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In the modern era, neurosyphilis in immunocompetent individuals is very rare compared to pre-antibiotic era. However, the incidence is still a few folds higher in immunodeficient individuals, especially in patients with human immunodeficiency virus (HIV) infection. The cases we found, involved mostly the brain with eye involvement was found in only five other instances. Ocular syphilis is a type of neurosyphilis that can threaten vision leading to blindness if not treated promptly. Its diagnosis can be challenging but it should be considered if the neuro-ophthalmological findings cannot be explained by any other commonly encountered disease and diagnosis by serology should be considered as it is a potentially treatable condition (1). We present a 27 years old male with HIV, not on any therapy who came to the hospital with complete loss of vision in the right eye for 4 months and partial loss of vision in the left eye for 5 weeks. He also had 6-month history of maculopapular rash over his trunk that extended to involve the entire body including the palms prior to presentation. He denied any redness, watering, or itchiness of the eyes, or focal neurological deficits. He also denied a history of diabetes, sexually transmitted infections (STIs), or recent trauma to the eyes. On physical exam, he was cachectic. His visual acuity was less than 20/200 in right eye and 20/100 in left eye. There was desquamative rash in his palms, but not in the trunk or genitals, and had no lymphadenopathy. Figure 1: Desquamative rash on the palms Normal lab included complete blood count, hepatic, renal, thyroid function tests, fasting blood glucose, electrolytes, erythrocyte sedimentation rate, C-reactive protein. Cerebrospinal fluid analysis showed lymphocytic pleocytosis, low glucose and high protein level. A diagnosis of neurosyphilis was confirmed with reactive serum rapid plasma Reagin (RPR) test and positive CSF venereal disease research laboratory (VDRL) test. His CD4 count was 418/mcL and viral load was 289,000/mcL. He tested negative for other STIs including genital herpes, Tuberculosis (TB), Hepatitis and gonorrhea. Intravenous penicillin G was administered for 10 days. HIV treatment was held to avoid immune reconstitution inflammatory syndrome (IRIS). He was discharged with better vision to follow-up with ophthalmology and primary care in order to initiate anti-retroviral therapy. He was advised to observe safe sex practices. Outpatient ophthalmology workup with B-scan, fundoscopy, and optical coherence tomography (OCT) of the macula showed panuveitis, vitritis and disc hyperemia without signs of raised intraocular pressure. In 3 months, he completely regained vision in his left eye and had partial recovery of vision in the right eye. Discussion: Painless vision loss without accompanying symptoms or trauma in a young individual without any obvious cause like diabetes presents a wide differential diagnosis that can be categorized into compressive, mitochondrial, vascular, infectious versus noninfectious optic neuritis, and infiltrative etiologies. Accordingly pituitary lesions, meningioma, slowly-growing aneurysm, leukemia, and lymphoma were considered but were less likely due to absence of raised intraocular pressure. Noninfectious causes including sarcoidosis and infectious causes like TB were considered, but were not likely given absence of constitutional symptoms. Bilateral anterior ischemic optic neuropathy was also considered, but our patient lacked the typical acute onset of vision loss commonly consistent with this disease. Our patients’ HIV history pointed towards an infectious etiology. A reactive serum RPR and CSF VDRL confirmed neurosyphilis while disc hyperemia on ophthalmoscopy and the positive response to IV antibiotic eventually validated the diagnosis. Ocular syphilis commonly causes posterior uveitis and panuveitis though may occasionally cause anterior uveitis, optic neuropathy, retinal vasculitis and interstitial keratitis. It may lead to decreased visual acuity and could eventually cause permanent blindness. It can occur at any stage of syphilis, including primary and secondary syphilis (2). Diagnosis requires a high suspicion as a negative CSF VDRL does not rule out neurosyphilis and only 35% cases of ocular syphilis have neurosyphilis. The recommended adult regimen is IV aqueous crystalline penicillin G 18-24 million units per day (either as continuous infusion or 3-4 million units every 4 hours) for 10-14 days. Alternative regimen for adults is procaine penicillin 2.4 million units IM per day and oral probenecid 500mg four times per day, also for 10-14 days (3). Co-administration of corticosteroids or immunosuppressants along with antibiotics is controversial. Oral steroids, along with supplements, are often required to control ocular inflammation. The use of intravitreal dexamethasone implant for refractory macular edema in syphilitic uveitis has also been reported (4). However, steroids should not be started before starting antibiotics as it could worsen disease to a life and/or sight-threatening degree (5). Patients treated for syphilis should have the VDRL test repeated every three months for a period of 1-year post-treatment as titers should become nonreactive within a year after therapy. Patients should be retreated, if an initially high-titer VDRL test does not decrease fourfold within a year, or if a previously non-reactive VDRL test becomes reactive again (6). Factors associated with poor visual prognosis include the time between onset of uveitis and treatment (>12 weeks), longer duration of ocular symptoms (>28 days) as seen with our patient, presence of macular edema or long-standing optic neuropathy, coinfection with HIV, and poor initial visual acuity. Factors associated with higher success rates included the presence of vasculitis (as detected by fundus fluorescence angiography), anterior uveitis, or neurosyphilis. Common long-term complications of syphilitic uveitis include glaucoma, cataract, epiretinal membrane and macular edema. Choroidal neovascularization and widespread chorioretinal scarring can occur in some patients. The Jarisch-Herxheimer reaction following initiation of antibiotic therapy, may result in fever, malaise and headache, as well as a worsening of ocular manifestations and may be prevented by administration of systemic corticosteroids concurrent with antibiotic treatment (7). Conclusion: Ocular syphilis, although a rare entity, if overlooked in the early stages, can lead to irreversible damage to eyes. Early diagnosis and treatment can prevent permanent blindness. References: 1. Taylor MM, Aynalem G, Olea LM, He P, Smith LV, Kerndt PR. A consequence of the syphilis epidemic among men who have sex with men (MSM): neurosyphilis in Los Angeles, 2001-2004. Sex Transm Dis. 2008;35(5):430. 2. Jon D. W., Dean E., J. Michael J., Emmett T. C. How to Recognize Ocular Syphilis. Review of Ophthalmology. November 2008. 3. Zachary W., Shruthi H.B., Andrew G.L., Nagham A.Z., Nita B. Ophthalmologic Manifestations of Syphilis. American academy of ophthalmology. September 21, 2019 4. Dutta Majumder P, Mayilvakanam L, Palker AH, Sridharan S, Biswas J. Intravitreal sustained-release dexamethasone implant for the treatment of persistent cystoid macular edema in ocular syphilis. Indian J Ophthalmol. 2019 Sep;67(9):1487-1490. 5. Agarwal M, Ranjan R, Paul L, Sharma D. Syphilitic uveitis misdiagnosed as viral retinitis-a misleading history. J Ophthalmic Inflamm Infect. 2018 Dec 04;8(1):22. 6. Jordana G.F., Musa A. Recognizing Ocular Syphilis. Medical Retina, May/June 2018. 7. Vikram V. K., Koushik T. Syphilis Ocular Manifestations. StatPearls; 2021 Jan.

