Search results for: Recognition based graphical user authentication

Authors: András Barta, István Vajk

Abstract:

In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.

Keywords: Object recognition, Bayesian network, Wavelets, Document processing.

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Authors: Madiha Khan, Sidrah Ramzan, Seemab Khan, Shahzad Hassan, Kamran Saeed

Abstract:

Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information.

Keywords: GraphViz representation, semantic relatedness, similarity measurement, WordNet similarity.

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Authors: M. Hamad Hassan, S.A.M. Gilani

Abstract:

In this paper, a novel scheme is proposed for Ownership Identification and Color Image Authentication by deploying Cryptography & Digital Watermarking. The color image is first transformed from RGB to YST color space exclusively designed for watermarking. Followed by color space transformation, each channel is divided into 4×4 non-overlapping blocks with selection of central 2×2 sub-blocks. Depending upon the channel selected two to three LSBs of each central 2×2 sub-block are set to zero to hold the ownership, authentication and recovery information. The size & position of sub-block is important for correct localization, enhanced security & fast computation. As YS ÔèÑ T so it is suitable to embed the recovery information apart from the ownership and authentication information, therefore 4×4 block of T channel along with ownership information is then deployed by SHA160 to compute the content based hash that is unique and invulnerable to birthday attack or hash collision instead of using MD5 that may raise the condition i.e. H(m)=H(m'). For recovery, intensity mean of 4x4 block of each channel is computed and encoded upto eight bits. For watermark embedding, key based mapping of blocks is performed using 2DTorus Automorphism. Our scheme is oblivious, generates highly imperceptible images with correct localization of tampering within reasonable time and has the ability to recover the original work with probability of near one.

Keywords: Hash Collision, LSB, MD5, PSNR, SHA160

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Authors: J. Umamaheswari, G. Radhamani

Abstract:

This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.

Keywords: Recognition, classification, Relaxed Median Filter, Adaptive thresholding, clustering and Neural Networks

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Authors: Maha Shamseddine, Amjad Nusayr, Wassim Itani

Abstract:

In this paper, we present a Platform-as-a-Service (PaaS) model for controlling the privacy enforcement mechanisms applied on user data when stored and processed in Cloud data centers. The proposed architecture consists of establishing user configurable ‘sticky’ policies on the Graphical User Interface (GUI) data-bound components during the application development phase to specify the details of privacy enforcement on the contents of these components. Various privacy classification classes on the data components are formally defined to give the user full control on the degree and scope of privacy enforcement including the type of execution containers to process the data in the Cloud. This not only enhances the privacy-awareness of the developed Cloud services, but also results in major savings in performance and energy efficiency due to the fact that the privacy mechanisms are solely applied on sensitive data units and not on all the user content. The proposed design is implemented in a real PaaS cloud computing environment on the Microsoft Azure platform.

Keywords: Privacy enforcement, Platform-as-a-Service privacy awareness, cloud computing privacy.

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Authors: Anjin Park, Sungju Yun, Jungwhan Kim, Seungk Min, Keechul Jung

Abstract:

Finger spelling is an art of communicating by signs made with fingers, and has been introduced into sign language to serve as a bridge between the sign language and the verbal language. Previous approaches to finger spelling recognition are classified into two categories: glove-based and vision-based approaches. The glove-based approach is simpler and more accurate recognizing work of hand posture than vision-based, yet the interfaces require the user to wear a cumbersome and carry a load of cables that connected the device to a computer. In contrast, the vision-based approaches provide an attractive alternative to the cumbersome interface, and promise more natural and unobtrusive human-computer interaction. The vision-based approaches generally consist of two steps: hand extraction and recognition, and two steps are processed independently. This paper proposes real-time vision-based Korean finger spelling recognition system by integrating hand extraction into recognition. First, we tentatively detect a hand region using CAMShift algorithm. Then fill factor and aspect ratio estimated by width and height estimated by CAMShift are used to choose candidate from database, which can reduce the number of matching in recognition step. To recognize the finger spelling, we use DTW(dynamic time warping) based on modified chain codes, to be robust to scale and orientation variations. In this procedure, since accurate hand regions, without holes and noises, should be extracted to improve the precision, we use graph cuts algorithm that globally minimize the energy function elegantly expressed by Markov random fields (MRFs). In the experiments, the computational times are less than 130ms, and the times are not related to the number of templates of finger spellings in database, as candidate templates are selected in extraction step.

Keywords: CAMShift, DTW, Graph Cuts, MRF.

