Department of Electrical and Electronics Engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925

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Subject: search.filters.subject.Image preprocessing

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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.