Statistical/Geometric Techniques for Object Representation and Recognition

Object modeling and recognition are key areas of research in computer vision and graphics with wide range of applications. Though research in these areas is not new, traditionally most of it has focused on analyzing problems under controlled environments. The challenges posed by real life applications demand for more general and robust solutions. The wide variety of objects with large intra-class variability makes the task very challenging. The difficulty in modeling and matching objects also vary depending on the input modality. In addition, the easy availability of sensors and storage have resulted in tremendous increase in the amount of data that needs to be processed which requires efficient algorithms suitable for large-size databases. In this dissertation, we address some of the challenges involved in modeling and matching of objects in realistic scenarios. Object matching in images require accounting for large variability in the appearance due to changes in illumination and view point. Any real world object is characterized by its underlying shape and albedo, which unlike the image intensity are insensitive to changes in illumination conditions. We propose a stochastic filtering framework for estimating object albedo from a single intensity image by formulating the albedo estimation as an image estimation problem. We also show how this albedo estimate can be used for illumination insensitive object matching and for more accurate shape recovery from a single image using standard shape from shading formulation. We start with the simpler problem where the pose of the object is known and only the illumination varies. We then extend the proposed approach to handle unknown pose in addition to illumination variations. We also use the estimated albedo maps for another important application, which is recognizing faces across age progression. Many approaches which address the problem of modeling and recognizing objects from images assume that the underlying objects are of diffused texture. But most real world objects exhibit a combination of diffused and specular properties. We propose an approach for separating the diffused and specular reflectance from a given color image so that the algorithms proposed for objects of diffused texture become applicable to a much wider range of real world objects. Representing and matching the 2D and 3D geometry of objects is also an integral part of object matching with applications in gesture recognition, activity classification, trademark and logo recognition, etc. The challenge in matching 2D/3D shapes lies in accounting for the different rigid and non-rigid deformations, large intra-class variability, noise and outliers. In addition, since shapes are usually represented as a collection of landmark points, the shape matching algorithm also has to deal with the challenges of missing or unknown correspondence across these data points. We propose an efficient shape indexing approach where the different feature vectors representing the shape are mapped to a hash table. For a query shape, we show how the similar shapes in the database can be efficiently retrieved without the need for establishing correspondence making the algorithm extremely fast and scalable. We also propose an approach for matching and registration of 3D point cloud data across unknown or missing correspondence using an implicit surface representation. Finally, we discuss possible future directions of this research

Fungal endophytic species Fusariumannulatum and Fusariumsolani: identification, molecular characterization, and study of plant growth promotion properties

Research on endophytic fungi has gained significant interest due to their potential to enhance plant growth directly by producingphytohormones, solubilizing macronutrients, fixing nitrogen, or indirectly inhibiting phytopathogens growth by producing ammonia, siderophore, hydrogen cyanide, or extracellular enzymes, thereby acting as biocontrol agents. The present study aimed to isolate fungal endophytes from Alternantheraphiloxeroidesand evaluate their plant growth promotion and antimicrobial activity. In total, nine fungal endophytic strains were isolated from different parts of A. philoxeroides such as leaves, roots, and stems. The results demonstrate that the strains MEFAphS1 and MEFAphR3 exhibited positive plant growth promotion properties,including phosphate solubilization, and IAA (Indoleacetic acid) production, and ammonia production. The IAA production was highest for MEFAphS1, with a concentration of 46.635±1.04 µg/mL, while MEFAphR3 displayed the highest ammonia production (0.903±0.01 µg/mL). The phosphate solubilization index (PSI) is the maximum for MEFAphS1 (1.5±0.10). MEFAphS1 also exhibited antibacterial activity against Vibrio vulnificus, Streptococcus pneumoniae, and V.parahaemolyticus,with the most substantial inhibition zone observed against V.vulnificus(28±1 mm). In contrast, MEFAphR3 showed an inhibition zone of 8±1.53 mm against V. parahaemolyticus. Molecular identification revealed the identity of the isolates MEFAphS1 and MEFAphR3 as Fusariumsolaniand F.annulatum. These results thus confirm the possible applications of the fungal endophytes as plant biofertilizers and bio-enhancers to increase crop productivity

Robust Feature Learning and Global Variance-Driven Classifier Alignment for Long-Tail Class Incremental Learning

This paper introduces a two-stage framework designed to enhance long-tail class incremental learning, enabling the model to progressively learn new classes, while mitigating catastrophic forgetting in the context of long-tailed data distributions. Addressing the challenge posed by the under-representation of tail classes in long-tail class incremental learning, our approach achieves classifier alignment by leveraging global variance as an informative measure and class prototypes in the second stage. This process effectively captures class properties and eliminates the need for data balancing or additional layer tuning. Alongside traditional class incremental learning losses in the first stage, the proposed approach incorporates mixup classes to learn robust feature representations, ensuring smoother boundaries. The proposed framework can seamlessly integrate as a module with any class incremental learning method to effectively handle long-tail class incremental learning scenarios. Extensive experimentation on the CIFAR-100 and ImageNet-Subset datasets validates the approach's efficacy, showcasing its superiority over state-of-the-art techniques across various long-tail CIL settings.Comment: Accepted in WACV 202