The proceedings contain 49 papers. The topics discussed include: enhancing face quality assessment through age and expression analysis;IDA-UIE: an iterative framework for deep network based degradation aware underwate...
ISBN:
(纸本)9798400710759
The proceedings contain 49 papers. The topics discussed include: enhancing face quality assessment through age and expression analysis;IDA-UIE: an iterative framework for deep network based degradation aware underwater image enhancement;an improved framework for precision grading of renal cell carcinoma using histopathological images;lost in context: the influence of context on feature attribution methods for object recognition;vision-language modeling with regularized spatial transformer networks for all weather crosswind landing of aircraft;on the efficacy of guidance tasks in panoptic segmentation;enhancing generalization ability in deepfake detection via continual learning;and a computervision framework on biomechanical analysis of jump landings.
The proceedings contain 39 papers. The topics discussed include: single view homography estimation for an inclined textured planar surface: overcoming the inverse and ill-posed challenge!;MMAG: mutually motivated atte...
ISBN:
(纸本)9798400716256
The proceedings contain 39 papers. The topics discussed include: single view homography estimation for an inclined textured planar surface: overcoming the inverse and ill-posed challenge!;MMAG: mutually motivated attention gates for simultaneous extraction of contextual and spatial information from a monocular image;automatic assessment of communication skill in real-world job interviews: a comparative study using deep learning and domain adaptation;an efficient motor imagery classification framework using sparse brain connectivity and class-consistent dictionary learning from electroencephalogram signals;mandala as computational art: vectorization and beyond;degradation aware multi-scale approach to no reference image quality assessment;dual stage semantic information based generative adversarial network for image super-resolution;knowledge distillation with ensemble calibration;and a novel framework for robust fingerprint representations using deep convolution network with attention mechanism.
the proceedings contain 59 papers. the topics discussed include: interpreting intrinsic image decomposition using concept activations;quaternion factorized simulated exposure fusion;learning from multiple datasets for...
ISBN:
(纸本)9781450398237
the proceedings contain 59 papers. the topics discussed include: interpreting intrinsic image decomposition using concept activations;quaternion factorized simulated exposure fusion;learning from multiple datasets for recognizing human actions;topological shape matching using multi-dimensional Reeb graphs;convolutional ensembling based few-shot defect detection technique;performance, trust, or both? COVID-19 diagnosis and prognosis using deep ensemble transfer learning on x-ray images;Alzheimer’s severity classification using transfer learning and residual separable convolution network;detecting coronavirus (COVID–19) disease cues from chest radiography images;posture guided human action recognition for fitness applications;towards robust handwritten text recognition with on-the-fly user participation;low resource degraded quality document image binarization – domain adaptation is the way;a globally-connected and trainable hierarchical fine-attention generative adversarial network based adversarial defense;and overcoming label noise for source-free unsupervised video domain adaptation.
The proceedings contain 49 papers. The topics discussed include: teaching GANs to sketch in vector format;iterative gradient encoding network using feature co-occurrence loss for single image reflection removal;MSDNet...
ISBN:
(纸本)9781450391276
The proceedings contain 49 papers. The topics discussed include: teaching GANs to sketch in vector format;iterative gradient encoding network using feature co-occurrence loss for single image reflection removal;MSDNet: a novel multi-stage progressive image dehazing network;monocular multi-layer layout estimation for warehouse racks;cross-view kernel similarity metric learning using pairwise constraints for person re-identification;abnormality detection and classification of macular diseases from optical coherence tomography images: using feature space comparison;a novel unsupervised thresholding technique for Landsat image change detection;robust brain state decoding using bidirectional long short term memory networks in functional MRI;NTU-X: an enhanced large-scale dataset for improving pose-based recognition of subtle human actions;and interpretive self-supervised pre-training: boosting performance on visual medical data.
One of the major challenges in no-reference (NR) image quality assessment (IQA) is the ability to generalize to diverse quality assessment applications. Recently, multi-modal vision-language models are found to be ver...
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ISBN:
(纸本)9798400710759
One of the major challenges in no-reference (NR) image quality assessment (IQA) is the ability to generalize to diverse quality assessment applications. Recently, multi-modal vision-language models are found to be very promising in this direction. They are beginning to form a part of several state of the art NR IQA methods. On the other hand, multi-modal large language models (LLMs) are increasingly being studied for various computervision applications including IQA. In this work, we perform a thorough study of the ability of multi-modal LLMs for NR IQA by training some of its components and testing for its generalizability. In particular, we keep the LLM frozen and learn parameters corresponding to the querying transformer, LLM prompt and some layers that process the embedding output by the LLM. We observe that some of these components offer a generalization performance far superior to any existing NR IQA algorithm.
High-intensity activities in sports like basketball can result in fatigue without proper recovery. This study introduces a collaborative framework that leverages computervision (CV) and Machine Learning for evaluatin...
