Abstract: Pattern mining is one of the most important tasks to extract meaningful and useful information from raw data. This task aims to extract item-sets that represent any type of homogeneity and regularity in data. Although many efficient algorithms have been developed in this regard, the growing interest in data has caused the performance of existing pattern mining techniques to be dropped. The goal of this paper is to propose new efficient pattern mining algorithms to work in big data. To this aim, a series of algorithms based on the MapReduce framework and the Hadoop open-source implementation have been proposed. The proposed algorithms can be divided into three main groups. First, two algorithms [Apriori MapReduce (AprioriMR) and iterative AprioriMR] with no pruning strategy are proposed, which extract any existing itemset in data. Second, two algorithms (space pruning AprioriMR and top AprioriMR) that prune the search space by means of the well-known anti-monotone property are proposed.

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Abstract: Traditional password conversion scheme for user authentication is to transform the passwords into hash values. These hash-based password schemes are comparatively simple and fast because those are based on text and famed cryptography. However, those can be exposed to cyber-attacks utilizing password by cracking tool or hash-cracking online sites. Attackers can thoroughly figure out an original password from hash value when that is relatively simple and plain. As a result, many hacking accidents have been happened predominantly in systems adopting those hash-based schemes. In this work, we suggest enhanced password processing scheme based on image using visual cryptography (VC). Different from the traditional scheme based on hash and text, our scheme transforms a user ID of text type to two images encrypted by VC. The user should make two images consisted of subpixels by random function with SEED which includes personal information. The server only has user’s ID and one of the images

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Abstract: Estimation of nitrogen content and weed control is essential and critical operation and can affect crop yield. Fertilizers and weedicides play an important role in maintaining nitrogen and weed control but their role is under criticism due to perceived excessive use and they are potentially harmful to the environment. Autonomous estimation of nitrogen content and weed control concepts have recently being extensively researched due to the advantages that they possess. In this proposed work, we systematically choose methods to be used for the estimation of nitrogen and classification of weeds.

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Abstract: Unconstrained face recognition is still an open problem as state-of-the-art algorithms have not yet reached high recognition performance in real-world environments. This paper addresses this problem by proposing a new approach called Sparse Fingerprint Classification Algorithm (SFCA). In the training phase, for each enrolled subject, a grid of patches is extracted from each subject’s face images in order to construct representative dictionaries. In the testing phase, a grid is extracted from the query image and every patch is transformed into a binary sparse representation using the dictionary, creating a fingerprint of the face. The binary coefficients vote for their corresponding classes and the maximum-vote class decides the identity of the query image. Experiments were carried out on seven widely-used face databases. The results demonstrate that when the size of the dataset is small or medium (e.g., the number of subjects is not greater than one hundred), SFCA is able to deal wit

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Abstract: In this paper, we analyse first-person-view videos to develop a personalized user authentication mechanism. Our proposed algorithm generates provisional image-based passwords which benefit a variety of purposes such as unlocking a mobile device or fallback authentication. First, representative frames are extracted from the egocentric videos. Then, they are split into distinguishable segments before a clustering procedure is applied to discard repetitive scenes. The whole process aims to retain memorable images to form the authentication challenges. We integrate eye tracking data to select informative sequences of video frames and suggest another alternative method if an eyefacing camera is not available. To evaluate our system, we perform experiments in different settings including object- interaction activities and traveling contexts. Even though our mechanism produces variable graphical passwords, the log-in effort for the user is comparable with approaches based on static challenge

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Abstract: Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many machine learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal with conflicting concerns of the small sample size and high dimensionality in one sample face recognition is critical for its achievable recognition accuracy and feasibility in practice. Being different from conventional methods for global face recognition based on generalization ability promotion and local face recognition depending on image segmentation, a single sample face recognition algorithm based on Locality Preserving Projection (LPP) feature transfer is proposed here.

