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A smartphone-based gait recognition system is very interesting research in surveillance. Its goal is to recognize a target user from their walking pattern using the inertial signal. However, the performance in realistic scenarios is unsatisfactory due ...
One of the main challenges in controlling the spread of COVID19 pandemic is to diagnose infection early. The most reliable method RT - PCR takes several hours to give results. Although the Anti-Body (Serological) test gives the results in a few hours, ...
Optical Coherence Tomography (OCT) is a non-invasive imaging technology for diagnosing various macular pathologies. It assists ophthalmologists to detect abnormalities in the retina and thereby avoid many sight-threatening conditions. However, manual ...
The Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) is India's premier conference in Computer Vision, Graphics, Image Processing and related fields. Started in 1998, it is a biennial international conference providing a ...
We propose a novel Line based parameterization for category specific CAD models. The proposed parameterization associates 3D category-specific CAD model and object under consideration using a dictionary based RANSAC method that uses object Viewpoints as ...
Single image dehazing is an ill-posed problem that requires assumptions, priors and constraints to solve. In this paper, boundary constraint utilizing median filter has been proposed on the image radiance for the rough estimation of transmission-map in ...
In this paper, we propose a pipeline for generating a 2D floorplan using depth cameras. In our pipeline we use an existing approach to recovering the camera motion trajectories from the depth and RGB sequences. Given these motion estimates we construct ...
Accurate and robust visual object tracking is one of the most challenging computer vision problems. Recently, discriminative correlation filter trackers have shown promising results on benchmark datasets with continuous performance improvements in ...
Deep approaches to predict monocular depth and ego-motion have grown in recent years due to their ability to produce dense depth from monocular images. The main idea behind them is to optimize the photometric consistency over image sequences by warping ...
Advancement in the field of 3D capture, owing to use of consumer depth sensors, has reinvigorated the research interest for scalable shape classification and recognition algorithms. Majority of recent deep learning pipelines for 3D shapes uses ...
Tracking athletes performing a particular action in the presence of crowded backgrounds is a challenging task. This task becomes even more difficult in activities like cliff-diving, uneven bars, parallel bars, and others, where athletes perform highly ...
With contemporary advancements of graphics engines, recent trend in deep learning community is to train models on automatically annotated simulated examples and apply on real data during test time. This alleviates the burden of manual annotation. ...
Deep generative models like variational autoencoders approximate the intrinsic geometry of high dimensional data manifolds by learning a set of low-dimensional latent-space variables and an embedding function. The geometrical properties of these latent ...
In this paper, we propose a novel, real-time dynamic hand gesture recognition framework using convolutional neural network with depth and RGB data fusion. Hand gestures are a natural form of communication between humans as well as between human and ...
In this paper, we attempt to advance the research work done in human action recognition to a rather specialized application namely Indian Classical Dance (ICD) classification. The variation in such dance forms in terms of hand and body postures, facial ...
As much as good representation and theory are needed to explain human actions, so are the action videos used for learning good segmentation techniques. To accurately model complex actions such as diving, figure skating, and yoga practices, videos ...
The problem of temporal Cricket stroke localization is important and is the first step towards creation of a domain-specific sports activity dataset. Having a pre-annotated dataset of Cricket activities would be beneficial for training deep neural ...
In practice, images can contain different amounts of noise for different color channels, which is not acknowledged by existing super-resolution approaches. In this paper, we propose to super-resolve noisy color images by considering the color channels ...
In this paper, we address the problem of 3D object categorization for point cloud data. With the availability of inexpensive scanning devices and powerful computational resources, there is a rapid growth of point cloud data. This necessitates efficient ...
Diabetic Retinopathy (DR) is the leading cause of blindness in the modern world. Diagnosis of DR requires an experienced ophthalmologist and it is a tedious and time-consuming process. In this paper, we propose a Convolutional Neural Network (CNN) based ...