The construction method of the covert channel has always been a topic of research and exploration in the field of information security, and the use of IP data packets for covert channel construction is also an important method. Based on the above content, this paper proposes an enlarging-the-capacity packet sorting covert channel. We establish enlarging-the-capacity packet sorting covert channel model and derive the functional relationship between the total number of covert information transmitted and the number of ports. This method can send more secret information when the network status is not ideal. The simulation results show that the covert channel of the extended packet sorting has high performance in different packet loss rates and delays, and is superior to other IP covert channel.

Voice-over IP (VoIP) technology is a kind of digital transmission technology based on IP network. It is one of the important methods to use voice service in VoIP as steganographic carrier to ensure secure transmission. However, the traditional steganographic code has some problems, such as low embedding efficiency and weak concealment, which cannot meet the requirements of VoIP streaming media information hiding for the security of secret information. Therefore, a steganographic algorithm combining F5 and simplified wet paper code (SWPC) algorithm is proposed. The main idea is to embed secret messages in each row of the carrier matrix using the F5 algorithm, and then the SWPC algorithm is used to embed the columns according to the wet and dry characteristics of the wet paper code without affecting the results before row embedding. We use the VoIP streams encoded by the ITU-T G.729a codec as a carrier to verify the proposed scheme. The experimental results demonstrate that the proposed scheme can achieve relatively better IP speech data steganographic transparency and that it can outperform F5-WPC and SWPC approaches.

