BITS Faculty Publications

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Subject: search.filters.subject.Image preprocessing

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    Comparative study of preprocessing and classification methods in character recognition of natural scene images
    (Springer, 2025-01) Sinha, Yash
    This paper presents an approach to character recognition in natural scene images. Recognizing such text is a challenging problem in the field of Computer Vision, more than the recognition of scanned documents due to several reasons. We propose a classification technique for classifying characters based on a pipeline of image processing operations and ensemble machine learning techniques. This pipeline tackles problems where Optical Character Recognition (OCR) fails. We present a framework that comprises a sequence of operations such as resizing, grey scaling, thresholding, morphological opening and median filtering on the images to handle background clutter, noise, multi-sized and multi-oriented characters and variance in illumination. We used image pixels and HOG (Histogram of Oriented Gradients) as features to train three different models based on Nearest-Neighbour, Random Forest and Extra Tree classifiers. When the input images were pre-processed, HOG features were extracted and fed into extra tree classifier, and the model classified the characters with maximum accuracy, among the other models that we tested. The proposed steps have been experimentally proven to yield better accuracy than the present state-of-the-art classification techniques on the Chars74k dataset. In addition, the paper includes a comparative study elaborating on various image processing operations, feature extraction methods and classification techniques.
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    Comparative Study of Convolutional Neural Network Object Detection Algorithms for Image Processing
    (IEEE, 2023) Singh, Navin
    This paper presents a comparative study on three Convolutional Neural Network (CNN) object detection algorithms to find the best detector based on the combination of speed and accuracy on a personal computer. The MATLAB® development environment is used to evaluate three different object detector algorithms, namely Faster Region-Based Convolutional Network (R-CNN), Single Shot Detector (SSD) and You Only Look Once (YOLO). These algorithms are trained, and their performance metrics are tested on a small sample dataset. The results show that the SSD object detector algorithm performs best when considering both performance and processing speeds. Faster R-CNN detected objects at an average speed of 4.838 seconds and achieved a mean average precision of 0.76 with an average loss of 0.429. SSD detected objects at an average speed of 0.377 seconds and achieved a mean average precision of 0.92 with an average loss of 1.754. YOLO v3 detected objects at an average speed of 1.004 seconds and achieved a mean average precision of 0.81 with an average loss of 2.739.
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    A FEM and Image Processing Based Method for Simulation of 01 Manufacturing Imperfections
    (ARME, 2012) Rout, Bijay Kumar
    Use of appropriate methods to capture manufacturing imperfection at the conceptual stage is a major challenge for the designer and researchers in industry. Imperfections are observed in almost all type of in macro, micro and nano-machining domain of manufacturing process. These imperfections lead to undesirable performance in application phase. In the present work, a simulation based approach to handle manufacturing imperfection is implemented using image processing operators. This method simulates the image of the component due to manufacturing imperfections. The usage of these image processing operators facilitates a realistic simulation of manufacturing errors, in macro, micro, and nano domain manufacturing. The simulated image is further processed for its structural properties i.e. maximum deflection, reactions, Von Mises stress, and change in amount of material, corresponding to its intended application. In order to generate these results based on modified image of beam, the concept of "Solid Isotropic Material with Penalization"(SIMP) is utilized along with 2-D finite element routine. An example of a simple cantilever beam is selected to illustrate the proposed methodology, and the results are analyzed. The present work discusses a simple and easy method to predict the behavior of designed component prior to its manufacturing.
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    Applications of fractional calculus in computer vision: A survey
    (Elsevier, 2022-06) Agarwal, Shivi; Mathur, Trilok
    Fractional calculus is an abstract idea exploring interpretations of differentiation having non-integer order. For a very long time, it was considered as a topic of mere theoretical interest. However, the introduction of several useful definitions of fractional derivatives has extended its domain to applications. Supported by computational power and algorithmic representations, fractional calculus has emerged as a multifarious domain. It has been found that the fractional derivatives are capable of incorporating memory into the system and thus suitable to improve the performance of locality-aware tasks such as image processing and computer vision in general. This article presents an extensive survey of fractional-order derivative-based techniques that are used in computer vision. It briefly introduces the basics and presents applications of the fractional calculus in six different domains viz. edge detection, optical flow, image segmentation, image de-noising, image recognition, and object detection. The fractional derivatives ensure noise resilience and can preserve both high and low-frequency components of an image. The relative similarity of neighboring pixels can get affected by an error, noise, or non–homogeneous illumination in an image. In that case, the fractional differentiation can model special similarities and help compensate for the issue suitably. The fractional derivatives can be evaluated for discontinuous functions, which help estimate discontinuous optical flow. The order of the differentiation also provides an additional degree of freedom in the optimization process. This study shows the successful implementations of fractional calculus in computer vision and contributes to bringing out challenges and future scopes.
