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.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Sparse representation-based MRI super-resolution reconstruction

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.

A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics

Analysis of Quantum Noise-Reducing Filters on Chest X-ray Images: A Review

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.

Statistical measurement of ultrasound placenta images using segmentation approach

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.

Impact of Image Artifact and Solution to the Image Quality Issues in Real Time SAR Images

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 ————————————————————

/pdf/a-study-on-stress-based-emotional-state-detection-using-eeg-zjjjajwkjk.pdf

A Study On Stress Based Emotional State Detection Using EEG Signals

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.

A Survey on Multi-feature Hand Biometrics Recognition

Dampers are being used in the buildings as one of the control mechanisms to bring down the vibration due to lateral loads like earthquakes. Different types of dampers such as viscous damper, friction damper, tuned mass damper, hybrid damper, etc have gained importance in the recent past in mitigating the undesirable effects of earthquakes. The behavior of damper installed buildings during earthquakes depends on the arrangement as well as the number of dampers also. In this study, fluid viscous damper (FVD) is provided in a G+19 storey high rise RC building to reduce the vibration due to earthquake effects. IS 1893:2016 code based methods are adopted to determine the seismic responses such as lateral displacement, drift, base shear and energy dissipation of the building with and without damper using ETABS software version 2018. The main focus of this investigation is to obtain a maximum benefit of using FVD considering the different arrangement and number of dampers in the building using PYTHON on the basis of drift and energy dissipation criteria. Out of 28 building models, models 4, 27 and 28 experienced drift more than the permissible limit (0.004) as specified in IS 1893:2016. The energy dissipation capacity of the buildings with dampers varies between 1306 and 2091 kN-m for the different models under study. The program suggests the positions 2, 22, 14, 17, 5, 11 with 80 numbers of dampers for the building and recommends the position 2 for the maximum benefit in terms of drift, energy dissipation and cost. viscous damper. responses such as top displacement, drift, base shear and energy dissipation capacity are obtained and compared. The results showed that in most of the cases damper placement reduced the dynamic responses of the building.

G. Malathi

Papers

Statistical measurement of ultrasound placenta images using segmentation approach

Impact of Image Artifact and Solution to the Image Quality Issues in Real Time SAR Images

A Study On Stress Based Emotional State Detection Using EEG Signals

A Survey on Multi-feature Hand Biometrics Recognition

A study on high-rise RC structure with fluid viscous damper using python