponding results. However, for more information on the theoretical background of the functions, the reader is referred to papers from the literature, which is vast. Hence, it shouldn't be to difficult to find the information you are looking for.

Two traditions have had a great impact on the theoretical and experimental research of perception. One tradition is statistical, stretching from Fechner's enunciation of psychophysics in 1860 to the modern view of perception as statistical decision making. The other tradition is phenomenological, from Brentano's “empirical standpoint” of 1874 to the Gestalt movement and the modern work on perceptual organization. Each tradition has at its core a distinctive assumption about the indivisible constituents of perception: the just-noticeable differences of sensation in the tradition of Fechner vs. the phenomenological Gestalts in the tradition of Brentano. But some key results from the two traditions can be explained and connected using an approach that is neither statistical nor phenomenological. This approach rests on a basic property of any information exchange: a principle of measurement formulated in 1946 by Gabor as a part of his quantal theory of information. Here the indivisible components are units (quanta) of information that remain invariant under changes of precision of measurement. This approach helped to understand how sensory measurements are implemented by single neural cells. But recent analyses suggest that this approach has the power to explain larger-scale characteristics of sensory systems.

Plant is one of the most important forms of life on earth. Plant recognition is very demanding in biology and agriculture as new plant discovery and the computerization of the management of plant species
become more popular. The recognition is a process resulting in the assignment of each individual plant to a descending series of related plants in terms of their common characteristics. The process is very timeconsuming as it has been mainly carried out by botanists. Computer-aided plant recognition is still very challenging task in computer vision as the lack of proper models or representation schemes, a large number of variations of the plant species, and imprecise image pre-processing techniques, such as edge
detection and contour extraction. The focus of computerized living plant recognition is on stable feature’s extraction of plants. The information of leaf veins, therefore, play an important role in identifying living
plants. The ultimate goal of this project is to develop a system where a user in the field can take a picture of an unknown plant, feed it to the system carried on a portable computer, and have the system classify the species and display sample images of the closest matches within a couple of seconds.

Automatic Fingerprint authentication for personal identification and verification has received considerable attention over the past decades among various biometric techniques because of the distinctiveness and persistence properties of fingerprints. Now fingerprints are set to explode in popularity as they are being used to secure smart phones and to authorize payments in online stores. The main objective of this paper is to review the extensive research work that has been done over the past decade and discuss the various approaches proposed for fingerprint matching. The proposed methods were based on 2D correlation in the spatial and frequency domains, Artificial Neural Networks, Hough transform, Fourier transform, graphs, local texture, ridge geometry etc. All these different techniques have their pros and cons. This paper also provides the performance comparison of several existing methods proposed by researchers in fingerprint matching.

The floral industry has increasingly become one of the most important sectors for export earnings, especially in developing countries. However, during the cultivation process there may be a number of challenges that affect it, one of which is flower disease. This paper presents an automatic identification of of flower dieases based on image processing techniques. In view of this, normal and diseased flower images are acquired to create a knowledge base where images are pre-processed and segmented to identify the region of interest. Texture features of images are extracted using Gabor feature extraction, from which we computed seven different measures of dispersion and central tendency with the purpose of reducing the dimensionality of features. Then, an artificial neural network is trained with seven input features extracted from individual images and eight output nodes representing eight classes of diseases considered in this work. Unkown samples of flower images are then tested based on the training model and we achieved an average accuracy of 83.3% in the identification of the flower diseases.

Face recognition is a field of computer vision that uses faces to identify or verify a person. In this paper, we present a neural network system for face recognition. Feature vector based on Fourier Gabor filters are used as input of the Back Propagation Neural Network (BPNN). To extract the features vector of the whole face in image, we use an algorithm for detecting skin human faces in color images and then we introduce Gabor filters with 8 different orientations and 5 different resolutions to get maximum information. Experiments show that the proposed method yields results.

