arXiv subjects

Partha Sarathi Mukherjee

Publications and source records attributed to Partha Sarathi Mukherjee.

5 recordsLinked to original sources

arXiv2025

Estimation of Piecewise Continuous Regression Function in Finite Dimension using Oblique Regression Tree with Applications in Image Denoising

Decision trees are one of the most widely used nonparametric methods for regression and classification. In existing literature, decision tree-based methods have been used for estimating continuous functions or piecewise-constant functions. However, they are not flexible enough to estimate the complex shapes of jump location curves (JLCs) in two-dimensional regression functions. In this article, we explore the Oblique-axis Regression Tree (ORT) and propose a method to efficiently estimate piece-wise continuous functions in a general finite dimension with fixed design points. The central idea involves clustering the local pixel intensities by recursive tree partitioning and using the local leaf-only averaging for estimation of the regression function at a given pixel. The proposed method can preserve complex shapes of the JLCs well in a finite-dimensional regression function. Due to a different set of assumptions on the underlying regression function, the overall framework of the proofs is different from what is available in the literature on regression trees. Theoretical analysis and numerical results, particularly on image denoising, indicate that the proposed method effectively preserves complicated edge structures while efficiently removing noise from piecewise continuous regression surfaces.

stat.AP↗

arXiv2025

An Efficient Image Denoising Method Integrating Multi-resolution Local Clustering and Adaptive Smoothing

The importance of developing efficient image denoising methods is immense especially for modern applications such as image comparisons, image monitoring, medical image diagnostics, and so forth. Available methods in the vast literature on image denoising can address certain issues in image denoising, but no one single method can solve all such issues. For example, jump regression based methods can preserve linear edges well, but cannot preserve many other fine details of an image. On the other hand, local clustering based methods can preserve fine edge structures, but cannot perform well in presence of heavy noise. The proposed method uses various shapes and sizes of local neighborhood based on local information, and integrates this adaptive approach with the local clustering based smoothing. Theoretical justifications and numerical studies show that the proposed method indeed performs better than these two individual methods and outperforms many other state-of-the-art techniques as well. Such performance demonstrates vast potential of the applicability of the proposed method in many modern-day applications.

stat.AP↗

arXiv2025

Monitoring of Drift Patterns in Image Data

Sequential monitoring of images has broad applications across various domains, including climate science, ecosystem monitoring, medical diagnostics, and so forth. In many such applications, images acquired over time exhibit gradual changes, referred to as drifts, which pose significant challenges for monitoring. Rather than detecting only abrupt step changes, it is crucial to monitor and characterize these drift patterns. Despite its practical importance, the problem of drift monitoring in image sequences has received limited attention. This paper addresses this gap by proposing a novel drift monitoring method based on an oblique-axis regression tree. It is particularly effective for monitoring drift patterns in the jump location curves present in the image intensity functions. By leveraging a decision tree framework, the method captures discontinuities both in spatial image intensity and temporal progression. A key advantage of this method lies in its flexibility: in the absence of drift, it remains capable of detecting abrupt step changes. Theoretical properties and numerical performance in diverse types of simulation settings indicate its broad applicability.

stat.AP↗

arXiv2019

Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian

Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance, autonomous cars, etc. Also, a significant trend of latest research studies in related problem areas is the use of sophisticated Deep Learning based approaches to improve the benchmark performance on various standard datasets. A trade-off between the speed (number of video frames processed per second) and detection accuracy has often been reported in the existing literature. In this article, we present a new but simple deep learning based strategy for pedestrian detection that improves this trade-off. Since training of similar models using publicly available sample datasets failed to improve the detection performance to some significant extent, particularly for the instances of pedestrians of smaller sizes, we have developed a new sample dataset consisting of more than 80K annotated pedestrian figures in videos recorded under varying traffic conditions. Performance of the proposed model on the test samples of the new dataset and two other existing datasets, namely Caltech Pedestrian Dataset (CPD) and CityPerson Dataset (CD) have been obtained. Our proposed system shows nearly 16\% improvement over the existing state-of-the-art result.

cs.CV↗

arXiv2016

Supporting novel biomedical research via multilayer collaboration networks

The value of research containing novel combinations of molecules can be seen in many innovative and award-winning research programs. Despite calls to use innovative approaches to address common diseases, an increasing majority of research funding goes toward "safe" incremental research. Counteracting this trend by nurturing novel and potentially transformative scientific research is challenging, it must be supported in competition with established research programs. Therefore, we propose a tool that helps to resolve the tension between safe but fundable research vs. high-risk but potentially transformational research. It does this by identifying hidden overlapping interest around novel molecular research topics. Specifically, it identifies paths of molecular interactions that connect research topics and hypotheses that would not typically be associated, as the basis for scientific collaboration. Because these collaborations are related to the scientists' present trajectory, they are low risk and can be initiated rapidly. Unlike most incremental steps, these collaborations have the potential for leaps in understanding, as they reposition research for novel disease applications. We demonstrate the use of this tool to identify scientists who could contribute to understanding the cellular role of genes with novel associations with Alzheimer's disease, which have not been thoroughly characterized, in part due to the funding emphasis on established research.

cs.SI↗