Simily Joseph

Bio: Simily Joseph is an academic researcher from Cochin University of Science and Technology. The author has contributed to research in topics: Local binary patterns & Image retrieval. The author has an hindex of 4, co-authored 4 publications receiving 42 citations.

Multi-Query Content Based Image Retrieval System using Local Binary Patterns

Abstract: Content Based Image Retrieval systems open new research areas in Computer Vision due to the high demand of image searching methods. CBIR is the process of finding relevant image from large collection of images using visual queries. The proposed system uses multiple image queries for finding desired images from database. The different queries are connected using logical AND operation. Local Binary Pattern (LBP) texture descriptors of the query images are extracted and those features are compared with the features of the images in the database for finding the desired images. The proposed system is used for retrieving similar human face expressions. The use of multiple queries reduces the semantic gap between low level visual features and high level user expectation. The experimental result shows that, the use of multiple queries has better retrieval performance over single image queries. General Terms Image Processing, Content Based Image Retrieval

Ultrasound Image Despeckling using Local Binary Pattern Weighted Linear Filtering

Abstract: Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images.

Local Binary Patterns, Haar Wavelet Features and Haralick Texture Features for Mammogram Image Classification Using Artificial Neural Networks

Abstract: The objective of this study is the classification of mammogram images into benign and malignant using Artificial Neural Network. This framework is based on combining Local Binary Patterns, Haar Wavelet features and Haralick Texture features. The study shows the importance of Computer Aided Medical Diagnosis in successful decision making by calculating the likelihood of a disease. This multi feature approach for classification obtains an average classification accuracy of 98.6% for training, validation and testing.

Content based image retrieval system for Malayalam handwritten characters

Abstract: Content Based Image Retrieval is one of the prominent areas in Computer Vision and Image Processing. Recognition of handwritten characters has been a popular area of research for many years and still remains an open problem. The proposed system uses visual image queries for retrieving similar images from database of Malayalam handwritten characters. Local Binary Pattern (LBP) descriptors of the query images are extracted and those features are compared with the features of the images in database for retrieving desired characters. This system with local binary pattern gives excellent retrieval performance.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control

Abstract: This page shows some examples of multimedia files. It is also available at: http://www.ieee-uffc.org/tr/mexample.pdf. For submission of multimedia manuscripts to TUFFC, please follow “Information for Contributors” at: http://www.ieeeuffc.org/tr/contrib.pdf. Multimedia Example Created by Jian-yu Lu, Editor-in-Chief, 07/07/03 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society 1. Color Figure: The IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (TUFFC) now accepts color figures online with corresponding grayscale figures in print without additional charges if authors follow the multimedia manuscript submission procedure in the “Information for Contributors”. The “color figures” icon below indicates in the print that a color version of the figure is available online. It also links to the color figure submitted originally by the authors for viewing details. Because of the sizes of color figures and the additional editorial work such as making two sets of PostScript files in which they remain identical after replacing grayscale with color, authors should request color figures online only when it is necessary. The color figure must be in JPEG (Joint Photographic Expert Group) or GIF (Graphics Interchange Format) format to reduce file size and to decrease the bandwidth usage on the TUFFC server. Fig. 1. An eight-layer printed circuit board (PCB) used primarily for multi-channel ultrasound signal reception and storage. The board was designed in the Ultrasound Lab at The University of Toledo, led by Dr. Jian-yu Lu. It is one of the many PCBs designed for a high-frame rate medical ultrasound imaging system [1], which consists of 128 linear, ±144V peak, 0.05-10MHz, and 12-bit arbitrary waveform generators for the production of ultrasound signals or for other research purposes. 2. Movies: Please click on the movie icon to see a movie. The two movies below give you an idea on the file size versus movie quality. “VCD quality” here means 1150kbits/s for video and 224kbits/s for 16-bit MP2 stereo audio at 44.1KHz sampling rate. Fig. 2. An introduction to the Ultrasound Lab at The University of Toledo. 3. Movies and Animations: Please click on the movie icons to see movies or animations. Fig. 3. Circuit design and construction of the imaging system. 4. Movies and Animation: Please click on the movie icons to see movies or animation. Movie [File size: 785KB; Format: MPEG1; Resolution: 320X240; Duration: 9 seconds]

Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images.

Abstract: Background. The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project. Methods. With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies. Results. In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outperformed SUVmean for outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmean and TLG (0.6 and 0.52, resp.). Conclusions. CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use at http://code.google.com/p/cgita.

GEO matching regions: multiple regions of interests using content based image retrieval based on relative locations

Abstract: Information retrieval systems are getting more attention in the era of multimedia technologies such as an image, video, audio and text files. The large numbers of images are challenges in computer systems field to store, manage data effectively and efficiently. The shape retrieval feature of different objects in the image also remains a difficult problem due to distinct angle view of different objects in a scene only; few studies have reported solution to the problem of finding relative locations of ROIs. In this paper, we proposed three methods such as1. Geolocation-based image retrieval (GLBIR), 2.Unsupervised feature technique Principal component analysis (PCA) and 3.multiple region-based image retrieval. The first proposed (GLBIR) method identifies geo location an image using visual attention based mechanism and its color layout descriptors. These features are extracted from geo-location of query image from Flickr database. Our proposed model does not fully semantic understanding of image content, uses visual metrics for example; the proximity ,color contrast, size and nearness to image's boundaries to locate viewer's attention. We analyzed results and compared with state of art CBIR Systems and GLBIR Technique. Our second method to refine images exploiting and fusing by unsupervised feature technique using principal component analysis (PCA). The visually similar images clustering together with analyses image retrieval process and remove outliers initially retrieved image set by PCA. To evaluation our proposed approach, we used thousands of images downloaded from Flickr and CIFAR-10 databases using Flickr public API. Finally, we determinately proposed a system for image retrieval based on region. It provides a user interface for availing to designate the watershed ROI within an input image. During the retrieval of images, regions' feature vectors having codes of region homogeneous to a region of input image are utilized for comparison. Standard datasets are used for evaluation of proposed approach. The experiment demonstrates and effectiveness of the proposed method to achieve higher annotation performance increases accuracy and reduces image retrieval time. We evaluated our proposed approach on images dataset from Flickr and CIFAR-10.

Digital holography and digital image processing : principles, methods, algorithms

Abstract: 1. Introduction.- 2. Optical Signals and Transforms.- 3. Digital Representation of Signals.- 4. Digital Representation of Signal Transformations.- 5. Methods and Algorithms of Digital Filtering.- 6. Fast Algorithms.- 7. Statistical Methods and Algorithms.- 8. Sensor Signal Perfecting, Image Restoration, Reconstruction and Enhancement.- 9. Image Resampling and Geometrical Transformations.- 10. Signal Parameter Estimation and Measurement. Object Localization.- 11. Target Location in Clutter.- 12. Nonlinear Filters in Signal/Image Processing.- 13. Computer Generated Holograms.