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Authors: C. Kalamani, S. Sowmiya, S. Dheivambigai, G. Harihara Sudhan

Abstract:

Radio Frequency Identification (RFID) is a blooming technology which uses radio frequency to track the objects. This technology transmits signals between tag and reader to fetch information from the tag with a unique serial identity. Generally, the drawbacks of RFID technology are high cost, high consumption of power and weak authentication systems between a reader and a tag. The proposed protocol utilizes less dynamic power using reversible truncated multipliers which are implemented in RFID tag-reader with mutual authentication protocol system to reduce both leakage and dynamic power consumption. The proposed system was simulated using Xilinx and Cadence tools.

Keywords: Mutual authentication, protocol, reversible gates, RFID.

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Authors: András Barta, István Vajk

Abstract:

In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.

Keywords: Object recognition, Bayesian network, Wavelets, Document processing.

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Authors: Sherif Abdelazeem

Abstract:

A comparison between the performance of Latin and Arabic handwritten digits recognition problems is presented. The performance of ten different classifiers is tested on two similar Arabic and Latin handwritten digits databases. The analysis shows that Arabic handwritten digits recognition problem is easier than that of Latin digits. This is because the interclass difference in case of Latin digits is smaller than in Arabic digits and variances in writing Latin digits are larger. Consequently, weaker yet fast classifiers are expected to play more prominent role in Arabic handwritten digits recognition.

Keywords: Handwritten recognition, Arabic recognition, Digits recognition, Document recognition

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Authors: Pavan S., Sridhar G., Sridhar V.

Abstract:

Super resolution is one of the commonly referred inference problems in computer vision. In the case of images, this problem is generally addressed using a graphical model framework wherein each node represents a portion of the image and the edges between the nodes represent the statistical dependencies. However, the large dimensionality of images along with the large number of possible states for a node makes the inference problem computationally intractable. In this paper, we propose a representation wherein each node can be represented as acombination of multiple regression functions. The proposed approach achieves a tradeoff between the computational complexity and inference accuracy by varying the number of regression functions for a node.

Keywords: Belief propagation, Graphical model, Regression, Super resolution.

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Authors: Emma K. Sales

Abstract:

Durian is the flagship fruit of Mindanao and there is an abundance of several cultivars with many confusing identities/ names. The project was conducted to develop procedure for reliable and rapid detection and sorting of durian planting materials. Moreover, it is also aimed to establish specific genetic or DNA markers for routine testing and authentication of durian cultivars in question. The project developed molecular procedures for routine testing. SSR primers were also screened and identified for their utility in discriminating durian cultivars collected. Results of the study showed the following accomplishments: 1. Twenty (29) SSR primers were selected and identified based on their ability to discriminate durian cultivars, 2. Optimized and established standard procedure for identification and authentication of Durian cultivars 3. Genetic profile of durian is now available at Biotech Unit Our results demonstrate the relevance of using molecular techniques in evaluating and identifying durian clones. The most polymorphic primers tested in this study could be useful tools for detecting variation even at the early stage of the plant especially for commercial purposes. The process developed combines the efficiency of the microsatellites development process with the optimization of non-radioactive detection process resulting in a user-friendly protocol that can be performed in two (2) weeks and easily incorporated into laboratories about to start microsatellite development projects. This can be of great importance to extend microsatellite analyses to other crop species where minimal genetic information is currently available. With this, the University can now be a service laboratory for routine testing and authentication of durian clones.

Keywords: DNA, SSR Analysis, genotype, genetic diversity, cultivars.

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Authors: Moulay A. Akhloufi, Abdelhakim Bendada

Abstract:

Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear techniques like PCA and LDA. In this work, we introduce a probabilistic Bayesian framework for face recognition in the infrared spectrum. In the infrared spectrum, variations can occur between face images of the same individual due to pose, metabolic, time changes, etc. Bayesian approaches permit to reduce intrapersonal variation, thus making them very interesting for infrared face recognition. This framework is compared with classical linear techniques. Non linear techniques we developed recently for infrared face recognition are also presented and compared to the Bayesian face recognition framework. A new approach for infrared face extraction based on SVM is introduced. Experimental results show that the Bayesian technique is promising and lead to interesting results in the infrared spectrum when a sufficient number of face images is used in an intrapersonal learning process.

Keywords: Face recognition, biometrics, probabilistic imageprocessing, infrared imaging.

