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Found 18 papers, 5 papers with code
Camera Calibration through Geometric Constraints from Rotation and Projection Matrices
The process of camera calibration involves estimating the intrinsic and extrinsic parameters, which are essential for accurately performing tasks such as 3D reconstruction, object tracking and augmented reality.
Learning adjacency matrix for dynamic graph neural network
In recent work, [1] introduced the concept of using a Block Adjacency Matrix (BA) for the representation of spatio-temporal data.
Mitigating climate and health impact of small-scale kiln industry using multi-spectral classifier and deep learning
Industrial air pollution has a direct health impact and is a major contributor to climate change.
Multi-task Learning for Camera Calibration
As far as we are aware, our approach is the first one that uses an approach to multi-task learning that includes mathematical formulas in a framework for learning to estimate camera parameters to predict both the extrinsic and intrinsic parameters jointly.
Neural Network Pruning Through Constrained Reinforcement Learning
In this work, we propose a general methodology for pruning neural networks.
Camera Calibration through Camera Projection Loss
To the best of our knowledge, ours is the first method to jointly estimate both the intrinsic and extrinsic parameters via a multi-task learning methodology that combines analytical equations in learning framework for the estimation of camera parameters.
Survey of Image Based Graph Neural Networks
In this survey paper, we analyze image based graph neural networks and propose a three-step classification approach.
Spatio-temporal Crop Classification On Volumetric Data
Our combined strategy outperforms both classical as well as recent DCNN based methods in terms of classification accuracy by 2% while maintaining a minimum number of parameters and the lowest inference time.
Comprehensive Online Network Pruning via Learnable Scaling Factors
One of the major challenges in deploying deep neural network architectures is their size which has an adverse effect on their inference time and memory requirements.
Attention Neural Network for Trash Detection on Water Channels
This paper proposes a method for the detection of visible trash floating on the water surface of the canals in urban areas.
Cross-View Image Retrieval -- Ground to Aerial Image Retrieval through Deep Learning
To train, compare, and evaluate the performance of cross-view image retrieval, we present a new 6 class cross-view image dataset termed as CrossViewRet which comprises of images including freeway, mountain, palace, river, ship, and stadium with 700 high-resolution dual-view images for each class.
Early Disease Diagnosis for Rice Crop
Many existing techniques provide automatic estimation of crop damage due to various diseases.
Teacher-Class Network: A Neural Network Compression Mechanism
To reduce the overwhelming size of Deep Neural Networks (DNN) teacher-student methodology tries to transfer knowledge from a complex teacher network to a simple student network.
Patch-based Generative Adversarial Network Towards Retinal Vessel Segmentation
Retinal blood vessels are considered to be the reliable diagnostic biomarkers of ophthalmologic and diabetic retinopathy.
Neural Arithmetic Unit by reusing many small pre-trained networks
As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication, n-digit division, and cross product.
Tiny-Inception-ResNet-v2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia
This paper proposes to employ a Inception-ResNet inspired deep learning architecture called Tiny-Inception-ResNet-v2 to eliminate bonded labor by identifying brick kilns within "Brick-Kiln-Belt" of South Asia.
Unsupervised Deep Features for Remote Sensing Image Matching via Discriminator Network
Results demonstrate that our technique supersedes the state-of-the-art methods used for unsupervised image matching with mean average precision (mAP) of 81%, and image retrieval with an overall improvement in mAP score of about 12%.
Point cloud segmentation using hierarchical tree for architectural models
Recent developments in the 3D scanning technologies have made the generation of highly accurate 3D point clouds relatively easy but the segmentation of these point clouds remains a challenging area.
