Subrahmanyam Murala

Indian Institute of Technology Ropar | Indian Institute of Technology Punjab · Department of Electrical Engineering

Due to the unavailability of large scale underwater depth image datasets and ill-posed problems, underwater single image depth prediction is a challenging task. An unambiguous depth prediction for single underwater image is an essential part of applications like underwater robotics, marine engineering, etc. This paper presents an end-to-end Underwa...

Motion estimation is the basic need for the success of many video analysis algorithms such as moving object detection, human activity recognition, etc. Most of the motion estimation algorithms are prone to weather conditions and thus, they fail to estimate the motion in degraded weather. Severe weather situations like snow, rain, haze, smog, etc.,...

Moving object segmentation (MOS) in videos received considerable attention because of its broad security-based applications like robotics, outdoor video surveillance, self-driving cars, etc. The current prevailing algorithms highly depend on additional trained modules for other applications or complicated training procedures or neglect the inter-fr...

Recently, single image super-resolution (SISR), aiming to preserve the lost structural and textural information from the input low resolution image, has witnessed huge demand from the videos and graphics industries. The exceptional success of convolution neural networks (CNNs), has absolutely revolutionized the field of SISR. However, for most of t...

Foreground-background segmentation (FBS) is one of the prime tasks for automated video-based applications like traffic analysis and surveillance. The different practical scenarios like weather degraded videos, irregular moving objects, dynamic background, etc., make FBS a challenging task. The existing FBS algorithms mainly depend on one of the thr...

Low light image enhancement is one of the challenging tasks in computer vision, and it becomes more difficult when images are very dark. Recently, most of low light image enhancement work is done either on synthetic data or on the images which are considerably visible. In this paper, we propose a method to enhance real-world night time images, whic...

The current prevailing algorithms highly depend on additional pre-trained modules trained for other applications or complicated training procedures or neglect the inter-frame spatio-temporal structural dependencies. Also, the generalized effect of existing works with completely unseen data is difficult to identify. Specifically, the outdoor videos...

In video frame segmentation, many existing deep networks and contemporary approaches give a remarkable performance with the assumption that the only foreground is moving, and the background is stationary. However, in the presence of infrequent motion of foreground objects, sudden illumination changes in the background, bad weather, dynamic backgrou...

Degradation in the quality of images that are captured in the hazy environment is mainlydue to 1) different weather conditions and 2) the attenuation in reflected light. These factors introduce a severe color distortion and low visibility in the captured images. To tackle these problems, we propose an end-to-end trainable image de-hazing network na...

In this paper, we have proposed a novel feature descriptors combining color and texture information collectively. In our proposed color descriptor component, the inter-channel relationship between Hue (H) and Saturation (S) channels in the HSV color space has been explored which was not done earlier. We have quantized the H channel into a number of...

Underwater image restoration is a challenging problem due to the multiple distortions. Degradation in the information is mainly due to the 1) light scattering effect 2) wavelength dependent color attenuation and 3) object blurriness effect. In this letter, we propose a novel end-to-end deep network for underwater image restoration. The proposed net...

The existing approaches achieved remarkable performance in many computer vision applications like moving object segmentation (MOS), classification, etc. However, in presence of infrequent motion of foreground objects, bad weather and dynamic background, the accurate foreground-background segmentation is a tedious task. In addition, the computationa...

Depth prediction from single image is a challenging task due to the intra scale ambiguity and unavailability of prior information. The prediction of an unambiguous depth from single RGB image is very important aspect for computer vision applications. In this paper, an end-to-end sparse-to-dense network (S2DNet) is proposed for single image depth es...

Moving object segmentation in videos (MOS) is a highly demanding task for security-based applications like automated outdoor video surveillance. Most of the existing techniques proposed for MOS are highly depend on fine-tuning a model on the first frame(s) of test sequence or complicated training procedure, which leads to limited practical service-...

Even with proper acquisition of brain tumor images, the accurate and reliable segmentation of tumors in brain is a complicated job. Automatic segmentation become possible with development of deep learning algorithms that brings plethora of solutions in this research prospect. In this paper, we designed a network architecture named as residual cycli...

Unlike prevalent facial expressions, micro expressions have subtle, involuntary muscle movements which are short-lived in nature. These minute muscle movements reflect true emotions of a person. Due to the short duration and low intensity, these micro-expressions are very difficult to perceive and interpret correctly. In this paper, we propose the...

Haze removal from a single image is a challenging task. Estimation of accurate scene transmission map (TrMap) is the key to reconstruct the haze-free scene. In this paper, we propose a convolutional neural network based architecture to estimate the TrMap of the hazy scene. The proposed network takes the hazy image as an input and extracts the haze...

Effective motion estimation is one of the prime steps for any human action recognition (HAR) algorithm. Optical flow (OF) and motion history image (MHI) are two well-known methods for motion estimation in videos. OF has several advantages over MHI. But the major drawback with OF is that it is computationally very expensive as compared to the MHI. T...

