Approaches for Automated Object Recognition and Extraction from Images — a Study

Digital Image Interpretation is one of the most challenging and important tasks in many scientific and engineering applications. The two vital subtasks in image interpretation are recognition and extraction of object(s) of interest (OOI) from an image. When such tasks are manually performed, it calls for human experts, making them more time consuming, less cost effective and highly constrained. These negative factors led to the development of a computer system which performed an automatic analysis of visual information in order to bring in consistency, efficiency and accuracy in image analysis. This paper focuses on the survey of various existing automated approaches for recognition and extraction of OOI from an image in various scientific and engineering applications. In this work a categorization of these approaches is made based on the four principle factors (Input, Object, Feature, Attention) with which each approach is driven. Most of the approaches discussed in this paper are proved to work efficiently in real environments

G-L fractional differential operator modified using auto-correlation function: Texture enhancement in images

Texture plays an important role in the low-level image analysis and understanding in the field of computer vision. Texture based image enhancement is very important in many applications. In order to attain texture enhancement in images, a modified version of the Grunwald-Letnikov (G-L) definition based fractional differential operator is proposed in this paper. Considering the G-L based fractional differential operator’s basic definition and implementation, a filter is devised and its applicability for texture enhancement is analyzed. Subsequently, the filter is modified by considering the auto-correlation effect between pixels in a neighborhood. Experiments are carried out on a number of standard texture-rich images and it is proved that the modified filter enhances the image contrast by nonlinearly enhancing the image textural features. In addition, the texture enhancement is quantitatively proven by a few Gray Level Co-occurrence Matrix (GLCM) measures, such as contrast, correlation, energy and homogeneity. Their % of Improvement is discussed in detail and the substantial improvement attained by the modified G-L FD operator over the basic G-L FD operator is well proved. Keywords: Image texture, Fractional differentiation, Auto-correlation function, Texture enhancement, G-L definition, Gray level co-occurrence matri

Semi-supervised change detection approach combining sparse fusion and constrained k means for multi-temporal remote sensing images

Change detection is the measure of the thematic change information that can guide to more tangible insights into an underlying process involving land cover, land usage and environmental changes. This paper deals with a semi-supervised change detection approach combining sparse fusion and constrained k means clustering on multi-temporal remote sensing images taken at different timings T1 and T2. Initially a remote sensing fusion method with sparse representation over learned dictionaries is applied to the difference images. The dictionaries are learned from the difference images adaptively. The fused image is calculated by combining the sparse coefficients and the dictionary. Finally the fused image is subjected to constrained k means (CKM) clustering combining few known labelled patterns and unlabelled patterns which have been collected from experts. The enhanced (CKM) approach (ECKM) is compared with k means, adaptive k means (AKM) and fuzzy c means (FCM). Experimental results were carried out on multi-temporal remote sensing images. Results obtained using PCC and F1 measure confirms the effectiveness of the proposed approach. It is also noticed that the ECKM provides better results with less misclassification of errors as compared to k means, adaptive k means and fuzzy c means

Emotion-Based Content Personalization in Social Networks

Personalization is the process of customizing social network pages of users according to their needs and personal interests. It can also be used for filtering unwanted information from an individual's page received from other users, in case this information is unpleasant or unacceptable. To avoid unwanted information from a particular user in current social networks, the user needs to be denied accessibility by blocking them. However, instead of blocking the user, it would be preferable to have a mechanism that prevents the undesirable content in a user's social network page. Thus, this paper presents a model that determine the emotions shared in the content of a social network page by the user. The model determines the dominant emotions for a period of time and uses these to filter the content using the user's dominant emotions. Using the developed model, a novel system based on item based collaborative filtering process to personalize the user's social network page has been developed. A user study involving 5000 Twitter messages shows that the developed system performs satisfactory with a correctness in the filtering process of 87%

Application of Satellite Remote Sensing to find Soil Fertilization by using Soil Colour: study area Vellore District, Tamil Nadu, India.

Nowadays the usage of Remote Sensing and GIS techniques are too vast and many Earth observations had been done by Remote Sensing and GIS techniques. The innovation of this work is to use the LANDSAT image data and GIS Techniques to assess land information and soil classification in the most coming up area of Vellore district and propose the possible fertilization for this study area. Landsat image is classified by minimum distance classification algorithm and according to the reflectance characteristics of the surface material. From the classified data we can find out the best fertilization for the best soil using colour of the soil. It is proved that, within limitations the classification algorithms and threshold parameters have an important influence on the classification resul