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A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single Image Dehazing✱
Dehazing is a difficult process because of the damage caused by the non-uniform fog and haze distribution in images. To address these issues, a Multi-Scale Residual dense Dehazing Network (MSRDNet) is proposed in this paper. A Contextual feature ...
Interpreting Intrinsic Image Decomposition using Concept Activations
Evaluation of ill-posed problems like Intrinsic Image Decomposition (IID) is challenging. IID involves decomposing an image into its constituent illumination-invariant Reflectance (R) and albedo-invariant Shading (S) components. Contemporary IID ...
Quaternion Factorized Simulated Exposure Fusion
Image Fusion maximizes the visual information at each pixel location by merging content from multiple images in order to produce an enhanced image. Exposure Fusion, specifically, fuses a bracketed exposure stack of poorly lit images to generate a ...
Learning from Multiple Datasets for Recognizing Human Actions
Action recognition has evolved as an important research problem in the computer vision community. Majority of the human action recognition methods focus mainly on training from a single dataset. Scarcity of labelled data in a single dataset often leads ...
Topological Shape Matching using Multi-Dimensional Reeb Graphs
Shape matching or retrieval is an important problem in computer graphics and data analysis. Topological techniques based on Reeb graphs and persistence diagrams have been employed to obtain an effective solution in this problem. In the current paper, ...
Convolutional Ensembling based Few-Shot Defect Detection Technique
Over the past few years, there has been a significant improvement in the domain of few-shot learning. This learning paradigm has shown promising results for the challenging problem of anomaly detection, where the general task is to deal with heavy ...
Masked Student Dataset of Expressions
Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 ...
Performance, Trust, or both? COVID-19 Diagnosis and Prognosis using Deep Ensemble Transfer Learning on X-ray Images✱
The COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. ...
Alzheimer’s severity classification using Transfer Learning and Residual Separable Convolution Network
Severity classification is the most pivotal task in Alzheimer’s disease diagnosis. Detection of brain structural changes from brain MR images is crucial for Alzheimer’s classification. In this paper, we have proposed a transfer learning and residual ...
Detecting Coronavirus (COVID -19) Disease Cues from Chest Radiography Images
This paper proposes a deep learning-based approach to detect COVID-19 infections in lung tissues from chest Computed Tomography (CT) images. A two-stage classification model is designed to identify the infection from CT scans of COVID-19 and Community ...
Posture Guided Human Action Recognition for Fitness Applications
Human action recognition has attracted a lot of attention in the recent past due to newer applications in computer vision such as fitness tracking, augmented reality and virtual reality. Most of the existing deep learning based methods first deploy a ...
Towards Robust Handwritten Text Recognition with On-the-fly User Participation
Long-term OCR services aim to provide high-quality output to their users at competitive costs. It is essential to upgrade the models because of the complex data loaded by the users. The service providers encourage the users who provide data where the ...
Low Resource Degraded Quality Document Image Binarization – Domain Adaptation is the Way
Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy ...
A Globally-Connected and Trainable Hierarchical Fine-Attention Generative Adversarial Network based Adversarial Defense
Deep Neural Network (DNN) inferences have been proven highly susceptible to carefully engineered adversarial perturbations, presenting a pivotal hindrance to real-world Computer Vision tasks. Most of the existing defenses have poor generalization ...
FERA-net: An emotion classifier from facial expressions using FER-net with attention mechanism✱
Emotions play a significant and important role in daily life. It can be recognized by facial expressions, speech, and physiological signals such as electroencephalogram (EEG), electrocardiogram (ECG), body temperature, etc. Facial expression is one of ...
One shot learning in StyleALAE: Preserving facial identity during semantic modification
- Ravi Kiran Reddy Kotlo Budde,
- Kumar Shubham,
- Gopalakrishnan Venkatesh,
- Sriram Gandikota,
- Sarthak Khoche,
- Dinesh Babu Jayagopi,
- Gopalakrishnan Srinivasaraghavan
Semantic face editing of real-world facial images is an important application of generative models. Recently, several works have explored possible techniques to generate such modifications by utilizing the latent structure of pre-trained GAN models. ...