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Authors: M. Hamad Hassan, S.A.M. Gilani

Abstract:

In this paper, a semi-fragile watermarking scheme is proposed for color image authentication. In this particular scheme, the color image is first transformed from RGB to YST color space, suitable for watermarking the color media. Each channel is divided into 4×4 non-overlapping blocks and its each 2×2 sub-block is selected. The embedding space is created by setting the two LSBs of selected sub-block to zero, which will hold the authentication and recovery information. For verification of work authentication and parity bits denoted by 'a' & 'p' are computed for each 2×2 subblock. For recovery, intensity mean of each 2×2 sub-block is computed and encoded upto six to eight bits depending upon the channel selection. The size of sub-block is important for correct localization and fast computation. For watermark distribution 2DTorus Automorphism is implemented using a private key to have a secure mapping of blocks. The perceptibility of watermarked image is quite reasonable both subjectively and objectively. Our scheme is oblivious, correctly localizes the tampering and able to recovery the original work with probability of near one.

Keywords: Image Authentication, YST Color Space, Intensity Mean, LSBs, PSNR.

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Authors: S. Vural, H. Yamauchi

Abstract:

Current systems for face recognition techniques often use either SVM or Adaboost techniques for face detection part and use PCA for face recognition part. In this paper, we offer a novel method for not only a powerful face detection system based on Six-segment-filters (SSR) and Adaboost learning algorithms but also for a face recognition system. A new exclusive face detection algorithm has been developed and connected with the recognition algorithm. As a result of it, we obtained an overall high-system performance compared with current systems. The proposed algorithm was tested on CMU, FERET, UNIBE, MIT face databases and significant performance has obtained.

Keywords: Adaboost, Face Detection, Face recognition, SVM, Gabor filters, PCA-ICA.

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Authors: Valentin P. Velikov

Abstract:

Automatic program generation saves time, human resources, and allows receiving syntactically clear and logically correct modules. The 4-th generation programming languages are related to drawing the data and the processes of the subject area, as well as, to obtain a frame of the respective information system. The application can be separated in interface and business logic. That means, for an interactive generation of the needed system to be used an already existing toolkit or to be created a new one.

Keywords: Computer science, graphical user interface, user dialog interface, dialog frames, data modeling, subject area modeling.

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Authors: P. Phokharatkul, K. Sankhuangaw, S. Somkuarnpanit, S. Phaiboon, C. Kimpan

Abstract:

Much research into handwritten Thai character recognition have been proposed, such as comparing heads of characters, Fuzzy logic and structure trees, etc. This paper presents a system of handwritten Thai character recognition, which is based on the Ant-minor algorithm (data mining based on Ant colony optimization). Zoning is initially used to determine each character. Then three distinct features (also called attributes) of each character in each zone are extracted. The attributes are Head zone, End point, and Feature code. All attributes are used for construct the classification rules by an Ant-miner algorithm in order to classify 112 Thai characters. For this experiment, the Ant-miner algorithm is adapted, with a small change to increase the recognition rate. The result of this experiment is a 97% recognition rate of the training set (11200 characters) and 82.7% recognition rate of unseen data test (22400 characters).

Keywords: Hand written, Thai character recognition, Ant-mineralgorithm, distinct feature.

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Authors: К. R. Aida–Zade, C. Ardil, S. S. Rustamov

Abstract:

Statement of the automatic speech recognition problem, the assignment of speech recognition and the application fields are shown in the paper. At the same time as Azerbaijan speech, the establishment principles of speech recognition system and the problems arising in the system are investigated. The computing algorithms of speech features, being the main part of speech recognition system, are analyzed. From this point of view, the determination algorithms of Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) coefficients expressing the basic speech features are developed. Combined use of cepstrals of MFCC and LPC in speech recognition system is suggested to improve the reliability of speech recognition system. To this end, the recognition system is divided into MFCC and LPC-based recognition subsystems. The training and recognition processes are realized in both subsystems separately, and recognition system gets the decision being the same results of each subsystems. This results in decrease of error rate during recognition. The training and recognition processes are realized by artificial neural networks in the automatic speech recognition system. The neural networks are trained by the conjugate gradient method. In the paper the problems observed by the number of speech features at training the neural networks of MFCC and LPC-based speech recognition subsystems are investigated. The variety of results of neural networks trained from different initial points in training process is analyzed. Methodology of combined use of neural networks trained from different initial points in speech recognition system is suggested to improve the reliability of recognition system and increase the recognition quality, and obtained practical results are shown.