Objective: To assess the level of symptoms control achieved by Asthmatic Patients in Pakistan and the associated factors making an impact on symptom control. Methods: A prospective cross-sectional study was carried out at Darul Sehat Hospital from December 2018 till December 2019. All patients of irrespective of age and gender diagnosed as bronchial asthma were consecutively included. Symptom control was assessed according to Global Initiative for Asthma (GINA) assessment tool. Prevalence of asthma symptom control was determined while association of risk factors with uncontrolled asthma was also evaluated. Results: Of 113 patients the mean age was 36.8 +-16.6 years. There were 76 (67.3%) patients had uncontrolled asthma. A significant association of uncontrolled asthma was observed with incorrect inhaler technique (p value of 0.039) more than 10 years asthma duration (p value

Introduction Pulmonary tuberculosis (PTB) is caused by species of organisms in the Mycobacterium tuberculosis complex. It is a major public health problem worldwide and is endemic in Pakistan. Various clinical and biochemical markers exist for its diagnosis. Radiology has an important role in the diagnosis of PTB. Initially, a chest radiograph is warranted for PTB evaluation. High-resolution computed tomography (HRCT) also has high sensitivity and specificity for PTB diagnosis. Features of primary TB include consolidation, lymphadenopathy, pleural effusion and miliary nodules whereas post-primary TB include apical consolidation, nodules and cavitation. The aim of this study was to determine the diagnostic accuracy of HRCT chest in diagnosing sputum smear positive and smear negative PTB. Methods A cross-sectional study was conducted at a large tertiary care teaching hospital. A retrospective review of medical records of patients who underwent HRCT chest and sputum acid-fast bacillus (AFB) direct smear and AFB culture for suspicion of PTB was undertaken. All HRCT chest examinations were performed on multislice computed tomography (CT) scanner. On HRCT, PTB was defined as the presence of consolidation, centrilobular nodules, branching nodules with tree in bud appearance with or without lymphadenopathy and pleural effusion. Diagnostic accuracy of HRCT including sensitivity, specificity, positive and negative predictive values was calculated using 2 x 2 table, taking findings of AFB culture as a gold standard. Results A total of 108 patients were included in this study with a mean age of 51.85 ± 16.86 years. Diagnostic accuracy of HRCT in diagnosing PTB was found to be 84.26% with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 89.09%, 79.25%, 81.67%, and 87.50%, respectively. In sputum smear positive patients, HRCT has diagnostic accuracy, sensitivity, specificity, PPV and NPV of 87.50%, 88.57%, 84.62%, 93.94%, and 73.33%, respectively. In sputum smear negative patients, HRCT has diagnostic accuracy, sensitivity, specificity, PPV and NPV of 81.67%, 90.00%, 77.50%, 66.67%, and 93.94%, respectively. Conclusion HRCT has high sensitivity in diagnosing sputum smear positive and sputum smear negative PTB. The specificity of HRCT in diagnosing sputum smear positive PTB was high, whereas it was slightly low in diagnosing sputum smear negative PTB. Overall diagnostic accuracy of HRCT was high in diagnosing PTB.

Covert communication is an extension of the traditional encryption technology, which hides information through carriers such as text and images. Traditional covert communication relies on a third-party central node, which is easy to be detected and attacked. As a new type of decentralized technology, blockchain has been widely developed in recent years. Its anti-interference, anti-tampering and other characteristics can effectively alleviate the problems faced by traditional covert communications. Combining with blockchain technology to design an effective and feasible hidden communication model and method becomes the focus of research. To this end, this paper designs a new covert communication model suitable for Ethereum based on the whisper protocol. First, we introduce the covert communication model under the blockchain, as well as the whisper protocol in Ethereum. Then, based on the characteristics and structure of the whisper protocol, a new type of covert communication method was designed. Finally, the anti-jamming, anti-tampering and anti-detection of the model are proved, and its information embedding and transmission efficiency are also evaluated by comparing with the time-based covert communication.

