Generative AI enables realistic data generation, making it essential for tasks such as text, image, and video synthesis. In robotics, predicting unknown map regions as an image generation problem is key to improving exploration and navigation. This study uniquely compares the performance of three deep generative models—Conditional Variational Autoencoder (CVAE), Vision Transformer (ViT), and Conditional Generative Adversarial Network (CGAN)—for map completion. Unlike prior studies focusing on individual models, this work provides a direct performance comparison for global map prediction. A custom dataset, derived from HouseExpo and enriched with structured time-series exploration data in Gazebo, was developed for this purpose. Experimental results demonstrate that CGAN achieves superior performance in completing unknown regions, making it more effective for robotic exploration. Additionally, we introduce a novel dataset generation methodology leveraging ROS, Gazebo, and Voronoi-based mapping. Future research will focus on scaling the dataset and integrating these models into exploration systems, further advancing generative AI applications in robotics.

Diabetic Retinopathy caused by Diabetes Mellitus is a major vision threatening condition across the global population. Early detection and grading of diabetic retinopathy are pivotal to avoiding the associated vision impairment, and retinal image analysis serves as an effective method for the screening process. Analysing retinal images manually to detect diabetic retinopathy and grade them is a time-consuming pro cess that necessitates the involvement of experts to perform the classification. Computer-aided diagnosis of diabetic retinopathy through retinal image analysis is an effective tool to reduce the time involved in the screening process. This study proposed a convolutional neural network based Selective Feature Map Fusion architecture that leverages the fused feature maps of ResNet50 and EfficientNetV2L networks for the detection and grading of diabetic retinopathy. Feature set of ResNet50 and EfficientNetV2L captures different aspects of the underlying lesion distribution, resulting in a more comprehensive representation of the features. This approach helps the model generalize better to unseen data by incorporating diverse data aspects, thereby preventing overfitting to a specific feature set and developing a model that is more robust to noise. Selective feature fusion helps to reduce the computational overhead of the the feature fusion computation. Comparative analysis against the ResNet50 and EfficientNetV2L models individually revealed the superior performance of the fused model in both detection and grading of diabetic retinopathy. The proposed model attained an excellent level of accuracy of 95.2% in diabetic retinopathy detection with a sensitivity of 95.2% and a specificity of 95.2%, when tested on the IDRiD dataset. The model achieved a classification accuracy of 92% for diabetic retinopathy grading with a sensitivity of 88%, a specificity of 98% and an F1-score of 0.89. Moreover, the fused framework attained superior performance when compared against existing methodologies on the IDRiD dataset. The developed model exhibited robust performance also in the DeepDRiD dataset, and it can be employed successfully in the screening processes for the diagnosis and grading of diabetic retinopathy.

In past few decades, handwritten verification system has got a lot of attention, but that's still a continuing process. Signature verification techniques are being used to determine whether such a signature is real or forged i.e., produced by an impostor. Many enhancements have certainly been suggested in the literature a most notable of which being use of Deep Learning algorithms to learn image attributes from signature images. As an explorative study, we trained models based on the principle of transfer learning using three state-of-the-art CNNs, namely VGG-16, VGG-19 and Alexnet as feature extractors and classifiers. The CEDAR Dataset, the ICDAR 2011 Dataset and the combination of both the CEDAR and the ICDAR 2011 Datasets are the three datasets considered for the proposed methodology. The models showed performance, with the highest precision reaching up to 100%. Among the three models, the Alexnet model exhibited the highest accuracy and lowest training cost.

Individuals afflicted with color vision deficiency (CVD) often face obstacles in effectively navigating and engaging with their surroundings due to challenges in accurately discerning colors. Such limitations can hinder a range of daily activities, compelling these individuals to rely on external assistance for color-centric tasks, potentially curtailing their autonomy and inclusiveness. In response to these impediments, our study centers on the design and implementation of a machine learning-driven color adaptation framework. Utilizing the TensorFlow and Keras libraries, this system harnesses sophisticated machine learning methodologies to detect and modify colors within visual content, thereby augmenting perceptibility for those with CVD. Our principal aim is to equip individuals with CVD with a pragmatic tool that enhances color clarity in images, facilitating self-evaluation of their visual condition. This innovation targets to bolster navigational capabilities, diminish reliance on external assistance for colororiented activities, and advance inclusivity via technological advancements. Furthermore, our investigation underscores the precision and dependability of the machine learning algorithms through meticulous testing and validation protocols, guaranteeing robust performance across diverse contexts and image categories. A user-centric and easily navigable graphical user interface (GUI) is emphasized to accommodate users with varying technical proficiencies. Beyond the immediate technological impact, our research aspires to amplify awareness and deepen comprehension of color vision deficiency within the wider populace, thereby fostering a society characterized by enhanced equity and accessibility for all.

