Rajeev Srivastava

Bio: Rajeev Srivastava is an academic researcher from Indian Institute of Technology (BHU) Varanasi. The author has contributed to research in topics: Anisotropic diffusion & Image retrieval. The author has an hindex of 19, co-authored 193 publications receiving 1519 citations. Previous affiliations of Rajeev Srivastava include Indian Institutes of Technology & Banaras Hindu University.

Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

Abstract: A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law's Texture Energy based features, Tamura's features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images.

Soils of India: historical perspective, classification and recent advances

Abstract: Derived from a wide range of rocks and minerals, a large variety of soils occur in the Indian subcontinent. Soil-forming factors like climate, vegetation and topography acting for varying periods on a range of rock formations and parent materials, have given rise to different kinds of soil. The National Bureau of Soil Survey and Land Use Planning, Nagpur has developed a database on soils with field and laboratory studies over the last 30 years. This has generated maps and soil information at different scales, showing area and distribution of various soil groups in different agroecological subregions. The 1 : 250,000 scale map shows a threshold soil variation index of 4–5 and 10–25 soil families per m ha for alluvial plains and black soil regions respectively. Progress in basic and fundamental research in Indian soils has been reviewed in terms of soils, their formation related to climate, relief, organisms, parent materials and time.

Sensing mechanism in tin oxide-based thick-film gas sensors

Abstract: A model has been developed for the sensing mechanism of metal oxide-based thick-film gas sensors. The model explains the behaviour of the sensor conductance as a function of the concentration of test gas and the operating temperature of the sensor. Using the Schottky-barrier conduction mechanism through grain boundaries, a relationship between the degree of surface coverage θ and the conductance G has been obtained. To relate the conductance with the concentration of the gas, the Freundlich adsorption isotherm for gases and vapours on a solid surface has been used. The isotherm relates the degree of surface coverage θ with the partial pressure of the gas (concentration). By eliminating θ, an expression relating the variation of G with concentration has been obtained. To study the validity of the model, a thick-film Pd-doped tin oxide gas sensor has been fabricated and tested with propanol (C3H7OH). The variation in the conductance with changes in concentration and temperature has been observed. The observed data show an excellent fit with the developed model. Using the experimental data, the constants of the theoretical equation have also been evaluated.

Image Processing

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.

Remote Sensing And Image Interpretation

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IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Statistical optics

Abstract: Course Description This is an advanced course in which we explore the field of Statistical Optics. Topics covered include such subjects as the statistical properties of natural (thermal) and laser light, spatial and temporal coherence, effects of partial coherence on optical imaging instruments, effects on imaging due to randomly inhomogeneous media, and a statistical treatment of the detection of light. Development of this more comprehensive model of the behavior of light draws upon the use of tools traditionally available to the electrical engineer, such as linear system theory and the theory of stochastic processes.