This work offers an approach to aerial image classification for use in remote sensing object recognition, image processing and computer vision. Sparse coding (SC) is used to classify unmanned-aerial-vehicle (UAV) and satellite images because SC representation can generalize a large dataset and improve the detection of distinctive features by reducing calculation time for feature matching and classification. Features from images are extracted based on the following descriptors: (a) Scale Invariant Feature Transform; (b) Histogram of Oriented Gradients; and (c) Local Binary Patterns. SC representation and local image features are combined to represent global features for classification. Features are deployed in a sparse model to store descriptor features using extant dictionaries such as (a) the Discrete Cosine Transform and (b) the Discrete Wavelet Transform. An additional two dictionaries are proposed as developed for the present work: (c) the Discrete Ridgelet Transform (DRT) and (d) the Discrete Tchebichef Transform. The DRT dictionary is constructed by using the Ricker wavelet function to generate finite Ridgelet transforms as basis elements for a hybrid dictionary. Different pooling methods have also been employed to convert sparse-coded features into a feature matrix. Various machine learning algorithms are then applied to the feature matrix to classify objects contained in UAV and satellite imagery data. Experimental results show that the SC model secured better accuracy rates for extracted discriminative features contained in remote sensing images. The authors concluded that the proposed SC technique and proposed dictionaries provided feasible solutions for image classification and object recognition.

Functional neuroimaging modalities play an important role in deciding the diagnosis and course of treatment of neuronal dysfunction and degeneration. This article presents an analytical tool with visualization by exploiting the strengths of the MEG (magnetoencephalographic) neuroimaging technique. The tool automates MEG data import (in tSSS format), channel information extraction, time/frequency decomposition, and circular graph visualization (connectogram) for simple result inspection. For advanced users, the tool also provides magnitude squared coherence (MSC) values allowing personalized threshold levels, and the computation of default model from MEG data of control population. Default model obtained from healthy population data serves as a useful benchmark to diagnose and monitor neuronal recovery during treatment. The proposed tool further provides optional labels with international 10-10 system nomenclature in order to facilitate comparison studies with EEG (electroencephalography) sensor space. Potential applications in epilepsy and traumatic brain injury studies are also discussed.

Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery HRRS. Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network CNN approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle UAV and satellite imagery. The article has two major sections: the first describes the extraction of dense multiscale features multiple scales from the last convolutional layer of a pre-trained CNN models; the second describes the encoding of extracted features into global image features via sparse coding to achieve scene classification. The authors compared experimental outcomes with existing techniques such as Scale-Invariant Feature Transform and demonstrated that features from pre-trained CNNs generalized well with HRRS datasets and were more expressive than low-and mid-level features, exhibiting an overall 90.3% accuracy rate for scene classification compared to 85.4% achieved by SIFT with sparse coding. Thus, the proposed CNN-based sparse coding approach obtained a robust performance that holds promising potential for future applications in satellite and UAV imaging.

Detection and quantification of functional deficits due to moderate traumatic brain injury (mTBI) is crucial for clinical decision-making and timely commencement of functional therapy. In this work, we explore magnetoencephalography (MEG) based functional connectivity features i.e. magnitude squared coherence (MSC) and phase lag index (PLI) to quantify synchronized brain activity patterns as a means to detect functional deficits. We propose a multi-instance one-class support vector machine (SVM) model generated from a healthy control population. Any dispersion from the decision boundary of the model would be identified as an anomaly instance of mTBI case (Glasgow Coma Scale, GCS score between 9 and 13). The decision boundary was optimized by considering the closest anomaly (GCS =13) from the negative class as a support vector. Validated against magnetic resonance imaging (MRI) data, the proposed model at high beta band yielded an accuracy of 94.19% and a sensitivity of 90.00%, when tested with our mTBI dataset. The results support the suggestion of multi-instance one-class SVM for the detection of mTBI.