These days consciousness about public safety increases and CCTV cameras are installed at almost all public places. But generally automatic smart surveillance systems are not available. In this manuscript, emphasis is given to detect and classify abnormal events in surveillance video especially in crowd environments. Abnormal event detection is a challenging task because the definition of abnormality is subjective. A normal event in one situation can be considered an abnormal event in another case. In the surveillance video with a dense crowd, automatic anomaly detection becomes very difficult because of clutter and severe occlusion. This manuscript represents CNN (Convolutional Neural Network) and CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) based approaches for detection and classification of abnormal events. The CNN architecture is developed from scratch and can be used for spatial domains. LSTM architecture is developed for the temporal domain. Feature sequences are generated using CNN model and given as input to LSTM model. Experiments are carried out using five different publicly available benchmark datasets. The performance is measured by accuracy and area under the ROC (receiver operating characteristic) curve (AUC). CNN-LSTM approach works better than only CNN.

Robot perception involves recognizing the surrounding environment, particularly in indoor spaces like kitchens, classrooms, and dining areas. This recognition is crucial for tasks such as object identification. Objects in indoor environments can be categorized into "things," with fixed and countable shapes (e.g., tables, chairs), and "stuff," which lack a fixed shape and cannot be counted (e.g., sky, walls). Object detection and instance segmentation methods excel in identifying "things," with instance segmentation providing more detailed representations than object detection. However, semantic segmentation can identify both "things" and "stuff" but lacks segmentation at the object level. Panoptic segmentation, a fusion of both methods, offers comprehensive object and stuff identification and object-level segmentation. Considerations need to be made regarding the variabilities of room conditions in contrast to implementing panoptic segmentation indoors. High or low contrast in the room potentially reduces the clarity of the shape of an object, thus affecting the segmentation results of that object. We experimented with how contrast varieties impact the panoptic segmentation performance using the MaskDINO model, the first on the panoptic quality (PQ) leaderboard. We then improved the model generalization on the various contrasts by re-optimizing it using a contrast-augmented dataset.

Non-destructive and online defect detection on seals is increasingly being deployed in packaging processes, especially for food and pharmaceutical products. It is a key control step in these processes as it curtails the costs of these defects. To address this cause, this paper highlights a combination of two cost-effective methods, namely machine learning algorithms and infrared thermography. Expectations can, however, be restricted when the training data is small, unbalanced, and subject to optical imperfections. This paper proposes a classification method that tackles these limitations. Its accuracy exceeds 93% with two small training sets, including 2.5 to 10 times fewer negatives. Its algorithm has a low computational cost compared to deep learning approaches, and does not need any prior statistical studies on defects characterization.

Color Visual Secret Sharing (VSS) is an essential form of VSS. It is so because nowadays, most people like to share visual data as a color image. There are color VSS schemes capable of dealing with halftone color images or color images with selected colors, and some dealing with natural color images, which generate low quality of recovered secret. The proposed scheme deals with a color image in the RGB domain and generates gray shares for color images using color to gray and back through compression. These shares are encrypted into an innocent-looking gray cover image using a Discrete Cosine Transform (DCT) to make meaningful shares. Reconstruct a high-quality color image through the gray shares extracted from an innocent-looking gray cover image. Thus, using lower bandwidth for transmission and less storage.

The key events extraction from a video for the bestrepresentation of its contents is known as video summarization.In this study, the game of cricket is specifically consideredfor extracting important events such as boundaries, sixes andwickets. The cricket video highlight generation frameworksrequire extensive key event identification. These key events canbe identified by extracting the audio, visual and textual featuresfrom any cricket video.The prediction accuracy of the cricketvideo summarization mainly depends on the game rules, player’sform, their skill, and different natural conditions. This paperprovides a complete survey of latest research in cricket videosummarization methods. It includes the quantitative evaluationof the outcomes of the existing frameworks. This extensive reviewhighly recommended developing deep learning-assisted videosummarization approaches for cricket video due to their morerepresentative feature extraction and classification capabilitythan the conventional edge, texture features, and classifiers. Thescope of this analysis also includes future visions and researchopportunities in cricket highlight generation.

Blood vessels of retina contain information about many severe diseases like glaucoma, hypertension, obesity, diabetes etc. Health professionals use this information to detect and diagnose these diseases. Therefore, it is necessary to segment retinal blood vessels. Quality of retinal image directly affects the accuracy of segmentation. Therefore, quality of image must be as good as possible. Many researchers have proposed various methods to segment retinal blood vessels. Most of the researchers have focused only on segmentation process and paid less attention on pre processing of image even though pre processing plays vital role in segmentation. The proposed method introduces a novel method called multi-scale switching morphological (MSMO) for pre processing and Fréchet match filter for retinal vessel segmentation. We have experimentally tested and verified the proposed method on DRIVE, STARE and HRF data sets. Obtained outcome demonstrate that performance of the proposed method has improved substantially. The cause of improved performance is the better pre processing and segmentation methods.