Keywords: Speech recognition, cepstral analysis, Voice activation detection algorithm, Mel Frequency Cepstral Coefficients, features of speech, Cepstral Mean Subtraction, neural networks, Linear Predictive Coding.

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Authors: S. Ali, R. Yunus, A. Arif, Y. Ayaz, M. Baber Sial, R. Asif, N. Naseer, M. Jawad Khan

Abstract:

In this study, we have proposed a gesture to emotion recognition method using flex sensors mounted on metacarpophalangeal joints. The flex sensors are fixed in a wearable glove. The data from the glove are sent to PC using Wi-Fi. Four gestures: finger pointing, thumbs up, fist open and fist close are performed by five subjects. Each gesture is categorized into sad, happy, and excited class based on the velocity and acceleration of the hand gesture. Seventeen inspectors observed the emotions and hand gestures of the five subjects. The emotional state based on the investigators assessment and acquired movement speed data is compared. Overall, we achieved 77% accurate results. Therefore, the proposed design can be used for emotional state detection applications.

Keywords: Emotion identification, emotion models, gesture recognition, user perception.

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Authors: K.Amrithavarshini, S.Chandrachudeswaran

Abstract:

This paper presents an idea to improve the efficiency of security checks in airports through the active tracking and monitoring of passengers and staff using OFDM modulation technique and Finger print authentication. The details of the passenger are multiplexed using OFDM .To authenticate the passenger, the fingerprint along with important identification information is collected. The details of the passenger can be transmitted after necessary modulation, and received using various transceivers placed within the premises of the airport, and checked at the appropriate check points, thereby increasing the efficiency of checking. OFDM has been employed for spectral efficiency.

Keywords: Orthogonal Frequency Division Multiplexing, FFT Algorithm, Fingerprint Authentication, Airport Security

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Authors: Tomas Shejbal, Matej Petkov, Tomas Zalabsky, Jan Pidanic, Zdenek Nemec

Abstract:

The paper describes software for remote control and measuring with new Graphical User Interface for Rohde & Schwarz instruments. Software allows remote control through Ethernet and supports basic and advanced functions for control various type of instruments like network and spectrum analyzers, power meters, signal generators and oscilloscopes. Standard Commands for Programmable Instruments (SCPI) and Virtual Instrument Software Architecture (VISA) are used for remote control and setup of instruments. Developed software is modular with user friendly graphic user interface for each instrument with automatic identification of instruments.

Keywords: Remote control, Rohde&Schwarz, SCPI, VISA, MATLAB, spectum analyzer, network analyzer, oscilloscope, signal generator.

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Authors: Sanghoon Kim, Sun-Tae Chung, Souhwan Jung, Seongwon Cho

Abstract:

Robust face recognition under various illumination environments is very difficult and needs to be accomplished for successful commercialization. In this paper, we propose an improved illumination normalization method for face recognition. Illumination normalization algorithm based on anisotropic smoothing is well known to be effective among illumination normalization methods but deteriorates the intensity contrast of the original image, and incurs less sharp edges. The proposed method in this paper improves the previous anisotropic smoothing-based illumination normalization method so that it increases the intensity contrast and enhances the edges while diminishing the effect of illumination variations. Due to the result of these improvements, face images preprocessed by the proposed illumination normalization method becomes to have more distinctive feature vectors (Gabor feature vectors) for face recognition. Through experiments of face recognition based on Gabor feature vector similarity, the effectiveness of the proposed illumination normalization method is verified.

Keywords: Illumination Normalization, Face Recognition, Anisotropic smoothing, Gabor feature vector.

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Authors: Ramin Halavati, Mansour Jamzad, Mahdieh Soleymani

Abstract:

Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in cases that the words where chosen from a prespecified list.

Keywords: Image Processing, Pattern Recognition.

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Authors: Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

Abstract:

In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.

Keywords: Face recognition, Binary vector quantization (BVQ), Local Binary Patterns (LBP), DCT coefficients.

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Authors: Shin-Jer Yang, Kai-Tai Yang

Abstract:

In response to a changing world and the fast growth of the Internet, more and more enterprises are replacing web-based services with cloud-based ones. Multi-tenancy technology is becoming more important especially with Software as a Service (SaaS). This in turn leads to a greater focus on the application of Identity and Access Management (IAM). Conventional Near-Field Communication (NFC) based verification relies on a computer browser and a card reader to access an NFC tag. This type of verification does not support mobile device login and user-based access management functions. This study designs an NFC-based third-party cloud identity and access management scheme (NFC-IAM) addressing this shortcoming. Data from simulation tests analyzed with Key Performance Indicators (KPIs) suggest that the NFC-IAM not only takes less time in identity identification but also cuts time by 80% in terms of two-factor authentication and improves verification accuracy to 99.9% or better. In functional performance analyses, NFC-IAM performed better in salability and portability. The NFC-IAM App (Application Software) and back-end system to be developed and deployed in mobile device are to support IAM features and also offers users a more user-friendly experience and stronger security protection. In the future, our NFC-IAM can be employed to different environments including identification for mobile payment systems, permission management for remote equipment monitoring, among other applications.