Introduction Tuberculosis is a major health problem in Pakistan. The prevalence of pulmonary as well as extrapulmonary tuberculosis is quite high. Tuberculin skin test, radiological imaging, and sputum smear microscopy have limitations in the diagnosis of tuberculosis. Xpert MTB/RIF was recently approved for the diagnosis of pulmonary tuberculosis and has shown promising results. The aim of this study was to determine the diagnostic accuracy of Xpert MTB/RIF in sputum smear-negative pulmonary tuberculosis using acid-fast bacilli (AFB) culture as the gold standard. Materials and methods This cross-sectional study was conducted at Iqbal Yad Chest Clinic and Nazimabad Chest Clinic of Ojha Institute of Chest Diseases, Dow University of Health Sciences. Patients of either gender aged 18-65 years suspected to have pulmonary tuberculosis with at least two sputum samples negative for AFB underwent Xpert MTB/RIF testing. Early morning sputum samples were obtained and sent for AFB smear microscopy, Xpert testing and also for culture analysis. Results Mean age of the patients was 37.48 ±17.49 years. There were 84 (37.3%) females and 141 (62.7%) males. Positive findings on Xpert MTB/RIF were found in 147 (65.3%) patients whereas AFB culture showed positive findings in 174 (77.3%) patients. Sensitivity, specificity, positive predicted value, negative predicted value and overall diagnostic accuracy of Xpert MTB/RIF was found to be 84.48%, 100%, 100%, 65.38%, and 88%, respectively. Conclusion Xpert MTB/RIF has high sensitivity, specificity, and diagnostic accuracy in diagnosis of sputum smear-negative cases of pulmonary tuberculosis.

Traumatic brain injury (TBI) is a source of disability and morbidity worldwide. Mild cognitive impairment (MCI) and mild TBI cause functional connectivity interruption for a very limited time frame; however, the patient diagnosed with moderate to severe forms of TBI requires quick, hassle free and precise identification of functional deficits in order to provide timely care. Magnetoencephalography (MEG) is the neuroimaging modality that provides the required information, and is useful for non-contact recording of functional connectivity assessment of TBI subjects. Default mode network (DMN) has been studied and described using functional magnetic resonance imaging (fMRI). This paper proposes a method to develop a default model of biomagnetic activations, as sensed over cortical region using MEG scans. The model is used to classify and assess TBI subjects. The classification is performed by devising default coherence limits between all pairs of MEG sensors for positive (control) group, and the assessment of severity is carried out by using PU learning method (single class model), where P (positive) data is from control population is utilized to compute significant functional connectivity deficits.

Counting precisely the number of people in a crowd is one of the most attractive issues for video analytics application. In this paper, an integrated method using Histogram of Oriented Gradient (HOG) and Completed Local Binary Pattern (CLBP) is proposed to detect a head-shoulder region of people within image or video sequence. Head-shoulder region is used as features to detect people against the false positive and false negative issue. HOG and CLBP are used to extract the edge contour and texture features of head-shoulder region, respectively. The two features are fused together to generate a combined feature vector. Support Vector Machine (SVM) is used to execute classification of the fusion features to classify people from a mixture of objects. The results show that the detection rate of the proposed method HOG-CLBP, on Recall value and Accuracy, achieves better performance compared to the current method for dense crowd scenario.

Gazing at computer screen for long hours may cause eye strain. The strain goes away after the eyes have been relaxed and kept away from the screen for some time; however, prolonged exposure may cause dryness and irritability. The eye-strain is monitored by a laser speckle flowgraphy (LSFG) system in this study. The LSFG system measures and displays choroidal vascular maps providing relative blood flow readings. The results show consistently high erythrocyte movement after watching low definition and low quality video for long time. Moreover, it is also observed that the blood flow tends to stabilize itself after relaxing the eyes for a couple of hours.

Functional connectivity is becoming popular as a second opinion for neurosurgeons and specialists in order to decide on the need for surgical resection, or prescribing medication and appraise prognosis. Neuroimaging modalities such as fMRI, fNIRS, PET, and EEG provide functional connectivity estimation. MEG is the most recent trend in functional connectivity assessment research as it gives more accurate results. The magnetic signals are not disrupted by volume conduction, as in EEG. Besides a reasonable spatial resolution, it offers an extraordinary temporal resolution. However there is a need of a generalized model for default mode network connectivity using MEG. This paper presents a novel method for generating a generalized model and discusses significance of threshold levels in assessing synchronization of activity from various brain regions.

Water drinking test is used as stress test for glaucoma, as it produces changes in intra-ocular pressure. Laser speckle flowgraphy (LSFG) system is used to assess blood flow changes in four regions of the optic nerve head in this study. The LSFG system is a relatively new and simple blood flow measuring technique that displays optic nerve head vascular maps providing relative blood flow readings. The results show significant changes in blood flow of the four regions of the optic nerve head namely, superior, inferior, temporal and nasal after drinking water. These changes are important to further the current understanding of auto-regulation phenomenon after water drinking test in healthy subjects. This data can serve as an important baseline data for comparison with patients displaying glaucoma onset and progression.