Split and Knit: 3D Fingerprint Capture with a Single Camera
3D fingerprint capture is less sensitive to skin moisture levels and avoids skin deformation, which is common in contact-based sensors, in addition to capturing depth information. Unfortunately, its adoption is limited due to high cost and system ...
I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets✱
Laparoscopic cholecystectomy is a widely performed minimally invasive surgical procedure that imposes many challenges to the operating surgeon. While we strive to understand and automate such surgeries, the key is to identify the actions involved in ...
Supervised Contrastive Multi-tasking Learning Based Hierarchical Yoga Pose Classification Using CNNs
In this paper, we propose a technique for hierarchical yoga pose classification in a multi-tasking framework. Novelty lies in the proposed supervised contrastive combined loss function. We propose the usage of linear combination of three loss functions:...
Overcoming Label Noise for Source-free Unsupervised Video Domain Adaptation
Despite the progress seen in classification methods, current approaches for handling videos with distribution shifts in source and target domains remain source-dependent as they require access to the source data during the adaptation stage. In this ...
REF-SHARP: REFined face and geometry reconstruction of people in loose clothing✱
In this paper, we address the problem of monocular 3D human reconstruction with an acute focus on the challenge of recovering person-specific facial geometry as well as suppressing surface noise, specifically addressing the issue of false geometrical ...
End-to-End GPU-Accelerated Low-Poly Remeshing using Curvature Map and Voronoi Tessellation✱
We propose a novel algorithm for low-poly remeshing of 3D surfaces that runs fully in GPU. Since the input mesh is generally not well-organized, performing mesh simplification directly on the input mesh is liable to produce a low-poly mesh with a ...
Depth estimation using Stereo Light Field Camera✱
Light field imaging has emerged as a new modality, enabling to capture the angular and spatial information of a scene. This additional angular information is used to estimate the depth of a 3-D scene. The continuum of virtual view-points in light field ...
Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification✱
In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied ‘closed-set’ and ‘large-scale training ...
A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads✱
The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a ...
Design of a System and Method for Optimal selection of Tumor Slice using Linear Ultrasound Imaging for Histopathology
In excision biopsy, a tumor mass is surgically removed from the body. Subsequently, it is sliced at an appropriate location and investigated microscopically through a process called histopathology. Any bias in tumor slicing severely influences ...
Multi-view Learning with Two-stage Training of 2D CNNs for Tumor Sub-regions Segmentation from 3D Brain MRI Volumes
In this study, we have performed brain tumor segmentation on a publicly available BraTS 2019 dataset. The training data contains multi-modal 3D volumetric brain MRI data for 259 High Grade Glioma (HGG) cases and 76 Low Grade Glioma (LGG) cases. The ...
A Dataset and Model for Crossing Indian Roads
Roads in medium-sized Indian towns often have lots of traffic but no (or disregarded) traffic stops. This makes it hard for the blind to cross roads safely, because vision is crucial to determine when crossing is safe. Automatic and reliable image-...
Towards Realistic Underwater Dataset Generation and Color Restoration✱
Recovery of true color from underwater images is an ill-posed problem. This is because the wide-band attenuation coefficients for the RGB color channels depend on object range, reflectance, etc. which are difficult to model. Also, there is ...
A Novel Statistical High Density Salt-and-Pepper Noise Removal Algorithm for Brain Magnetic Resonance Images
Brain Magnetic Resonance Imaging (MRI) is a non-invasive technique that produces high quality images of the brain and is most suitable for analysis and diagnosis. However, these images can be soiled with noise during image acquisition or transmission. ...
Index Terms
- Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing
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Acceptance Rates
Year | Submitted | Accepted | Rate |
---|---|---|---|
ICVGIP '16 | 286 | 95 | 33% |
Overall | 286 | 95 | 33% |