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Abstract: Textual passwords remain the most commonly employed user authentication mechanism, and potentially will continue to be so for years to come. Despite the well-known security and usability issues concerning textual passwords, none of the numerous proposed authentication alternatives appear to have achieved a sufficient level of adoption to dominate in the foreseeable future. Password hints, consisting of a user generated text saved at the account setup stage, are employed in several authentication systems to help users to recall forgotten passwords. However, users are often unable to create hints that jog the memory without revealing too much information regarding the passwords themselves. We propose a rethink of password hints by introducing S`YNTHIMA, a novel cued recall-based textual password method that reveals no information regarding the password, requires no modifications to authentication servers, and requires no additional setup or registration steps.

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Abstract: A person when he or she does not have a proper rest especially a driver tends to fall asleep causing a traffic accident. It is why the present work wants to realize a system that can detect the drowsiness of the driver, in order to reduce traffic accidents. For that system, it will take the processing of images through a camera which will focus on the driver. In that, it is going to analyze the changes that happen in the face and then will be processed through a program in order to detect drowsiness to send an alert to the driver.

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Abstract: In this paper, driver drowsiness detection algorithm based on the state of eyes of the driver which is determined by his iris visibility has been implemented. If eyes remain in one state either open or closed longer than expected time as well as if the driver is not looking straight front, it is an indication that driver is drowsy and then the system warns the driver. System is capable of detecting the state of eyes with or without the regular glasses. Matlab with image processing tools has been used to process the image provided by a camera. Matlab creates System Object using Viola Jones algorithm to detect the objects such as nose, mouth or upper body. After capturing an image, rectangular eyes area was adjusted to reduce the noise. RGB to Gray scale and finally to Binary image conversion is with a suitable threshold value. A median filter was used to reduce the noise and then the image was smoothened. The drowsiness detection is done based on the conditions like Black to White pixe

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Abstract: In this paper we presented an intangible hand gesture based computer mouse control system. Our approach uses a novel skin color segmentation technique to control mouse movement. The system uses morphological operations like structuring elements and blob counting. Our system can remove other skin like objects from the background. One of the key features of the system is its simplicity. We have successfully tested our system for an intangible interface between human hand and computer mouse without any complex processing. Various mouse operations like cursor movements, right click and left click have been performed. The system has been implemented using webcam and MATLAB.

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Abstract: This paper presents a Face Detection System with Expression Recognition using Artificial Neural Networks. It is an automated vision system designed and implemented using MATLAB. The Face Detection with Expression Recognition system accomplishes facial expression recognition through two phases. The captured image is processed first to detect the face, and then the facial expression is recognized. These two phases are completed in five stages. The first two stages of the system deal with detecting and cropping the face using image processing, in particular the Viola-Jones object detection framework. The third stage deals with converting the colors of the cropped image from RGB into gray scale and applying the appropriate smoothing filter. The fourth stage consists of feature extraction using Artificial Neural Networks, so as the extracted features are compared with training samples. The final stage classifies the given outputs and shows facial expression recognition results. It then dete

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Abstract: Face recognition is one of the latest technologies being studied area in biometric as it has wide area of applications. But Face detection is one of the challenging problems in Image processing. The basic aim of face detection is determine if there is any face in an image & then locate position of a face in an image. Evidently face detection is the first step towards creating an automated system which may involve other face processing. The neural network is created & trained with training set of faces & non-faces. All results are implemented in MATLAB 2013 environment.

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Abstract: It is an extremely bulky process to predict a disease based on the visual diagnosis of cell type with precision or accuracy, especially when multiple features are associated. If we get the information about the dead skin which is not visible by naked eyes well in time then we can easily prevent the further spreading of disease on the other part of body. One of the major problems coming in the medical field is that doctors are not able to detect that infected part which is not visible by naked eyes and therefore they only operate the visible infected part of the skin and this may cause a major problem like cancer or any dangerous disease in the future. Skin cancer classification system is developed and the relationship of the skin cancer image across different type of neural network is established. The collected medical images are feed into the system, and using different image processing schemes image properties are enhanced. Useful information can be extracted from these medical image

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Abstract: Human beings have the ability to express their emotions and feelings through the speech but some unfortunate ones do not have the ability. They cannot speak and listen. This paper presents a man machine interface using video camera. The system will use a single, color camera mounted above a neutral colored desk surface next to the computer. The output of the camera will be displayed on the monitor. Shape and position information about the hand will be gathered using detection of skin.