Content based image retrieval (CBIR) is used to retrieve digital images from large databases. However, the problem of retrieving images on the basis of the contents remains largely unsolved. The proposed method of image retrieval is based on the information provided by histogram analysis of the intensity or grayscale values of images. Some additional properties are also calculated and used that are based on regional characteristics of various objects in the image. The need to retrieve the additional regional properties arises due to the fact that the standard histograms are insensitive to small changes in images. Many images of different types can have similar histograms, because, histograms provide only a coarse characterization of an image. This is the main disadvantage of using histograms. This research is based on the concept of Histogram Refinement (Pass and Zabih, IEEE Workshop Appl Comput Vision (1996), 96–102). Distributing the grayscale image intensities by splitting the pixels using their intensity values into several classes just like the histogram refinement method can provide an estimate of the object characteristics present in an image. After the calculation of clusters using a color refinement method, the inherent features of each of the clusters is calculated based on the regional properties of the clusters. These additional region based features expound some structural information of the image. Finally, all of these features are used for image retrieval. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 298–306, 2011; © 2011 Wiley Periodicals, Inc.

Vegetation encroachment and its monitoring near high voltage power line is a challenging problem for electricity distribution companies. Electric supply companies monitor the vegetation/trees near power lines to avoid the blackouts that may occur in case of an improper monitoring of vegetation. The uninterrupted electric power supply is vital for industries, businesses, and daily life. Many approaches are employed to monitor vegetation/trees near the transmission line poles, but these approaches are more time consuming and expensive. In this paper, we have proposed a novel approach to monitor the vegetation near or under the power poles using satellite stereo images which were acquired using QuickBird and IKONOS satellite images and calculated disparity map using stereo matching algorithms i.e. dynamic programming (DP) and Hierarchical dynamic programming (HDP). The 3D depth of vegetation is based on disparity map which has been measured using stereo algorithms. Results showed that HDP performs well as compared to DP based on QuickBird and IKONOS satellite stereo images in terms of accuracy.

Magnetoencephalography (MEG) is used for functional connectivity analysis, and can record brain signals from deep sources non-invasively. Modern MEG systems measure signals at a temporal resolution of milliseconds and at millimeter precision. However, there is a lack of standardization in the position and orientation of sensors, unlike the electroencephalography (EEG) that follows sensor positioning guidelines defined by international 10–20 10-10 or 10-5 systems. Mapping MEG sensor positioning to EEG's is essential to enable data fusion and comparison of both modalities. This paper reports the development of a novel framework for MEG data visualization and analysis. The strength of the proposed framework is demonstrated through input of sizeable data from multiple healthy subjects and generating default mode connectivity visualization from the most common and significantly active coherent brain regions.

Large scale systems are focused at providing rapid and secure connections between system entities and users. The basic purpose of development of such systems is increasing integrity between members, staff and other departments. As different system users access the system on different levels of access according to their defined roles in organizations and institutes, a mechanism for controlling and monitoring access levels of these users becomes a vital element of such systems as features such as these, are vital to concerned organizations which need to evaluate their risk exposure ensuing from IT system access provided to employees. It is required that a proper architecture for such genre of systems is defined so that a fast, reliable, efficient and secure system environment can be provided. Role-based access control (RBAC) presents a popular model for I.T security and access control that aids in reducing the complexity of safety measures and their administration. Plenty of work has already been done in RBAC for enhancing and simplifying security and reliability. This paper aims in defining the architecture using RBAC and attributes for organizations with emphasis on segregation of duties, load sharing of administrative tasks and controlling IT system access

This work presents a multimodal biometrics system that combines face and fingerprint authentication modules. The proposed face verification module incorporates Gabor Wavelet texture features and face edge features. As for the fingerprint module, a simple algorithm is employed for extracting fingers' Minutia in order to build feature vector for each sample fingerprint. The proposed system can be used effectively for personal identification at international airports checkpoints.