Unlike prevalent facial expressions, micro expressions have subtle, involuntary muscle movements which are short-lived in nature. These minute muscle movements reflect true emotions of a person. Due to the short duration and low intensity, these micro-expressions are very difficult to perceive and interpret correctly. In this paper, we propose the...

Facial expressions exhibit strong symptom of human emotional state. Automated facial expression recognition system plays a significant role in the study of human behaviour analysis. In this paper, we propose a robust feature descriptor named regional adaptive affinitive patterns (RADAP) for facial expression recognition. The RADAP computes the posi...

In this paper, we have proposed a novel feature descriptors combining color and texture information collectively. In our proposed color descriptor component, the inter-channel relationship between Hue (H) and Saturation (S) channels in the HSV color space has been explored which was not done earlier. We have quantized the H channel into a number of...

Vision-based systems for healthcare applications utilize computer vision techniques to provide intelligent support to patients and medical practitioners for common needs to sophisticated technical assistance. With the ease of availability and advancements in sensing, mobile devices, and computing power, development of advanced computer vision techn...

In this paper, new feature descriptors are designed for medical image retrieval and change detection applications respectively. Inspired by isomerism, we propose a novel feature descriptor named antithetic isomeric cluster pattern (ANTIC). The ANTIC is defined by the two properties: cluster patterns and antithetic isomerism (ANTI). The cluster patt...

Recognition of human actions from videos can be improved if depth information is available. Depth information certainly helps in segregating foreground motion from the background. Single image depth estimation (SIDE) is a commonly used method for the analysis of weather degraded images. In this study, the idea of SIDE is extended to human action re...

A novel color feature descriptor, Multichannel Distributed Local Pattern (MDLP) is proposed in this manuscript. The MDLP combines the salient features of both local binary and local mesh patterns in the neighborhood. The multi-distance information computed by the MDLP aids in robust extraction of the texture arrangement. Further, MDLP features are...

Background subtraction in video provides the preliminary information which is essential for many computer vision applications. In this paper, we propose a sequence of approaches named CANDID to handle the change detection problem in challenging video scenarios. The CANDID adaptively initializes the pixel-level distance threshold and update rate. Th...

In this paper, a novel approach for content based image retrieval (CBIR) in diabetic retinopathy (DR) is proposed. The concept of salient point selection and inter-plane relationship technique is used. Salient points are selected from edgy image and later using inter-planer relationship, Local Binary Patterns (LBPs) are calculated using the salient...

In this paper, a new texture descriptor named "Fractional Local Neighborhood Intensity Pattern" (FLNIP) has been proposed for content based image retrieval (CBIR). It is an extension of the Local Neighborhood Intensity Pattern (LNIP)[1]. FLNIP calculates the relative intensity difference between a particular pixel and the center pixel of a 3x3 wind...

In this paper, authors proposed a novel approach for image retrieval in transform domain using 3D local transform pattern (3D-LTraP). The various existing spatial domain techniques such as local binary pattern (LBP), Local ternary pattern (LTP), Local derivative pattern (LDP) and Local tetra pattern (LTrP) are encoding the spatial relationship betw...

In this paper, a new texture descriptor based on the local neighborhood intensity difference is proposed for content based image retrieval (CBIR). For computation of texture features like Local Binary Pattern (LBP), the center pixel in a 3*3 window of an image is compared with all the remaining neighbors, one pixel at a time to generate a binary bi...

In this paper, we propose a novel feature extraction and retrieval technique for medical images. Our proposed technique (LPVCoP) extracts similarity between grayscale images by using the relationship between reference pixel and its surrounding neighbor pixels through peak/valley edges which are obtained by taking directional derivatives. LPVCoP use...

This study proposes a new feature descriptor, local directional mask maximum edge pattern, for image retrieval and face recognition applications. Local binary pattern (LBP) and LBP variants collect the relationship between the centre pixel and its surrounding neighbours in an image. Thus, LBP based features are very sensitive to the noise variation...

In this chapter, a new feature descriptor, local mesh correlation histograms (LMeCH) is proposed for content-based image retrieval (CBIR). The LMeCH integrates the local mesh patterns (LMeP) and grayscale joint histogram. Firstly, the LMeP features are extracted from the image and then the joint histogram is constructed between the LMeP and graysca...

In this paper, a new coding scheme, local Gabor maximum edge position octal patterns (LGMEPOP) is proposed for content based image retrieval. The standard local binary pattern (LBP) collects the sign edge (binary code) information between the center pixel and its surrounding neighbors in an image. Further, the concept of LBP is extended to local ma...

Nowadays, images are part of our daily life, and content based image retrieval (CBIR) is a wide research area. Many researchers have done work on local patterns and directional patterns. In this paper, a new direction based feature descriptor has been proposed for content based image retrieval. The proposed method extracts the directional features...