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Abstract: Sign language is widely used by individuals with hearing impairment to communicate with each other conveniently using hand gestures. However, non-sign-language speakers find it very difficult to communicate with those with speech or hearing impairment since it interpreters are not readily available at all times. Many countries have their own sign language, such as American Sign Language (ASL) which is mainly used in the United States and the English-speaking part of Canada. The proposed system helps non-sign-language speakers in recognizing gestures used in American Sign Language. The system described in this paper is implemented using MATLAB. In this approach, firstly, the signs are captured using a webcam. The images captured are then processed further and the features are extracted from the captured images using PCA. Comparison of the features is done using Euclidean Distance with the training sets. Minimum Euclidean distance helps to recognize the character. This system will enab

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Abstract: In the recent years, there has been rapid increase in the number of deaf and dumb victims due to birth defects, accidents and oral diseases. Since deaf and dumb people cannot communicate with normal person so they have to depend on some sort of visual communication. Gesture shows an expressive movement of body parts such as physical movements of head, face, arms, hand or body which convey some message. Gesture recognition is the mathematical interpretation of a human motion by a computing device. Sign language provide best communication platform for the hearing impaired and dumb person to communicate with normal person. The objective of this research is to develop a real time system for hand gesture recognition which recognize hand gestures, features of hands such as peak calculation and angle calculation and then convert gesture images into voice and vice versa. To implement this system we use a simple night vision web-cam with 20 megapixel intensity. The ideas consisted of designing

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Abstract: Usually many people suffered from visual disabilities. Written transcript is an appearing form of information that is unapproachable by a lot of sightless and visually impaired except it is symbolized in a non-visual form like Braille. Smart reader is a need of an effective system for visually impaired. The OCR (Optical Character Recognition) functions of MATLAB for converting image to text. This paper proposes the smart reader system for visually impaired. Here proposed a novel audio-tactile user interface that supports the user to read the information.

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Abstract: Image processing, with the development of technology, has a very wide field of usage. The health sector is also one of these uses. Much progress has been recorded on image processing and eye tracking. The both processes have software application. Image processing, a subdivision of the signal processor, can consist of an image or video-like visual objects as input and output as an image or various parameters of it. Also eye tracking is a kind of image processing process. In general terms, eye tracking refers to eye movements, image processing or image processing through the input and the recorded data as software.

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Abstract: Visually impaired people face lot of difficulties in their daily life. Many a times they rely on others for help. Several technologies for assistance of visually impaired people have been developed. Among the various technologies being utilized to assist the blind, Computer Vision based solutions are emerging as one of the most promising options due to their affordability and accessibility. This paper proposes a system for visually impaired people. The proposed system aims to create a wearable visual aid for visually impaired people in which speech commands are accepted from the user. Its functionality addresses identification of objects and sign boards. This will help the visually impaired person to manage day-to-day activities and to navigate through his/her surroundings. Raspberry Pi is used to implement artificial vision using python language on the Open CV platform.

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Abstract: There are many applications employing image processing and/or control algorithms. Video surveillance, visual inspection of manufactured goods are among the examples. Self - driven cars are today one of the hottest topic. Using powerful image processing, control and learning technologies, self-driven vehicles appears to be just around the corner. Another possible application are robots in charge of moving manufactured parts between the stations. Indeed, robotic-carriers (wheeled or tracked) may be visually guided to follow colored lines painted on the floor in order to automatically transport parts between the stations. This paper presents the construction and the implementation of such a visually guided vehicle which is able to follow a white line on a dark background. The vehicle is controlled by a so called embedded computer (a Raspberry Pi board) which uses a camera, minimal image processing and simple control law in order to command the vehicle to "stay on the line". A de

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Abstract: A robotics-based guidance method is presented to guide a robot platform which is designed independently to drive through the crops in a field according to the designed concept of open architecture. Thus, the robot platform is designed in real time to guide the platform on the basis of detection of crop using Ultra-Sonic sensor. The proposed system is basically developed to implement an agricultural production. This type of system is very useful in agriculture field where we need to spray the pesticide to different crops. This system automatically sense crop by using ultra-Sonic sensor using camera it will send the live video.

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