Video-surveillance systems are one of the main source of information during investigations. However, the adopted closed-circuit devices are often affected by poor quality mainly because of economical and practical problems. In many cases a low quality image can give useful information during the first phase of the investigation. As a consequence, the images and sequences coming from video-surveillance systems need to be digitalized in order to be processed to enhance and extract features useful for crime analysis. Visual information is the most appealing and intuitive mode of conveying information. Further, the amount of information that video carries is significantly greater than that carried by any other media. From surveillance for security, to capturing and analyzing medical procedures, to forensic applications. The greatest users of video information constitute the surveillance cameras used for security. The content in such applications constitutes hours of video usually taken from a single camera that is stationary. For such applications, it is usually necessary to trace certain anomalous events or individuals .In particular, airport surveillance cameras, surveillance camera in traffic, shopping malls are very good examples. The purpose of these cameras is to detect suspicious activity, like a perpetrator leaving a piece of luggage that might be harmful, or picking up something that might not belong to him or her, Firing from a vehicle, suspicious activity in shopping malls etc. In this work, the task is the automatic segmentation of continuous video into activity-based segments. It is different from the conventional shot-detection and scene change problems of video processing. The information being searched for is not the boundary between two different video contents in a single video, but a significant change in the activity within a single scene in the video. Thus, a temporal segmentation of the video into different events is desired. The approach used is to combine some of the conventional shot-detection methods with some new techniques for video-content analysis . KeywordsVideo-surveillance systems, crime analysis, automatic segmentation, activity-based segments, video-content analysis. International Journal of Machine Intelligence ISSN: 0975-2927 & E-ISSN: 0975-9166, Volume 4, Issue 1, 2012 Citation: Angadi S.A., Vilas Naik and Ashwin Kumar (2012) Automatic Activity Segmentation from Surveillance Video Using Conventional Shot Boundary Detection Methods. International Journal of Machine Intelligence, ISSN: 0975-2927 & E-ISSN: 0975-9166, Volume 4, Issue 1, pp.-404. Copyright: Copyright©2012 Angadi S.A., Vilas Naik and Ashwin Kumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Text detection and extraction from scene images is useful in finding certain facts in supporting court of laws for solving crime problems and various forensic science applications. Text detection from scene images is a difficult and challenging problem due to various issues such as; varying font size, style, uneven illumination and other degradations. In this paper, a new method that uses wavelet energy features for text detection and extraction from scene images is presented. Initially, preprocessed binarized image will be divided into 50x50 blocks. Then, wavelet energy features are obtained from every block and potential text blocks are identified using newly defined discriminant functions. Further, the detected text blocks are merged and refined to extract text regions. The proposed method is robust and achieves a detection rate of 97% on a variety of scene images each of size 240x320. KeywordsText detection, scene images, wavelet energy features, discriminant functions. International Journal of Machine Intelligence ISSN: 0975-2927 & E-ISSN: 0975-9166, Volume 4, Issue 1, 2012 Citation: Angadi S.A., et al. (2012) Text detection and extraction from Scene Images using wavelet features. International Journal of Machine Intelligence, ISSN: 0975-2927 & E-ISSN: 0975-9166, Volume 4, Issue 1, pp.-409. Copyright: Copyright©2012 Angadi S.A., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Structural pattern recognition in case of molecules is an important task in the field of bio-engineering. Several techniques are employed in order to get the exact structural conformation and structural parameters of molecules. Present paper discusses some of the available techniques such as Fourier Infra-Red Spectrocopy, Raman Spectroscopy and Theoretical Structure simulation which are employed in this technologically important field. An attempt has been made to look for the exact structural conformation in case of Polyformaldehyde using First-principles calculations based on Density Functional Theory.

Mycobacterium Tuberculosis(M.TB) bacilli is causative agent of Tuberculosis. Manual detection method for M.TB is time consuming, tedious & sometime it may confuse with some stain residue & non tuberculosis bacilli. For this reason the need of atomization is required for exact identification of tuberculosis. We present a new method for M.TB bacilli cells recognition method using Moment invariant and Gaussian member function. The object i.e. M.TB bacilli cells are extracted using color segmentation method, from Ziehl-Neelsen (ZN) stained sputum smears images. These images have blue color background on which red color M.TB bacilli cells. The objects are extracted using color segmentation (thresholding) method and boundary detection. The various extracted shapes contain M. TB bacilli cells, non TB cells, stain residue etc, these extracted objects are preprocess using morphological dilation and then moment invariant feature extracted. Our main objective is to recognizes M.TB bacilli cells with the help of color segmentation & shape base feature moment invariants using Gaussian member function. The recognition rate of M.TB bacilli cells is obtaining 98.17%.

This paper describes a different neural network model for classification and grading of bulk seeds samples using different artificial neural network models. Algorithms are developed to acquire and process color images of bulk seeds samples. Different seeds like Groundnut, Jowar, Wheat, Rice, Metagi, Red gram, Bengal gram, and Lentils etc. are considered for the study. The developed algorithms are used to extract over 11 (9 color, area and equidiameter) features, 18 (color only) features and 20 (18 color and 2 boundary) features. The area and equidiameter features are extracted from the watershed segmentation. Different types of Neural Network based classifier is used to identify the unknown seeds samples. The classification is carried out using different types of features sets, viz., color, area and equidiameter. Classification accuracies of over 85% are obtained for all the seeds types using all the three feature sets. And also different neural network gives different accuracies and time period taken for training all the three feature sets.