Security system comprised of a single form of biometric information cannot fulfil user’s expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process.In this paper we propose a Efficient and Robust Multimodal Biometric System for feature level fusion that combines the information to investigate whether the integration of fingerprints and signatures . Proposed system extracts Gabor texture from the preprocessed fingerprints and signatures sample. The feature vectors attained from different methods are in different sizes and the features from equivalent image may be correlated. Therefore, we proposed wavelet-based fusion techniques. Finally apply neural network’s Cascaded feed forward Back propagation Algorithm to Train Neurons for recognition.proposed approach is authenticated for their accuracy on Fingerprints virtual database fused with signature virtual database of 16 users. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 99.8% and with false rejection rate (FRR) of = 1%

A document image, beside text, may contain pictures, graphs, signatures, logos, bar-codes, hand-drawn sketches and/or seals.  Further, the text blocks in an image may be in Manhattan or any complex layout. Document Layout Analysis is an important preprocessing step before subjecting any such image to OCR. Here, the image with complex layout and content is segmented into its constituent components. For many present day applications, separating the text from the non-text blocks is sufficient. This enables the conversion of the text elements present in the image to their corresponding editable form.

In this work, an effort has been made to separate the text areas from the various kinds of possible non-text elements. The document images may have been obtained from a scanner or camera. If the source is a scanner, there is control on the scanning resolution, and lighting of the paper surface. Moreover, during the scanning process, the paper surface remains parallel to the sensor surface. However, when an image is obtained through a camera, these advantages are no longer available. Here, an algorithm is proposed to separate the text present in an image from the clutter, irrespective of the imaging technology used. This is achieved by using both the structural and textural information of the text present in the gray image. A bank of Gabor filters characterizes the statistical distribution of the text elements in the document. A connected component based technique removes certain types of non-text elements from the image.

When a camera is used to acquire document images, generally, along with the structural and textural information of the text, color information is also obtained. It can be assumed that text present in an image has a certain amount of color homogeneity. So, a graph-theoretical color clustering scheme is employed to segment the iso-color components of the image. Each iso-color image is then analyzed separately for its structural and textural properties. The results of such analyses are merged with the information obtained from the gray component of the image. This helps to separate the colored text areas from the non-text elements.

The proposed scheme is computationally intensive, because the separation of the text from non-text entities is performed at the pixel level. Since any entity is represented by a connected set of pixels, it makes more sense to carry out the separation only at specific points, selected as representatives of their neighborhood. Harris' operator evaluates an edge-measure at each pixel and selects pixels, which are locally rich on this measure. These points are then employed for separating text from non-text elements.

Many documents in India and at other places of the world are bi-lingual or tri-lingual in nature. In these documents, successive words in a line of text may be of different scripts (languages). Hence, for OCR of these documents, the script must be recognized at the level of words, rather than lines or paragraphs. A database of about 20,000 words each from 11 Indian scripts, namely, Bengali, Devanagari, Gujarati, Kannada, Malayalam, Odiya, Punjabi, Roman, Tamil, Telugu and Urdu, is created. This is so far the largest database of Indian words collected and deployed for script recognition purpose. Here again, a bank of 36 Gabor filters is used to extract the feature vector which represents the script of the word. The effectiveness of Gabor features is compared with that of DCT and it is found that Gabor features marginally outperform the DCT. Simple, linear and non-linear classifiers are employed to classify the word in the feature space.

It is assumed that a scheme developed to recognize the script of the words would work equally fine for sentences and paragraphs. This assumption has been verified with supporting results. A systematic study has been conducted to evaluate and compare the accuracy of various feature-classifier combinations for word script recognition. We have considered the cases of bi-script and tri-script documents, which are largely available. Average recognition accuracies for bi-script and tri-script cases are 98.4% and 98.2%, respectively. A hierarchical blind script recognizer, involving all eleven scripts has been developed and evaluated, which yields an average accuracy of 94.1%.

