G. Malathi

Abstract: Handwriting Analysis is a method to understand and predict the characteristic traits of a person based on his handwriting style. Graphology is the scientific term used for handwriting analysis. Professional handwriting examiners, called graphologists, manually study and understand the handwriting of an individual to classify the writers personality. Nevertheless, the manual process of handwriting analysis is time-consuming, costly and depends majorly on the skills of the graphologists. To make this process computerized we extracted several features of handwriting samples and classified the writer into 5 personality traits namely Energetic, Extrovert, Introvert, Sloppy and Optimistic. Histogram of oriented gradient(HOG) extracts the features from the handwriting sample of the writer which serves as an input for the Support Vector Machine model to give output as the personality trait of the person. For this paper, digital handwriting sample data of 50 different users were collected. The proposed system predicts the personality trait of a person with 80% correctness using the Polynomial kernel. In this paper, we propose a computerized method for personality trait prediction based on the users handwriting. Two different methods are applied to the same handwriting sample data to measure and compare the performance of the proposed system.

Abstract: Basically, the Synthetic Aperture Radar (SAR) images are often degraded due to three factors namely noise, blur and artifact. The noise is the undesirable fluctuation in a random portion of the image and is often detracts from the image. The blur will reduce the object visibility. According to the recent literatures the most dangerous effect which appear in real time images are artifacts. The shadowing effect is the best example to depict the image artifact. The presence of shadows mostly affects the vital information of an image. In the shadowing effect, the portion of the object is totally obscured or hidden from the image. In this paper, we focus the impact of image artifact such as shadow in real time images and we focus how to detect the shadowing effect. Further, this paper is devoted to removal of shadows from very high resolution (VHR) SAR images and aerial view Images.

Abstract: Biometrics is the one of the most emerging technology in our day to day life. Biometrics is going to be a future of security and applications. Why we need biometrics? The password is not user-friendly, we could not dump all password in our brain. Sometimes we couldn’t remember which application which password we used. To overcome all those problems, Biometrics provides security “You are the password for your application”. Biometrics provides trustworthiness. Both behavioral and physiological characteristics of biometric features recognize the individuality of a person whether the user is genuine or an imposter. Hand biometrics is one of the traditional biometric systems. Generally, hand biometrics can be either captured by contact and contactless-based approach. Hand biometrics comprises of palm textures, knuckle, hand geometry, fingerprints which can be used for recognition. Hand biometrics consist of more uniqueness and individuality of the person has been identified. In this survey paper, we are going to present the working of the hand geometry and palm print technically. The primary objective is to study in-depth of hand biometric system and architecture of hand biometrics. The secondary objective is to literature survey on hand geometry and palmprint. This paper also studies the pre-processing and feature extraction of various systems used in hand and palm print. Finally paper addresses the performance evaluation techniques of hand biometrics.

Abstract: Emotion plays an important role in day today’s life of human being. The brain is a central processing unit for every humans and responses to different emotions such as memory, anger, happiness, sad, frustration, fear, satisfaction, calm and pleasant. This paper focuses on the survey of stress based emotions using EEG signals and machine learning models that are used in the detection ————————————————————

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

Abstract: Magnetic Resonance Imaging (MRI) data collection is influenced by SNR, hardware, image time, and other factors. The super-resolution analysis is a critical way to improve the imaging quality. This work presents a framework of super-resolution MRI via sparse reconstruction, and this method is promising to solve the data collection limitations. A novel dictionary training method for sparse reconstruction for enhancing the similarity of sparse representations between the low resolution and high resolution MRI block pairs through simultaneous training two dictionaries. Low resolution MRI blocks generate the high resolution MRI blocks with proposed sparse representation (SR) coefficients. Comprehensive evaluations are implemented to test the feasibility and performance of the SR–MRI method on the real database.

Abstract: Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.

Abstract: Face recognition has the wide research and applications on many areas. Many surveys of face recognition are implemented. Different from previous surveys on from a single viewpoint of application, method or condition, this paper has a comprehensive survey on face recognition from practical applications, sensory inputs, methods, and application conditions. In the sensory inputs, we review face recognition from image-based, video-based, 3D-based and hypersprectral image based face recognition, and a comprehensive survey of face recognition methods from the viewpoints of signal processing and machine learning are implemented, such as kernel learning, manifold learning method. Moreover we discuss the single training sample based face recognition and under the variable poses. The prominent algorithms are described and critically analyzed, and relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed

Abstract: Medical diagnosis is the major challenge faced by the medical experts. Highly specialized tools are necessary to assist the experts in diagnosing the diseases. Gestational Diabetes Mellitus is a condition in pregnant women which increases the blood sugar levels. It complicates the pregnancy by affecting the placental growth. The ultrasound screening of placenta in the initial stages of gestation helps to identify the complication induced by GDM on the placental development which accounts for the fetal growth. This work focus on the classification of ultrasound placenta images into normal and abnormal images based on statistical measurements. The ultrasound images are usually low in resolution which may lead to loss of characteristic features of the ultrasound images. The placenta images obtained in an ultrasound examination is stereo mapped to reconstruct the placenta structure from the ultrasound images. The dimensionality reduction is done on stereo mapped placenta images using wavelet decomposition. The ultrasound placenta image is segmented using watershed approach to obtain the statistical measurements of the stereo mapped placenta images. Using the statistical measurements, the ultrasound placenta images are then classified as normal and abnormal using Back Propagation neural networks.