Patents Granted

System and Method for Detecting Object in Adverse Atmosphere by Restoring Degraded Image in Deep Convolutional Layer

New Indian Patent Application No: 202131002651

Status: Published Old PDF New PDF Grant Certificate

Abstract: The present invention discloses a system and method for detecting objects in real-time adverse weather-degraded scenes. A single-stage CNN architecture is adopted, namely, AWDRDNet for detecting objects more accurately in adverse weather-degraded realistic scenes. The present invention relates to a feed-forward deeper convolutional layer comprising a plurality of convolution blocks (B1,..,BK,..,BN) producing better quality of restoration images (RI1,..,RIK,..,RIN); wherein receptive field plays an important role in analyzing local features over degraded scenes. Another key feature of the proposed invention is the clipping of pre-defined multi-scale anchor boxes per cell to a restorated de-convolutional feature map (DC) only at the top of the network, which allows to efficiently reduce time-consumption. In terms of detection accuracy (recall-precision graph and mAP), the results of the reference dataset demonstrates the optimal performance of the proposed model and reveals the performance accuracy in low-light or rainy conditions to be higher than that in dusty or foggy conditions.

Type: Application

Filed: January 20, 2021

Publication Date: July 22, 2022

Date of Grant July 01, 2024

Applicants: Anu Singha (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University), Sourav Dey Roy (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University), Dr. Mrinal Kanti Bhowmik (Associate Professor, Department of Computer Science & Engineering, Tripura University)

A system and method for segmenting suspicious hyperthermic regions from breast thermograms

New Indian Patent Application No: 202031027352

Status: Published Old PDF New PDF Grant Certificate

Abstract: The Appearance of suspicious hyperthermic regions (SHRs) in breast thermograms is the single most marker of breast abnormality. Hence, accurate segmentation and analysis of SHRs are very crucial for grading the degree of severity in breast thermograms. A novel breast abnormality grading approach namely Morphology Model of Suspicious Hyperthermic Regions (MMSHRs) has been proposed here. The proposed MMSHRs method first segments the SHRs and then, the morphology of the SHRs has been analyzed to grade the thermograms according to their degree of severity. The experimental results show that the proposed segmentation method can extract the SHRs more accurately with higher average accuracy rate compared to the other state-of-the-art methods. The segmentation of SHRs is followed by the extraction of morphological features of SHRs, which categorizes the abnormal thermograms into mild abnormal and severely abnormal with classification accuracy of 91% based on the degree of severity present in the thermograms.

Type: Application

Filed: June 27, 2020

Publication Date: December 31, 2021

Date of Grant: November 28, 2024

Applicants: Usha Rani Gogoi (DST - INSPIRE Fellow, Department of Computer Science and Engineering, Tripura University), Dr. Mrinal Kanti Bhowmik (Associate Professor, Department of Computer Science & Engineering, Tripura University)

Patents Filed by Dr. Mrinal Kanti Bhowmik

System and Method for Classification of Multiclass Scenes in Adversarial Weather using a Convolution Neural Network

New Indian Patent Application No: 202231068251

Status: Published Old PDF New PDF

Abstract: The present invention discloses a system and method for classification of multiclass scenes in adversarial weather using a convolution neural network. A CNN architecture is adopted, namely, AWDMC-Net for classifying scenes more accurately in adverse weather-degraded realistic scenes. The present invention relates to a one or more convolutional layer (Conv1…Convn) comprising a plurality of convolutional block (CB1, CB2, …. CBN). Another key feature of the proposed invention is adopting different combinations of skip connections in building blocks of CNN adaptively pruning the least convolutional kernels (3). “Entropy Guided Mean-l1 Norm” adaptively evaluate the convolutional kernels using the filters and their corresponding output feature maps (FMn). The prediction performance that represents six atmospheric/ weather conditions, namely, fog, dust, rain, haze, poor illumination, and clear day conditions. The AWDMC-Net reduces the time consumption for atmospheric/ weather classification tasks and meets the requirements of practical applications in real-world scenarios.