While ridges and furrows on the surface of rock glaciers are probably the most characteristic morphological features of this expression of creeping mountain permafrost, this is the first study that examines the utility of texture filters in detecting rock glacier flow structures. Texture features were derived from a Gabor filterbank applied to two panchromatic IKONOS orthoimages from the Chilean Andes, and terrain attributes were obtained in order to narrow down the area of interest and as additional predictor variables. Four classification methods of different complexity (generalized linear model, GLM; generalized additive model, GAM; support vector machine, SVM; Bundling) were applied to three sets of predictor variables (texture attributes, terrain attributes, and the combination of both), and predictive performances were estimated using two spatial cross-validation strategies in terms of the median area under the ROC (receiver operating characteristics) curve (AUROC). Overall, classifiers utilizing texture attributes alone or, usually even better, in combination with terrain attributes outperformed terrain attributes alone, and Bundling (in most cases followed by SVM) showed the overall best performance as a classification method. Cross-validated median AUROC values were mostly between 0.70 and 0.80, or “fair,” in this focused pilot study, but much better results are to be expected at the landscape scale. Permutation-based spatial variable importance measures indicate that Gabor features corresponding to texture wavelengths between 10 and 30 m are the most important predictors, which is consistent with typical ridge spacing on rock glaciers. Confounding occurred mostly in areas with linear erosion features and ridges or scarps in morainic and thermokarst terrain. We suggest that texture filters and the chosen methodological approach are relevant not only for rock glacier mapping but also for delineating debris-covered glaciers and characterizing glacier surfaces, as well as for other geomorphological and cryospheric applications.

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.

The most prominent reason for the death of women all over the world is breast cancer. Early detection of cancer helps to lower the death rate. Mammography scans determine breast tumors in the first stage. As the mammograms have slight contrast, thus, it is a blur to the radiologist to recognize micro growths. A computer-aided diagnostic system is a powerful tool for understanding mammograms. Also, the specialist helps determine the presence of the breast lesion and distinguish between the normal area and the mass. In this paper, the Gabor filter is presented as a key step in building a diagnostic system. It is considered a sufficient method to extract the features. That helps us to avoid tumor classification difficulties and false-positive reduction. The linear support vector machine technique is used in this system for results classification. To improve the results, adaptive histogram equalization pre-processing procedure is employed. Mini-MIAS database utilized to evaluate this method. The highest accuracy, sensitivity, and specificity achieved are 98.7%, 98%, 99%, respectively, at the region of interest (30×30). The results have demonstrated the efficacy and accuracy of the proposed method of helping the radiologist on diagnosing breast cancer.

The performance of any Automatic Fingerprint Identification System AFIS depends highly on the acquired fingerprint image quality, which is mostly exposed to degradation and noise during the acquisition process or because of fingerprint sweat, smut ..etc. Most AFIS use some form of image enhancement in order to improve the recognizer performance. This paper presents a simple modification to the enhancement method based on Gabor filtering algorithm in order to improve image quality. It has been noticed that the computations of the local ridge direction and also the enhancement is affected heavily by the block size used therefore, an attempt has been made to develop a more accurate approach wwhich is dynamically decides the block size. It is based on the local ridge frequency. The proposed enhancement algorithm has been tested to fingerprint images from FVC2002 and it shows a promising result. Keywords-Fingerprint, image enhancement, Gabor filter, block size. L Introduction In an ideal fingerprint image, ridges and valleys flow in locally constant directions and minutiae can easily be detected by following the detected ridge lines to their endings and bifurcations (figure 1). However, in practice, this is not always the case. Actually, the fingerprint image can get corrupted due to a number of reasons. For example, the subject can have a non-cooperative attitude when giving the sample, there can be some sort of environmental condition that is not suitable for the scanning device or the user might have dry hands (7]. In 141 it is found that about 10 percent of all fingerprint images captured are of poor quality. This leads to the following main problems such as, number of spurious minutiae might be introduced, a number of genuine minutiae might be lost and large errors in minutiae orientation and position may be introduced. To overcome these problems, a number of fingerprint image enhancement techniques have been proposed in the literature [7]. In AFIS (Automatic Fingerprint Identification Systems) most operations are performed on block level such as estimating directional field, segmentation or singular point detection, therefore it is important to select a suitable block size for optimal extraction of features for fingerprint image, as it affects the computation of directional field. When values of block size are small they cause spurious patterns to appear while large values level out important ridge singularity information 131. Several approaches to compute the block size dynamically to be suitable for each fingerprint have been proposed in literature. In [1] the block directional field computed with a constant blok size then, a onsistenc level criteria is evaluated if it is above a certain threshold, then the local orientations around this region are re-estimated at a lower resolution, block size, level until the consistency level is below a certain level. In [3] the proposed estimation method is, unlike in [4] that the ridge frequency is computed for each block of the image, in order to reduce the computational cost, the average ridge distance is computed once for the whole input image. In order to compute a correct average ridge distance value a search is done all over the block-direction image to find a region having the least variance in direction senlse, and hence to find a clear foreground area. According to the search result, the determined block and its nine neighboring blocks are selected to be used in the process. Based on the corresponding direction the summation along the ridge-dominant direction is

