Deepa Raj

Work place: Babasaheb Bhimrao Ambedkar University Lucknow (A Central University)



Research Interests: Image Processing, Image Manipulation, Image Compression, Computer Vision, Computer systems and computational processes, Software Engineering, Computational Science and Engineering


Dr. Deepa Raj, Working as associate professor in the Department of Computer Science at Babasaheb Bhimrao Ambedkar University Lucknow (A Central University). She did her post-Graduation from J.K Institute of applied physics and technology, Allahabad University and Ph.D. from Babasaheb Bhimrao Ambedkar University Lucknow in the field of software Engineering. Her field of interest is Software Engineering, Digital Image Processing, Computer Graphics and Algorithm analysis. She has attended lots of National and International conference and numbers of research papers published in her field.


Author Articles
Recent Object Detection Techniques: A Survey

By Diwakar Deepa Raj

DOI:, Pub. Date: 8 Apr. 2022

In the field of computer vision, object detection is the fundamental most widely used and challenging problem. Last several decades, great effort has been made by computer scientists or researchers to handle the object detection problem. Object detection is basically, used for detecting the object from image/video. At the beginning of the 21st century, a lot of work has been done in this field such as HOG, SIFT, SURF etc. are performing well but can’t be efficiently used for Real-time detection with speed and accuracy. Furthermore, in the deep learning era Convolution Neural Network made a rapid change and leads to a new pathway and a lot of excellent work has been done till dated such as region-based convolution network YOLO, SSD, retina NET etc. In this survey paper, lots of research papers were reviewed based on popular traditional object detection methods and current trending deep learning-based methods and displayed challenges, limitations, methodologies used to detect the object and also directions for future research.

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An Analysis of Fuzzy and Spatial Methods for Edge Detection

By Pushpa Mamoria Deepa Raj

DOI:, Pub. Date: 8 Nov. 2016

An image segmentation is an area in which image is subdivided into sub-regions for extracting characteristics of images which will help to analysis in various applications. For getting accuracy sharp changes of intensity is an important issue which is known as edge detection. In this paper various spatial edge detection methods and fuzzy based edge detection method has described and spatial edge detection methods and fuzzy if-then-else are compared to know which method will be more suitable to find edges for the enhancement of images.

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Comparison of Mamdani Fuzzy Inference System for Multiple Membership Functions

By Pushpa Mamoria Deepa Raj

DOI:, Pub. Date: 8 Sep. 2016

Contrast enhancement is an emerging method for image enhancement of specific application to analyze the images clearer for interpretation and analysis in the spatial domain. The goal of Contrast enhancement is to serve an input image so that resultant image is more suited to the particular application. Images with good steps of grays between black and white are commonly the best images for the aim of human perception, a novel approach is proposed in this paper based on fuzzy logic. Mamdani fuzzy inference system models are developed to enhance the contrast of images based on different membership functions (MFs). 

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