Awais Adnan

Publications

6 Object Identification with Color, Texture, and Object-Correlation in CBIR System

Authors: Muhammad Nawaz, Muhammad Ali, Awais Adnan, Sajid Anwar, Tamleek Ali

Abstract:

Needs of an efficient information retrieval in recent years in increased more then ever because of the frequent use of digital information in our life. We see a lot of work in the area of textual information but in multimedia information, we cannot find much progress. In text based information, new technology of data mining and data marts are now in working that were started from the basic concept of database some where in 1960. In image search and especially in image identification, computerized system at very initial stages. Even in the area of image search we cannot see much progress as in the case of text based search techniques. One main reason for this is the wide spread roots of image search where many area like artificial intelligence, statistics, image processing, pattern recognition play their role. Even human psychology and perception and cultural diversity also have their share for the design of a good and efficient image recognition and retrieval system. A new object based search technique is presented in this paper where object in the image are identified on the basis of their geometrical shapes and other features like color and texture where object-co-relation augments this search process. To be more focused on objects identification, simple images are selected for the work to reduce the role of segmentation in overall process however same technique can also be applied for other images.

Keywords: Texture, Color, features, Object correlation, Geometrical shape, contents

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5 Comparative Analysis of Transient-Fault Tolerant Schemes for Network on Chips

Authors: Muhammad Ali, Awais Adnan

Abstract:

Network on a chip (NoC) has been proposed as a viable solution to counter the inefficiency of buses in the current VLSI on-chip interconnects. However, as the silicon chip accommodates more transistors, the probability of transient faults is increasing, making fault tolerance a key concern in scaling chips. In packet based communication on a chip, transient failures can corrupt the data packet and hence, undermine the accuracy of data communication. In this paper, we present a comparative analysis of transient fault tolerant techniques including end-to-end, node-by-node, and stochastic communication based on flooding principle.

Keywords: Fault-Tolerance, NoC, Transient faults

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4 A Real-Time Specific Weed Recognition System Using Statistical Methods

Authors: Muhammad Islam, Awais Adnan, Imran Ahmed, Syed Inayat Ali Shah

Abstract:

The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. In order to accomplish this objective, a real-time robotic system is developed to identify and locate outdoor plants using machine vision technology and pattern recognition. The algorithm is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm has been tested on weeds at various locations, which have shown that the algorithm to be very effectiveness in weed identification. Further the results show a very reliable performance on weeds under varying field conditions. The analysis of the results shows over 90 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.

Keywords: Image Processing, standard deviation, Weed detection, real-timerecognition

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3 OCR For Printed Urdu Script Using Feed Forward Neural Network

Authors: Zaheer Ahmad, Awais Adnan, Jehanzeb Khan Orakzai, Inam Shamsher

Abstract:

This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. In the proposed system individual characters are recognized using our own proposed method/ algorithms. The feature detection methods are simple and robust. Supervised learning is used to train the feed forward neural network. A prototype of the system has been tested on printed Urdu characters and currently achieves 98.3% character level accuracy on average .Although the system is script/ language independent but we have designed it for Urdu characters only.

Keywords: Algorithm, Pattern Matching, Supervised Learning, Feed Forward Neural Networks

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2 Urdu Nastaleeq Optical Character Recognition

Authors: Zaheer Ahmad, Awais Adnan, Jehanzeb Khan Orakzai, Inam Shamsher

Abstract:

This paper discusses the Urdu script characteristics, Urdu Nastaleeq and a simple but a novel and robust technique to recognize the printed Urdu script without a lexicon. Urdu being a family of Arabic script is cursive and complex script in its nature, the main complexity of Urdu compound/connected text is not its connections but the forms/shapes the characters change when it is placed at initial, middle or at the end of a word. The characters recognition technique presented here is using the inherited complexity of Urdu script to solve the problem. A word is scanned and analyzed for the level of its complexity, the point where the level of complexity changes is marked for a character, segmented and feeded to Neural Networks. A prototype of the system has been tested on Urdu text and currently achieves 93.4% accuracy on the average.

Keywords: OCR, Urdu, Cursive Script

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1 Image Search by Features of Sorted Gray level Histogram Polynomial Curve

Authors: Muhammad Ali, Awais Adnan, Amir Hanif Dar

Abstract:

Image Searching was always a problem specially when these images are not properly managed or these are distributed over different locations. Currently different techniques are used for image search. On one end, more features of the image are captured and stored to get better results. Storing and management of such features is itself a time consuming job. While on the other extreme if fewer features are stored the accuracy rate is not satisfactory. Same image stored with different visual properties can further reduce the rate of accuracy. In this paper we present a new concept of using polynomials of sorted histogram of the image. This approach need less overhead and can cope with the difference in visual features of image.

Keywords: grayscale, Sorted Histogram, Polynomial Curves, feature pointsof images, visual properties of image

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Abstracts

1 A Technique for Image Segmentation Using K-Means Clustering Classification

Authors: Awais Adnan, Sadia Basar, Naila Habib

Abstract:

The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: Clustering, Image Segmentation, region, K-means function, local and global minimum

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