We explore the use of image analysis techniques for the classification of archaeological ceramic materials according to one aspect of their petrographic characterization. Specifically, we study the use of Gabor filter-based texture features, Laws' texture measures, and Haralick's texture measures for automated classification of a set of archaeological ceramic samples from two different Egyptian source materials: Marl clay and Nile clay. The motivation behind this work is the desire to pioneer the introduction of fully automated methods for pattern classification into the domain of archaeological science, where these can be extremely useful. The texture features are all extracted in a completely automated fashion and the classification is performed via a simple classification algorithm, the k-NN classifier. An accuracy of nearly 74% was obtained with base on the Laws' texture features.

The floral industry has increasingly become one of the most important sectors for export earnings, especially in developing countries. However, during the cultivation process there may be a number of challenges that affect it, one of which is flower disease. This paper presents an automatic identification of of flower dieases based on image processing techniques. In view of this, normal and diseased flower images are acquired to create a knowledge base where images are pre-processed and segmented to identify the region of interest. Texture features of images are extracted using Gabor feature extraction, from which we computed seven different measures of dispersion and central tendency with the purpose of reducing the dimensionality of features. Then, an artificial neural network is trained with seven input features extracted from individual images and eight output nodes representing eight classes of diseases considered in this work. Unkown samples of flower images are then tested ba...

For hyperspectral image (HSI) classification, it is challenging to adopt the methodology of sparse-representation-based classification. In this paper, we first propose an 1 -minimization-based spectral-spatial classification method for HSIs via a spatial translation-invariant wavelet (STIW)-based sparse representation (STIW-SR), wherein both the spectrum dictionary and the analyzed signal are formed with STIW features. Due to the capability of a STIW to reduce both the observation noise and the spatial nonstationarity while maintaining the ideal spectra, which is proved with our signal-interference-noise spectrum model involved, it is expected that the pixels in the same class congregate in a lower dimensional subspace, and the separations among class-specific subspaces are enhanced, thus yielding a highly discriminative sparse representation. Then, we develop an approach to evaluate the sparsity recoverability of an 1 -minimization on HSIs in a probabilistic framework. This approach takes into account not only the recovery probability under the given support length of the 0 -norm solution but also the a priori probability of the support length; consequently, it overcomes the inability of traditional mutual/cumulative coherence conditions to address high-coherence HSIs. This paper reveals that the higher sparsity recoverability of a STIW-SR leads to its higher classification accuracy and that the increasing coherence does not necessarily lead to a reduced sparsity recovery probability, and this paper verifies the connection between 0and 1 -minimizations on HSIs. Experimental results from realworld HSIs suggest that our classification method significantly outperforms several representative spectral-spatial classifiers and support vector machines.

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. In this paper, we first extend the well-known Gabor filters to color images using a specific form of hypercomplex numbers known as quaternions. These filters are constructed as windowed basis functions of the quaternion Fourier transform also known as hypercomplex Fourier transform. Based on this extension this paper presents the use of these new quaternionic Gabor filters in colour texture image segmentation. Experimental results on two colour texture images are presented. We tested the robustness of this technique for segmentation by adding Gaussian noise to the texture images. Experimental results indicate that the proposed method gives better segmentation results even in the presence of strongest noise.

Meningioma brain tumour discrimination is challenging as many histological patterns are mixed between the different subtypes. In clinical practice, dominant patterns are investigated for signs of specific meningioma pathology; however the simple observation could result in inter- and intra-observer variation due to the complexity of the histopathological patterns. Also employing a computerised feature extraction approach applied at a single resolution scale might not suffice in accurately delineating the mixture of histopathological patterns. In this work we propose a novel multiresolution feature extraction approach for characterising the textural properties of the different pathological patterns (i.e. mainly cell nuclei shape, orientation and spatial arrangement within the cytoplasm). The pattern textural properties are characterised at various scales and orientations for an improved separability between the different extracted features. The Gabor filter energy output of each magnitude response was combined with four other fixed-resolution texture signatures (2 model-based and 2 statistical-based) with and without cell nuclei segmentation. The highest classification accuracy of 95% was reported when combining the Gabor filters energy and the meningioma subimage fractal signature as a feature vector without performing any prior cell nuceli segmentation. This indicates that characterising the cell-nuclei self-similarity properties via Gabor filters can assists in achieving an improved meningioma subtype classification, which can assist in overcoming variations in reported diagnosis.

