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Anil Kumar Sao

Bio: Anil Kumar Sao is an academic researcher from Indian Institute of Technology Mandi. The author has contributed to research in topics: Sparse approximation & Face (geometry). The author has an hindex of 15, co-authored 79 publications receiving 696 citations. Previous affiliations of Anil Kumar Sao include Indian Institute of Technology Madras.


Papers
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Journal Article•DOI•
TL;DR: This paper proposes a new approach to address issues in a unified framework of depth map restoration, based on sparse representation, and suggests an alternative method of reconstructing dense depth map from very sparse non- uniformly sampled depth data by sequential cascading of uniform and non-uniform upsampling techniques.
Abstract: Depth map sensed by low-cost active sensor is often limited in resolution, whereas depth information achieved from structure from motion or sparse depth scanning techniques may result in a sparse point cloud. Achieving a high-resolution (HR) depth map from a low resolution (LR) depth map or densely reconstructing a sparse non-uniformly sampled depth map are fundamentally similar problems with different types of upsampling requirements. The first problem involves upsampling in a uniform grid, whereas the second type of problem requires an upsampling in a non-uniform grid. In this paper, we propose a new approach to address such issues in a unified framework, based on sparse representation. Unlike, most of the approaches of depth map restoration, our approach does not require an HR intensity image. Based on example depth maps, sub-dictionaries of exemplars are constructed, and are used to restore HR/dense depth map. In the case of uniform upsampling of LR depth map, an edge preserving constraint is used for preserving the discontinuity present in the depth map, and a pyramidal reconstruction strategy is applied in order to deal with higher upsampling factors. For upsampling of non-uniformly sampled sparse depth map, we compute the missing information in local patches from that from similar exemplars. Furthermore, we also suggest an alternative method of reconstructing dense depth map from very sparse non-uniformly sampled depth data by sequential cascading of uniform and non-uniform upsampling techniques. We provide a variety of qualitative and quantitative results to demonstrate the efficacy of our approach for depth map restoration.

30 citations

Journal Article•DOI•
TL;DR: A template-matching approach for face verification, which neither synthesizes the face image nor builds a model of theFace verification approach is proposed based on autoassociative neural network models to verify the identity of a person.
Abstract: Human faces are similar in structure with minor differences from person to person. These minor differences may average out while trying to synthesize the face image of a given person, or while building a model of face image in automatic face recognition. In this paper, we propose a template-matching approach for face verification, which neither synthesizes the face image nor builds a model of the face image. Template matching is performed using an edginess-based representation of the face image. The edginess-based representation of face images is computed using 1-D processing of images. An approach is proposed based on autoassociative neural network models to verify the identity of a person. The issues of pose and illumination in face verification are addressed.

29 citations

Journal Article•DOI•
TL;DR: Results on combining the evidences from the two phase functions show that the proposed method provides an alternative representation of the face images for dealing with the issue of illumination in face recognition.
Abstract: In this paper, we propose a representation of the face image based on the phase of the 2-D Fourier transform of the image to overcome the adverse effect of illumination. The phase of the Fourier transform preserves the locations of the edges of a given face image. The main problem in the use of the phase spectrum is the need for unwrapping of the phase. The problem of unwrapping is avoided by considering two functions of the phase spectrum rather than the phase directly. Each of these functions gives partial evidence of the given face image. The effect of noise is reduced by using the first few eigenvectors of the eigenanalysis on the two phase functions separately. Experimental results on combining the evidences from the two phase functions show that the proposed method provides an alternative representation of the face images for dealing with the issue of illumination in face recognition.

26 citations

Book Chapter•DOI•
20 Sep 2018
TL;DR: A new deep learning algorithm that does not depend on accurate segmentation by directly classifying image patches with cells is proposed that achieves state of the art accuracy while being extremely fast.
Abstract: Automated classification of cervical cancer cells has the potential to reduce high mortality rates due to cervical cancer in developing countries. However traditional algorithms for the same depend on accurate segmentation of cells, which in itself is an open problem. Often the algorithms are also not evaluated by considering the huge inter-observer variability in ground truth labels. We propose a new deep learning algorithm that does not depend on accurate segmentation by directly classifying image patches with cells. We evaluate the proposed algorithm on the popular Herlev dataset and show that it achieves state of the art accuracy while being extremely fast. The experimental results are also demonstrated using AIndra dataset collected by us, which also captures the inter observer variability.

25 citations

Journal Article•DOI•
TL;DR: The proposed method to combine the partial evidences obtained for each representation using an auto-associative neural network (AANN) model to arrive at a decision for face verification shows that the performance of the system using potential field representation is better than that using the edge gradient representation or the edge orientation representation.
Abstract: In this paper we discuss the significance of representation of images for face verification. We consider three different representations, namely, edge gradient, edge orientation and potential field derived from the edge gradient. These representations are examined in the context of face verification using a specific type of correlation filter, called the minimum average correlation energy (MACE) filter. The different representations are derived using one-dimensional (1-D) processing of image. The 1-D processing provides multiple partial evidences for a given face image, one evidence for each direction of the 1-D processing. Separate MACE filters are used for deriving each partial evidence. We propose a method to combine the partial evidences obtained for each representation using an auto-associative neural network (AANN) model, to arrive at a decision for face verification. Results show that the performance of the system using potential field representation is better than that using the edge gradient representation or the edge orientation representation. Also, the potential field representation derived from the edge gradient is observed to be less sensitive to variation in illumination compared to the gray level representation of images.

24 citations


Cited by
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Journal Article•DOI•
TL;DR: In this article, an extensive review on recent advancements in the field of solar photovoltaic power forecasting is presented, which aims to analyze and compare various methods of solar PV power forecasting in terms of characteristics and performance.

539 citations

Journal Article•DOI•
TL;DR: An attempt has been made to scrutinize the applications of artificial neural network (ANN) as an intelligent system-based method for optimizing and the prediction of different solar energy devices’ performance.

389 citations

Journal Article•DOI•
TL;DR: This paper presents a preliminary study on how to review solar irradiance and photovoltaic power forecasting using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting.

348 citations

Journal Article•DOI•
TL;DR: To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper.
Abstract: Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.

334 citations

Journal Article•DOI•
TL;DR: Overall, this review provides preliminary guidelines, research gaps and recommendations for developing a better and more user-friendly DG energy planning optimisation tool.
Abstract: An overview of numerical and mathematical modelling-based distributed generation (DG) system optimisation techniques is presented in this review paper. The objective is to compare different aspects of these two broad classes of DG optimisation techniques, explore their applications, and identify potential research directions from reviewed studies. Introductory descriptions of general electrical power system and DG system are first provided, followed by reviews on renewable resource assessment, load demand analysis, model formulation, and optimisation techniques. In renewable resource assessment model review, uncertain solar and wind energy resources are emphasised whereas applications of forecasting models have been highlighted based on their prediction horizons, computational power requirement, and training data intensity. For DG optimisation framework, (solar, wind and tidal) power generator, energy storage and energy balance models are discussed; in optimisation technique section, both numerical and mathematical modelling optimisation methods are reviewed, analysed and criticised with recommendations for their improvements. In overall, this review provides preliminary guidelines, research gaps and recommendations for developing a better and more user-friendly DG energy planning optimisation tool.

221 citations