Keywords: Cloud service, multi-tenancy, NFC, IAM, mobile device.

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Authors: Vladimir A. Fursov, Nikita E. Kozin

Abstract:

In this work the opportunity of construction of the qualifiers for face-recognition systems based on conjugation criteria is investigated. The linkage between the bipartite conjugation, the conjugation with a subspace and the conjugation with the null-space is shown. The unified solving rule is investigated. It makes the decision on the rating of face to a class considering the linkage between conjugation values. The described recognition method can be successfully applied to the distributed systems of video control and video observation.

Keywords: Conjugation, Eigenfaces, Recognition.

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Authors: V. Kabeer, N.K.Narayanan

Abstract:

This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.

Keywords: Face recognition, Image analysis, Wavelet feature extraction, Pattern recognition, Classifier algorithms

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Authors: G. Schirripa Spagnolo, M. De Santis

Abstract:

The protection of the contents of digital products is referred to as content authentication. In some applications, to be able to authenticate a digital product could be extremely essential. For example, if a digital product is used as a piece of evidence in the court, its integrity could mean life or death of the accused. Generally, the problem of content authentication can be solved using semifragile digital watermarking techniques. Recently many authors have proposed Computer Generated Hologram Watermarking (CGHWatermarking) techniques. Starting from these studies, in this paper a semi-fragile Computer Generated Hologram coding technique is proposed, which is able to detect malicious tampering while tolerating some incidental distortions. The proposed technique uses as watermark an encrypted image, and it is well suitable for digital image authentication.

Keywords: Asymmetric cryptography, Semi-Fragile watermarking, Image authentication, Hologram watermark, Public- Key Cryptography, RSA.

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Authors: Sanghoon Kim, Sun-Tae Chung, Souhwan Jung, Seoungseon Jeon, Jaemin Kim, Seongwon Cho

Abstract:

In this paper, we propose a face recognition algorithm using AAM and Gabor features. Gabor feature vectors which are well known to be robust with respect to small variations of shape, scaling, rotation, distortion, illumination and poses in images are popularly employed for feature vectors for many object detection and recognition algorithms. EBGM, which is prominent among face recognition algorithms employing Gabor feature vectors, requires localization of facial feature points where Gabor feature vectors are extracted. However, localization method employed in EBGM is based on Gabor jet similarity and is sensitive to initial values. Wrong localization of facial feature points affects face recognition rate. AAM is known to be successfully applied to localization of facial feature points. In this paper, we devise a facial feature point localization method which first roughly estimate facial feature points using AAM and refine facial feature points using Gabor jet similarity-based facial feature localization method with initial points set by the rough facial feature points obtained from AAM, and propose a face recognition algorithm using the devised localization method for facial feature localization and Gabor feature vectors. It is observed through experiments that such a cascaded localization method based on both AAM and Gabor jet similarity is more robust than the localization method based on only Gabor jet similarity. Also, it is shown that the proposed face recognition algorithm using this devised localization method and Gabor feature vectors performs better than the conventional face recognition algorithm using Gabor jet similarity-based localization method and Gabor feature vectors like EBGM.

Keywords: Face Recognition, AAM, Gabor features, EBGM.

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Authors: Vlado Kitanovski, Dimitar Taskovski, Sofija Bogdanova

Abstract:

In this paper we present a combined hashing/watermarking method for image authentication. A robust image hash, invariant to legitimate modifications, but fragile to illegitimate modifications is generated from the local image characteristics. To increase security of the system the watermark is generated using the image hash as a key. Quantized Index Modulation of DCT coefficients is used for watermark embedding. Watermark detection is performed without use of the original image. Experimental results demonstrate the effectiveness of the presented method in terms of robustness and fragility.

Keywords: authentication, blind watermarking, image hash, semi-fragile watermarking

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Authors: Delowar Hossain, Genci Capi

Abstract:

This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping.

Keywords: Deep learning, genetic algorithm, object recognition, robot grasping.

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