A real world problem of image retrieval and searching is considered in this paper. In modern generation, managing images from a large storage medium is not a straightforward job. Many researchers have worked on texture features, and produced diverse feature descriptors based on uniform, rotation invariant, edges and directional properties. However,...

In this paper, we propose a new algorithm using spherical symmetric three dimensional local ternary patterns (SS-3D-LTP) for natural, texture and biomedical image retrieval applications. The existing local binary patterns (LBP), local ternary patterns (LTP), local derivative patterns (LDP), local tetra patterns (LTrP) etc., are encode the relations...

In this paper, a new image indexing and retrieval algorithm using local mesh patterns are proposed for biomedical image retrieval application. The standard local binary pattern encodes the relationship between the referenced pixel and its surrounding neighbors, whereas the proposed method encodes the relationship among the surrounding neighbors for...

Content based image retrieval is grievous need of present scenario in digital imaging world. This work presents a new multi-scale content based image retrieval system which leverages the multi-resolution property of discrete wavelet transform (DWT) and the local information attribute of local extrema patterns (LEPs). Two level DWT is applied on ima...

In this paper, a new image indexing and retrieval algorithm using local mesh patterns (LMeP) is proposed for biomedical image retrieval application. The standard local binary pattern (LBP) encodes the relationship between the referenced pixel and its surrounding neighbors, whereas the proposed method encodes the relationship among the surrounding n...

This paper presents a novel feature extraction algorithm called local ternary co-occurrence patterns (LTCoP) for biomedical image retrieval. The LTCoP encodes the co-occurrence of similar ternary edges which are calculated based on the gray values of center pixel and its surrounding neighbors. Whereas the standard local derivative pattern (LDP) enc...

A new algorithm meant for biomedical image retrieval application is presented in this paper. The local region of image is represented by peak valley edge patterns (PVEP), which are calculated by the first-order derivatives in 0°, 45°, 90° and 135° directions. The PVEP differs from the existing local binary pattern (LBP) in a manner that it extracts...

In this paper, the modified color motif co-occurrence matrix (MCMCM) is presented for content-based image retrieval. The proposed method collects the inter-correlation between the red, green, and blue color planes which is absent in color motif co-occurrence matrix. The proposed method integrates the MCMCM and difference between the pixels of a sca...

In this paper, a new algorithm meant for object tracking application is proposed using local extrema patterns (LEP) and color features. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray level values. The proposed method differs from the existing LBP in a manner t...

In this paper, a new pattern based feature, local mesh peak valley edge pattern (LMePVEP) is proposed for biomedical image indexing and retrieval. The standard LBP extracts the gray scale relationship between the center pixel and its surrounding neighbours in an image. Whereas the proposed method extracts the gray scale relationship among the neigh...

A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented in this paper. The local region of image is represented by local maximum edge binary patterns (LMEBP), which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LMEBP...

In this paper, we propose a novel image indexing and retrieval algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR). The standard local binary pattern (LBP) and local ternary pattern (LTP) encode the relationship between the referenced pixel and its surrounding neighbors by computing gray-level difference. The propo...

A new image indexing and retrieval system for content based image retrieval (CBIR) is proposed in this paper. The characteristics (vector points) of image are computed using color (color histogram) and SOT (spatial orientation tree). The SOT defines the spatial parent–child relationship among wavelet coefficients in multi-resolution wavelet sub-ban...

In this paper, a new algorithm using directional local extrema patterns meant for content-based image retrieval application is proposed. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray-level values. The proposed method differs from the existing LBP in a manner...

A new image indexing and retrieval algorithm known as local binary pattern (LBP) correlogram is presented in this paper. LBP histogram captures only the patterns distribution in a texture while the spatial correlation between the pair of patterns is gathered by LBP correlogram. Multi-resolution texture decomposition and color correlation has been e...

This paper presents a relevance feedback (RF) algorithm for scale invariant features extracting form Caltech image database. The RF is a powerful technique to bridging the gap between high-level concepts and low-level features in image retrieval systems. This paper attempts to enhance the performance of RF by exploiting unlabelled images in the dat...

A new algorithm for medical image retrieval is presented in the paper. An 8-bit grayscale image is divided into eight binary bit-planes, and then binary wavelet transform (BWT) which is similar to the lifting scheme in real wavelet transform (RWT) is performed on each bitplane to extract the multi-resolution binary images. The local binary pattern...

In this paper, a new image‐indexing algorithm with the combination of wavelet and Rotated Wavelet Correlogram (RWC) is proposed in contrast to the Gabor Wavelet Correlogram (GWC). The 0° and 90° information (sub‐band) of image collected from Standard Wavelet Filters (SWFs) and +45° and −45° are collected from Rotated Wavelet Filters (RWFs) and furt...