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    BSwarm robot — A low cost mobile wireless sensor research platform using COTS products
    (IEEE, 2015) Shenoy, Meetha V.
    MWSN is an emerging area of research and most of the work in the field of MWSN is done at the simulation level as there is hardly any cost effective hardware platform(node/mote) available for MWSN applications. To handle mobility, the MWSN node should be much more efficient than the nodes in static WSN. Moreover, a MWSN node should be capable of handling real time mobility control, path planning and navigation. The application domains of MWSN can be further expanded by incorporating swarm like intelligence in MWSN. We have developed a low cost, small form factor hardware platform which will function as a node in MWSN using custom off the shelf(COTS) products. Our mobile hardware platform, henceforth called as BSwarm robot supports self-assembly, to achieve complex tasks. The platform also support image assisted navigation and provides extensive I/O support for further feature expansion. The testbed consisting of multiple BSwarm robot can be utilized for the development and validation of algorithms/protocols related to MWSNs, distributed control of Swarm robots, real time image processing etc. BSwarm robot is a multi processor based robot designed in such a way that it can be used for applications which may demand varied degree of processing, communication and input-output capabilities. This paper also highlights major factors that can be taken into consideration while choosing the hardware platform for MWSNs so that the protocol stack development for MWSNs becomes easier.
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    A Clustering and Image Processing Approach to Unsupervised Real-Time Road Segmentation for Autonomous Vehicles
    (IEEE, 2022) Chamola, Vinay
    Path planning is a crucial task in autonomous vehicles for which real-time road segmentation is very important. Most existing road segmentation techniques are supervised but, in many cases, their performance may be limited by the availability of and variety in a large training dataset. In contrast, we propose a research direction on unsupervised road segmentation that does not need any training or adaption and can be utilized widely. We use K-means clustering and image processing techniques to segment roads in RGB images. The scheme works well on the KITTI Road dataset (urban), giving a maximum, mean, and minimum IoU score of 93.75 %, 66.64% and 32.21% respectively. The minimum, mean and maximum time taken for segmentation were 1.084 s, 1.999 s and 3.794 s respectively on an Intel Core i5-8th Gen.(8GB RAM) CPU. A major reason for low values of minimum accuracy is that the scheme may segment the sidewalk also as a road. Although the mean IoU score is lower and the processing time higher relative to existing schemes, the results are very promising as our scheme is completely unsupervised and the processing time can be reduced by leveraging the capabilities of GPUs, parallel execution, hardware acceleration and the like.
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    Palm-print recognition based on scale invariant features
    (IEEE, 2019) Ajmera, Pawan K.
    Over the past few years, palm-print recognition has proved to be one of the extensively used technology for human identification/verification in many aspects. This paper presents the implementation of five feature extraction algorithms such as Mean, AAD (Average Absolute Deviation), GMF (Gaussian Membership Function) along with SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Feature) for effective recognition. SVM (Support Vector Machine) and KNN (K-Nearest Neighbor) are the machine learning algorithms used for the classification of data. Experimentations are carried out on the PolyU and IIT-Delhi palm-print databases. The scale invariant features of SURF provides the best performance with Correct Recognition Rate (CRR) of 99.56% and 97.95% for IIT-Delhi and PolyU palm-print database respectively.
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    Adaptive Quality Enhancement Fingerprint Analysis
    (IEEE, 2020) Ajmera, Pawan K.
    Poor quality of the fingerprint image prevents accurate recognition as the employed methods are largely dependent on the fingerprint image quality. Algorithms will be better suited to detect fingerprint images if they are adapted according to their quality classes. In this paper, a class adaptive fingerprint enhancement algorithm is presented, classes dry, good and wet are assigned and further image processing is carried out. Features such as mean, variance, moisture index, Ridge Valley Area Uniformity (RVAU) are extracted from the ROI images. There are two stages of fingerprint quality enhancements which include the quality preprocessing (QP) and the enhancement stage. Support Vector Machine (SVM) algorithm is used to classify the images. Further, comparison scores are calculated by comparing the given image with the database of the minutiae using the minutiae matching technique. Experimentation is carried out on the FVC fingerprint database. A comparative analysis of the fuzzy C-means based clustering and mean based clustering is also experimented.
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    Face Recognition using Local Texture Descriptor
    (IJERT, 2014-04) Ajmera, Pawan K.
    A face recognition system is a computer application for automatically identifying a person from a digital image. Recognition of face in uncontrolled lightening situations is one of the most important bottlenecks for practical face recognition systems. This paper addresses the problem of illumination effects on Face recognition and work for an approach to reduce their effect on recognition performance. For this following methods are used: (i) simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) Local Binary Pattern (LBP) texture descriptor which labels the pixels of an image and gives output as a histogram of image; and (iii) principle component analysis (PCA) feature extraction algorithm is used to improve robustness. The proposed method is tested on ORL face database. The crux of the work lies in optimizing Euclidean distance classifier for recognition of face.