In this paper, we present a computationally efficient statistical face modeling approach. The efficiency of our proposed approach is the result of mathematical simplifications in the core formula of a previous face modeling method and the use of the singular value decomposition. In order to reduce the errors in our resulting models, we preprocess the facial images to normalize for pose and illumination and remove little occlusions. Then, the statistical face models for the enrolled subjects are obtained from the normalized face images. The effects of the variations in pose, facial expression, and illumination on the accuracy of the system are studied. Experimental results demonstrate the reduction in the computational complexity of the new approach and its efficacy in modeling the face images.

Image segmentation is a technique to locate certain objects or boundaries within an image. Image segmentation plays a crucial role in many medical imaging applications. There are many algorithms and techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in high resolution image for image segmentation due to variability of spectral and structural information.Thus the spatial pattern or texture techniques are used. Thus the concept of Holder Exponent for segmentation of high resolution medical image is an efficient image segmentation technique. The proposed method is implemented in Matlab and verified using various kinds of high resolution medical images. The experimental results shows that the proposed image segmentation system is efficient than the existing
segmentation systems.

The Ground Probing Radar (GPR) is a valuable tool for near surface geological, geotechnical, engineering, environmental, archaeological and other work. GPR images of the subsurface frequently contain geometric information (constant or variable-dip reflections) from various structures such as bedding, cracks, fractures etc. Such features are frequently the target of the survey; however, they are usually not good reflectors and they are highly localized in time and in space. Their scale is therefore a factor significantly affecting their detectability. At the same time, the GPR method is very sensitive to broadband noise from buried small objects, electromagnetic anthropogenic activity and systemic factors, which frequently blurs the reflections from such targets.

This paper introduces a method to de-noise GPR data and extract geometric information from scale-and-dip dependent structural features, based on one-dimensional B-Spline Wavelets, two-dimensional directional B-Spline Wavelet (BSW) Filters and two-dimensional Gabor Filters. A directional BSW Filter is built by sidewise arranging s identical one-dimensional wavelets of length L, tapering the s-parallel direction (span) with a suitable window function and rotating the resulting matrix to the desired orientation. The length L of the wavelet defines the temporal and spatial scale to be isolated and the span determines the length over which to smooth (spatial resolution). The Gabor Filter is generated by multiplying an elliptical Gaussian by a complex plane wave; at any orientation the temporal or spatial scale(s) to be isolated are determined by the wavelength.λ of the plane wave and the spatial resolution by the spatial aspect ratio γ, which specifies the ellipticity of the support of the Gabor function. At any orientation, both types of filter may be tuned at any frequency or spatial wavenumber by varying the length or the wavelength respectively. The filters can be applied directly to two-dimensional radargrams, in which case they abstract information about given scales at given orientations. Alternatively, they can be rotated to different orientations under adaptive control, so that they remain tuned at a given frequency or wavenumber and the resulting images can be stacked in the LS sense, so as to obtain a complete representation of the input data at a given temporal or spatial scale.

In addition to isolating geometrical information for further scrutiny, the proposed filtering methods can be used to enhance the S/N ratio in a manner particularly suitable for GPR data, because the frequency response of the filters mimics the frequency characteristics of the source wavelet. Finally, signal attenuation and temporal localization are closely associated: low attenuation interfaces tend to produce reflections rich in high frequencies and fine-scale localization as a function of time. Conversely, high attenuation interfaces will produce reflections rich in low frequencies and broad localization. Accordingly, the temporal localization characteristics of the filters may be exploited to investigate the characteristics of signal propagation (hence material properties). The method is shown to be very effective in extracting fine to coarse scale information from noisy data and is demonstrated with applications to noisy GPR data from archaeometric and geotechnical surveys.