Type: Application

Filed: November 28, 2022

Published Date: May 31, 2024

Applicants: Sourav Dey Roy (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University), Anu Singha (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University), Priya Saha (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University) & Dr. Mrinal Kanti Bhowmik (Associate Professor, Department of Computer Science & Engineering, Tripura University)

A System and a Method using GSNET for Presence of Gun in Complex Scenes

New Indian Patent Application No: 202231068259

Status: Published Old PDF New PDF

Abstract: A system and a method using Geographical and Semantic spatial-temporal Network (GSNet) for detecting presence of gun in complex scenes. The Geographical and Semantic spatial-temporal Network (GSNet) comprises a plurality of dense blocks to receive the at least one input image and connected to an at least one of small attention network. Each one of the small attention networks is used for both spatial attention and channel attention. An at least one of transition layer having one convolution layer to reduce the size of output feature map and the convolution layer imposes weight to a local patch which are relevant to the scene. The output feature map is inputted to an at least one of enhancement classification layer and output (R) to a softmax classifier to provide two output which displays the presence of gun.

Type: Application

Filed: November 28, 2022

Publication Date: May 31, 2024

Applicants: Rajib Debnath (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University), Kakali Das (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University), Priya Saha (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University) & Dr. Mrinal Kanti Bhowmik (Associate Professor, Department of Computer Science & Engineering, Tripura University)

A Method and A System For Segmenting Suspicious Hot Spot Regions In A Plurality Of Joints

New Indian Patent Application No: 202431062925

Status: Filed

Abstract: A method for segmenting suspicious hot spot regions in a plurality of joints is disclosed. The method includes training an encoder decoder knee inflammation model (a Knee Inflammation Segmentation Network (f KNEE )) through source data having a plurality of knee IR images, a ground truth associated with the plurality of knee IR images to generate a plurality of optimized parameters. The method includes training a new target network based on the plurality of optimized parameters, and a Hot spot Attention Module (HAM) using target data having a plurality of hand IR images and a ground truth associated with each hand IR image amongst the plurality of hand IR images. The method includes producing a first feature map associated with the source data and a second feature map associated the target data based on the source data and the target data. The method includes processing, by a Hot spot Attention Module (HAM) in the new target network, the first feature map and the second feature map to highlight the suspicious hot spot regions from the source data and the target data. The method includes performing an entropy-based KL divergence, to reduce a domain shift between the source data and the target data to reduce a presence of the suspicious hot spots in the source data and the target data. The method includes extracting, by a Domain-Adapted Guided Hot spot Segmentation Network (DAHS-Net), the suspicious hot spot regions segmented from the source data and the target data.

Type: Application

Filed: August 20, 2024

Applicants: Puja Das (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University), Sourav Dey Roy (Research Associate, DBT Funded Project, Department of Computer Science and Engineering, Tripura University), & Dr. Mrinal Kanti Bhowmik (Associate Professor, Department of Computer Science & Engineering, Tripura University)

A Method and a System for Breast Abnormality Detection

New Indian Patent Application No: 202431055003

Status: Filed

Abstract: A method for performing a breast abnormality detection is disclosed. The method includes obtaining, by a first plurality of base learner networks, a plurality of common cancer abnormality features from a plurality of source images. The method includes initializing a random weight to each of the first plurality of base learner networks. The method includes updating a plurality of meta parameter weights from the first plurality of base learner networks based on the plurality of random weights, the plurality of tasks, and an inner learning rate. The method includes initializing a second plurality of base learner networks with the plurality of meta parameter weights and a plurality of target images of a breast and a plurality of labels. The method also includes processing the plurality of target images with the plurality of base learner networks upon initialization to detect the breast abnormality in the plurality of target images.

Type: Application

Filed: July 18, 2024

Applicants: Anindita Mohanta (Ph.D Scholar, Department of Computer Science and Engineering, Tripura University), Niharika Nath (Professor, Department of Biological and Chemical Sciences, New York Institute of Technology), Sourav Dey Roy (Research Associate, DBT Funded Project, Department of Computer Science and Engineering, Tripura University), & Dr. Mrinal Kanti Bhowmik (Associate Professor, Department of Computer Science & Engineering, Tripura University)