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ISBN:
(纸本)9798400710759
High-intensity activities in sports like basketball can result in fatigue without proper recovery. This study introduces a collaborative framework that leverages computervision (CV) and Machine Learning for evaluating jump landings and predicting athletic readiness by modelling Countermovement Jumps (CMJs) biomechanical aspects. Seventeen female collegiate basketball athletes of Sacred Heart University (SHU), CT, USA, participated in weekly CMJs over a 26-week season. Through CV-driven semantic analysis of videos, the framework identifies the crucial initial contact and maximum flexion point during jump landings and extracts kinetic and kinematic features of the lower extremities. Next, an inferential analysis is conducted to understand the relationship between these features and the CMJ-driven reactive strength indexmodified (RSImod) score, which measures fatigue and athletic readiness. An XGBoost regressor, trained on the past week's data, then predicted the RSImod score for the following week, which resulted in an MSE of 0.020 and an R-2 of 0.892. Using SHapley Additive exPlanations (SHAP), the framework offers interpretable feedback, aiding coaches in creating personalised training programs and optimising athletic performance while minimising injury risks.
We explore the applicability of spectrograms in Deep learning applications and in guiding creative decisions. To this end, we propose Spectrogrand, a novel spectrogram-driven end-to-end Generative AI pipeline creating...
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ISBN:
(纸本)9798400710759
We explore the applicability of spectrograms in Deep learning applications and in guiding creative decisions. To this end, we propose Spectrogrand, a novel spectrogram-driven end-to-end Generative AI pipeline creating interesting audiovisuals from text prompts and incorporating lightweight computational creativity metrics. This process involves selecting a music piece to underpin the audiovisual, generating an album cover image for the music, and performing neural style transfer on spectrogram chunks to generate the frames for the audiovisual. To democratise the benefits of this pipeline, we open-source the tool, computational creativity metrics, and associated data (1).
Thoracic trauma often results in rib fractures, which demand swift and accurate diagnosis for effective treatment. However, detecting these fractures on rib CT scans poses considerable challenges, involving the analys...
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ISBN:
(纸本)9798400710759
Thoracic trauma often results in rib fractures, which demand swift and accurate diagnosis for effective treatment. However, detecting these fractures on rib CT scans poses considerable challenges, involving the analysis of many image slices in sequence. Despite notable advancements in algorithms for automated fracture segmentation, the persisting challenges stem from the diverse shapes and sizes of these fractures. To address these issues, this study introduces a sophisticated deep-learning model with an auxiliary classification task designed to enhance the accuracy of rib fracture segmentation. The auxiliary classification task is crucial in distinguishing between fractured ribs and negative regions, encompassing non-fractured ribs and surrounding tissues, from the patches obtained from CT scans. By leveraging this auxiliary task, the model aims to improve feature representation at the bottleneck layer by highlighting the regions of interest. Experimental results on the RibFrac dataset demonstrate significant improvement in segmentation performance.
This paper presents a novel approach to computational art focusing on mandalas-an iconic heritage of indian art that has proliferated significantly in recent times. Our innovative software allows users to input a hand...
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ISBN:
(纸本)9798400710759
This paper presents a novel approach to computational art focusing on mandalas-an iconic heritage of indian art that has proliferated significantly in recent times. Our innovative software allows users to input a handcrafted mandala and select specific motifs for error rectification. The rectification leverages vector information for geometric discretization alongside a simple GCD rule, adhering to traditional mandala principles. The rectified image, being in vector form, allows various real-time operations in the vector space, such as insertion, deletion, or modification of motifs or layers, facilitating enhancements, enlargements, or creation of new compositions. We demonstrate the software's merit and versatility through various examples, highlighting its potential for special applications such as digital artistry with mandalas, including teaching and training. This work not only advances the field of computational art but also promises to preserve and enhance the rich tradition of mandala art through modern technology.
The proceedings contain 18 papers. The special focus in this conference is on computervision, Pattern Recognition, imageprocessing, and graphics. The topics include: RGB to Chlorophyll Fluorescence image Reconstruct...
ISBN:
(纸本)9789819752119
The proceedings contain 18 papers. The special focus in this conference is on computervision, Pattern Recognition, imageprocessing, and graphics. The topics include: RGB to Chlorophyll Fluorescence image Reconstruction of Maize for Plant Phenotyping;novel Unsupervised Disease Segmentation Framework Based on Clustering;YOLOv5-MasselNet: In-Field Maize Tassel Detection and Density Estimation Using YOLOv5;MuSiC V1.0: A Software Solution for Automated Mustard Silique Count Using YOLOv5;taPaSe: Tanjore Paddy Seed Dataset;processing 3D Point Clouds for High-Throughput Plant Phenotyping;a Deep Learning-Driven Contactless Finger Recognition System;deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?;unlocking Communication: Exploring Hand Gesture-Based Interaction in a Code-Mixed Conversational Speech-to-indian-Sign-Language Translator;a Touchless Palm-Photo Recognition System for Mobile and Handheld Devices;detection of Palmprint from Contactless Smartphone-Based Video Hand Dataset;improved Localization of Knuckle Regions for Contactless Acquisition;NCVPRIPG 2023 Summer Challenge on Writer Verification;C4MTS: Challenge on Categorizing Missing Traffic Signs from Contextual Cues;dePondFi’23 Challenge Summary;a Challenge on 3D Reconstruction and Restoration of indian Heritage from Partial and Noisy Scans.
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