Face recognition has been a fast growing, challenging and fascinating area in real time applications. A large number of face recognition algorithms have been developed in last decades. This paper presents a face recognition approach that utilizes the Sobel operator, Local Ternary Pattern (LTP) descriptor, Gabor features and Principal Component Analysis (PCA) algorithms to attain enhanced recognition accuracy where training set has only one image per person. In particular, the edge information of face image is enhanced using Sobel operator. Then we use LTP on the image to encode the micro-level information of spots, edges and other local characteristics. Finally Gabor-wavelets based features are then extracted and their histograms are concatenated together into a contiguous histogram to be used as a descriptor of the face. As Gabor features cause a very high dimensional histogram vector, therefore PCA is used to reduce the dimension. This approach is compared with the original LBP, LTP and Sobel LTP on gray-level images for face recognition. The experimental results exhibit that our method provides a remarkable performance under various conditions.

The amount of remote sensed imagery that has become available by far surpasses the possibility of manual analysis. One of the most important tasks in the analysis of remote sensed images is land use classification. This task can be recast as semantic classification of remote sensed images. In this paper we evaluate classifiers for semantic classification of aerial images. The evaluated classifiers are based on Gabor and
Gist descriptors which have been long established in image classification tasks. We use support vector machines and propose a kernel well suited for using with Gabor descriptors. These simple classifiers achieve correct classification rate of about 90% on two datasets. From these results follows that, in aerial image classification, simple classifiers give results comparable to more complex approaches, and the pursuit for more advanced solutions should continue having this in mind.

Image forgery detection becomes critical in the current environment since transfer of information now a day is through digital medium. Problem starts to appear as malicious users start to participate over the network. Most common type of forgery in such situation is copy and move. The proposed work uses the application of Gabor filter to detect and prevent copy move forgery and also predict the result in terms of accuracy.

In the production process of fabric, defect detection plays an important role in the control of product quality. Consider that traditional manual fabric defect detection method are time-consuming and inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can infer the attention map from the intermediate feature map and multiply the attention map to adaptively
refine the feature. This method improve the performance of classification and detection without increase the computation-consuming. The experiment results show that Faster RCNN with attention module can efficient improve the classification accuracy.

Computer-modeled characters resembling real people sometimes elicit cold, eerie feelings. This effect, called the uncanny valley, has been attributed to uncertainty about whether the character is human or living or real. Uncertainty, however, neither explains why anthropomorphic characters lie in the uncanny valley nor their characteristic eeriness. We propose that realism inconsistency causes anthropomorphic characters to appear unfamiliar, despite their physical similarity to real people, owing to perceptual narrowing. We further propose that their unfamiliar, fake appearance elicit cold, eerie feelings, motivating threat avoidance. In our experiment, 365 participants categorized and rated objects, animals, and humans whose realism was manipulated along consistency-reduced and control transitions. These data were used to quantify a Bayesian model of categorical perception. In hypothesis testing, we found reducing realism consistency did not make objects appear less familiar, but only animals and humans, thereby eliciting cold, eerie feelings. Next, structural equation models elucidated the relation among realism inconsistency (measured objectively in a 2D Morlet wavelet domain inspired by the primary visual cortex), realism, familiarity, eeriness, and warmth. The fact that reducing realism consistency only elicited cold, eerie feelings toward anthropomorphic characters, and only when it lessened familiarity, indicates the role of perceptual narrowing in the uncanny valley.

The shape, orientation and distribution of brushstrokes are distinctive markers left by the artist on the surface of a painting. Consequently, they are useful as additional evidence to decide on issues of attribution and authenticity. This paper proposes an image classification algorithm for the paintings of Amadeo de Souza-Cardoso, a modern Portuguese artist who is at the top of the Portuguese police’s list of forged artists. The proposed system is based on a binary classification using bag-of-features (SIFT descriptors and a Gabor filter) for image representation. Experimental results using the algorithm show that the classification of the paintings by extracting brushstroke information is very promising.

Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral image classification and exploit the benefits of using spatial features for the kernel based ELM (KELM) classifier. Specifically, Gabor filtering and multihypothesis (MH) prediction preprocessing are two approaches employed for spatial feature extraction. Gabor features have currently been successfully applied for hyperspectral image analysis due to the ability to represent useful spatial information. MH prediction preprocessing makes use of the spatial piecewise-continuous nature of hyperspectral imagery to integrate spectral and spatial information. The proposed Gabor-filtering-based KELM classifier and MH-prediction-based KELM classifier have been validated on two real hyperspectral datasets. Classification results demonstrate that the proposed methods outperform the conventional pixel-wise classifiers as well as Gabor-filtering-based support vector machine (SVM) and MH-prediction-based SVM in challenging small training sample size conditions.

Segmentation of the vessels in retina images is an essential step in earlier diagnosis of diabetic and hypertension. The application of image analysis using segmentation could assist the ophthalmologists, to detect the signs of diabetic retinopathy in the early stages. In this paper, a bank of 180 Gabor filters is used to capture high frequency information among which the maximum response for each pixel is selected. The vessels in the filtered retina images are segmented using a threshold value that passes the informative pixels and rejects the insignificant pixels. Determination of an effective threshold is of utmost importance for reliable segmentation which leads reliable vessel detection. The paper proposes a systematic way of determining the threshold value for reliable performance. The proposed approach is applied to retinal images from DRIVE retina database. The performance of the proposed vessel segmentation approach reaches to 0.95 based on the area under the receiver operating characteristic curve.

Vehicle license plate location is a key factor of the vehicle plate recognition system. This paper aims at developing a novel method for locating license plates, from both complex and close-up images, based on extracting areas with high vertical edge density and variance parameters.

Face recognition has been a fast growing, challenging and fascinating area in real time applications. A large number of face recognition algorithms have been developed in last decades. This paper presents a face recognition approach that utilizes the Sobel operator, Local Ternary Pattern (LTP) descriptor, Gabor features and Principal Component Analysis (PCA) algorithms to attain enhanced recognition accuracy where training set has only one image per person. In particular, the edge information of face image is enhanced using Sobel operator. Then we use LTP on the image to encode the micro-level information of spots, edges and other local characteristics. Finally Gabor-wavelets based features are then extracted and their histograms are concatenated together into a contiguous histogram to be used as a descriptor of the face. As Gabor features cause a very high dimensional histogram vector, therefore PCA is used to reduce the dimension. This approach is compared with the original LBP, LTP and Sobel LTP on gray-level images for face recognition. The experimental results exhibit that our method provides a remarkable performance under various conditions.

This paper proposes an image quality metric (IQM) using compressive sensing (CS) and a filter set consisting of derivative and Gabor filters. In this paper, compressive sensing that is used for acquiring a sparse or compressible signal with a small number of measurements is used for measuring the quality
between the reference and distorted images. However, an image is generally neither sparse nor compressible, so a CS technique cannot be directly used for image quality assessment. Thus, for converting an image into a sparse or compressible signal, the image is convolved with filters such as the gradient, Laplacian of Gaussian, and Gabor filters, since the filter outputs are generally compressible. A small number of measurements obtained by a CS technique are used for evaluating the image quality. Experimental results with various test images show the effectiveness of the proposed algorithm in terms of the Pearson correlation coefficient (CC), root mean squared error, Spearman rank order CC, and Kendall CC.

Results cont. II. Receptors down-regulation  Binding levels of [ 3 H]CGP-12177 were not decreased after chronic treatment with the different classes of drugs. III. Proteins function and levels after chronic ADs  All antidepressant classes increased the active form of b-arrestin1 in the cytosolic fraction.  All antidepressant classes significantly decreased b-arrestin2 and phospho-ERK1/2 levels in the cytosolic fraction.  No significant change was detected in GRK2 levels in the cytosolic fraction.

Aerial Images are a valuable data source for earth observation, can help us to measure and observe detailed structures on the Earth's surface. Aerial images are drastically growing. This has given particular urgency to the quest for how to make full use of ever-increasing Aerial images for intelligent earth observation. Hence, it is extremely important to understand huge and complex Aerial images. Aerial image classification, which aims at labeling Aerial images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, Aerial image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, recent achievements regarding deep learning for scene classification of Aerial images is still lacking.