International Journal of Computer and Applications

Towards better exploiting convolutional neural networks for remote sensing scene classification

Feature plays a very important role in the area of image processing. Before getting features, various image preprocessing techniques like binarization, thresholding, resizing, normalization etc. are applied on the sampled image. After that, feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. Feature extraction techniques are helpful in various image processing applications e.g. character recognition. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. Here in this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique, will be better. Hereby in this paper, we are going to refer features and feature extraction methods in case of character recognition application.

A Detailed Review of Feature Extraction in Image Processing Systems

Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. One specific field of interest is sign language recognition. This paper provides a thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research. The techniques reviewed are suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification, where the various algorithms at each stage are elaborated and their merits compared. Further, we also discuss the challenges and limitations faced by gesture recognition research in general, as well as those exclusive to sign language recognition. Overall, it is hoped that the study may provide readers with a comprehensive introduction into the field of automated gesture and sign language recognition, and further facilitate future research efforts in this area.

A review of hand gesture and sign language recognition techniques

This has been wonderful support for the University and I very much appreciate your role and others at JSC in giving us good advice and direction. As you can see from the table of contents, the program has covered a very broad range of topics. The 15 technical reports are those which were directly supported by the grant. Note also. that 13 M.Sc. and 11 Ph.D. students have also been directly involved in this effort. Finally, four staff members (Tesar, Tosunoglu, Hooper, and Freeman) have beer. involved and participated in the direction of the research.

University of Texas at Austin의 연구 현황

Software Engineering aims to produce a quality software product that is delivered on time, within the allocated budget, and with the requirements expected by the customer but unfortunately maximum of the times this goal is rarely achieved. A software life cycle is the series of identifiable stages that a software product undergoes during its lifetime. However, a properly managed project in a matured software engineering environment can consistently achieve this goal. This research is concerned with the methodologies that examine the life cycle of software through the development models, which are known as software development life cycle. Hereby, we are representing traditional i.e. waterfall, Iteration, Spiral models as well as modern development methodologies like Agile methodologies that includes Extreme programming, Scrum, Feature Driven Development, Component based software development methodologies etc. All of these models have advantages and disadvantages as well. Therefore, the main objective of this research is to represent different models of software development by showing the good and bad practices of each model. A comparative analysis of traditional as well as modern methodologies is made.

Comparative Analysis of Software Engineering Models from Traditional to Modern Methodologies

Software testing is a most important but expensive activity. To get the most efficient and effective testing, test cases are designed on the basis of conditions. While designing test cases, many test cases are developed that are of no use or produced in duplicate. Exhaustive testing requires program execution with all possible combinations of values for program variables, which is impractical due to resource limitations. Redundant test cases or the test cases that are of no use, simply increases the testing effort and hence increases the cost. Our goal is to reduce the time spent in testing by reducing the number of test cases. For this we have incorporated fuzzy techniques to reduce the number of test cases so that more efficient and accurate results may be achieved. Fuzzy clustering is a class of algorithms for cluster analysis in which the allocation of similar test cases is done to clusters that would help in finding out redundancy incorporated by test cases. We proposed a methodology based on fuzzy clustering by which we can significantly reduce the test suite. The final test suite resulted from methodology will yield good results for conditions/path coverage.

https://link.springer.com/content/pdf/10.1007%2Fs40012-013-0023-3.pdf

Software testing optimization through test suite reduction using fuzzy clustering

Preprocessing techniques are the first step in a character recognition system. This paper deals with the various preprocessing techniques involved in character recognition system with different kind of images ranges from simple handwritten form based documents and documents containing colored and complex background and varied intensities. Here, we are going to discuss all important preprocessing techniques like skew detection and correction, image enhancement techniques of contrast stretching, binarization, noise removal techniques, normalization and segmentation, morphological processing techniques.

Analytical Review of Preprocessing Techniques for Offline Handwritten Character Recognition

Handwritten character recognition is a difficult problem due to the great variations of writing styles, different size of the characters. Multiple types of handwriting styles from different persons are considered in this work. An image with higher resolution will certainly take much longer time to compute than a lower resolution image. In the practical image acquisition systems and conditions, shape distortion is common processes because different people’s handwriting has different shape of characters. The process of recognizing character recognition in this work has been divided into 2 phases. In the first phase, Image preprocessing is done in which image is firstly converted into binary form based on some threshold value obtained through Otsu’s method. After that removal of noise is done using median filter. After that feature extraction takes place that is done here through Fourier descriptor method using Fourier transform and correlation between template made through training data and test data is obtained. A multilayer feed forward neural network is created and trained through Back Propagation algorithm. After the training, testing is done to match the pattern with test data. Results for various convergence objective of neural network are obtained and analyzed.

/pdf/neural-network-based-approach-for-recognition-of-text-images-n4eydwxtjd.pdf

Gaurav Kumar

Papers

A Detailed Review of Feature Extraction in Image Processing Systems

Comparative Analysis of Software Engineering Models from Traditional to Modern Methodologies

Software testing optimization through test suite reduction using fuzzy clustering

Analytical Review of Preprocessing Techniques for Offline Handwritten Character Recognition

Neural Network based Approach for Recognition of Text Images