In this paper, we propose an adaptive background modeling algorithm for crowd surveillance system. We employed Approximate Median Method (AMM) along with the Phase congruency edge detector to develop the background model. The resulting foreground of the proposed model was obtained by applying a logical AND operation between binary maps of the (foreground) of the AMM image and the gradient information of the (Phase congruency edge detector) PC. Experimental results demonstrate that the proposed method is highly accurate while providing a processing speed of 24.8 fps allowing its implementation for real time application.

CBIR makes a wide use of histogram based methods for image indexing. Histograms describe the global intensity distribution of images. They are very easy to compute and are insensitive to small changes in object translations and rotations. However, they are not robust to large appearance changes, and they might give similar results for different kinds of images if the distributions of colors are same in the images. Our research focuses mainly on the image bins (histogram value divisions by frequency) separation technique followed by calculating the sum of values, and using them as image local features. At first, the histogram is first calculated for an image. After that, it is subdivided into sixteen equal bins and the sum of local values is calculated and stored. We have tested the proposed algorithm on a large database of images.

This paper caters the need of acquiring the principal objects, characters, and scenes from a video in order to entertain the image based query. The movie frames are divided into frames with 2D representative images called "key frames". Various regions in a key frame are marked as key objects according to their textures and shapes. These key objects serve as a catalogue of regions to be searched and matched from rest of the movie, using viewpoint invariant regions calculation, providing the location, size, and orientation of all the objects occurring in the movie in the form of a set of structures collaborating as video profile. The profile provides information about occurrences of every single key object from every frame of the movie it exists in. This information can further ease streaming of objects over various network-based viewing qualities. Hence, the method provides an effective reduced profiling approach of automatic logging and viewing information through query by example (QBE) procedure, and deals with video streaming issues at the same time.

Color histogram is widely used for image indexing in content-based image retrieval (CBIR). A color histogram describes the global color distribution of an image. It is very easy to compute and is insensitive to small changes in viewing positions. However, the histogram is not robust to large appearance changes. Moreover, the histogram might give similar results for different kinds of images if the distributions of colors are same in the images. On the other hand, color correlogram is efficiently used for image indexing in content-based image retrieval. Color correlogram extracts not only the color distribution of pixels in images like color histogram, but also extracts the spatial information of pixels in the images. The characteristic of the color Correlogram to take into account the spatial information as well as the distribution of color pixels greatly attracts the researcher for content based image retrieval. In this paper, we propose the image bin (histogram value divisions) separation technique followed by extracting maxima of frequencies and plotting a correlogram. At first, the histogram is first calculated for an image. After that, it is subdivided into four equal bins. Each bin is subdivided into four more bins and for every such subdivision the maxima of frequencies s calculated. This information is stored in the form of a correlogram. The distance between correlogram of the query image with the corresponding correlogram of database images is calculated. The proposed algorithm is tested on a database comprising a large number of images.

Shape from focus (SFF) uses focus measure operator for depth measurement from a sequence of images. From the analysis of defocused image, it is observed that the focus measure operator should respond to high frequency variations of image intensity and produce maximum values when the image is perfectly focused. Therefore, an effective focus measure operator must be a high-pass filter. Laplacian is mostly used as focus measure operator in the previous SFF methods. In this paper, generalized Laplacian is used as focus measure operator for better 3D shape recovery of objects.

Content-based image analyses and retrieval systems, most of the time, deal with histogram refinement techniques. The coarse characterization of histograms is researched and a very brief list of features is proposed in this paper, which is recommended for RGB images carrying some structure or colored information with similar appearance. The R, G, and B contents are split and their color and edge features are considered. These features include the count of horizontal, vertical and slanting edges, the number of clusters, and the percentage of noticeable colored channel by area. Iteration is committed onto the limited set of results generated by this procedure, which further refines the results based on the overall appearance of these selective images. This reduces the order of complexity, and simultaneously, enhances searching ability by reducing number of computations and comparison time.

This paper presents an image retrieval system using HSV color indexes. We classify the image into a fixed number of blocks, extract the key value of each block and assign the index code, which is classified by 24, to the HSV color space. The index code of each image is stored in the database. The desired image is retrieved on the web. Retrieval system outputs the image with a high matching factor according to a distribution chart. A small demonstration system has been tested and shows superior performance compared with the simple color based retrieval system.