Digital Image Processing

Ann Mary Mathew ¹, J. Anitha ²

Affiliations
1 Karunya University, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Karunya University, Coimbatore, Tamil Nadu, IN

Abstract

A large number of communication application contains videos. Videos in compressed format are uploaded online and may have blocking artifacts and other clarity issues not pleasing to the human eye. This algorithm called Iterative Video Enhancement Method (IVEM) takes as input the several number of compressed copies that are available and gives as output a single video that is enhanced both objectively and subjectively without any artifacts. This algorithm makes use of two sets namely Smoothness Constraint Set for removing blocking artifacts and Quantization Constraint Set which is the set of all quantization coefficients. The video that is obtained as the output is superior in quality compared to the two compressed input copies. However, it is not of the same quality of the original source video. Hence the quality of the reconstructed video can be further enhanced by using super resolution – a technique used for up scaling video by using multiple frames of the same object to achieve a higher resolution video.

Keywords

Compression, Blocking Artifacts, Reconstruction, Super Resolution.

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Priya Premkumar ¹, J. Anitha ²

Affiliations
1 Karunya University, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Karunya University, Coimbatore, Tamil Nadu, IN

Abstract

A common mistake in the concept of image search is that the technology is based on detecting and processing the information in the image itself. Searching for an images works like this; the meta data of the image is indexed and stored in a large database or repository and when a search query(keywords) is entered the image search engine accesses the index, and queries are matched with the stored information. The results are presented in no particular order of relevancy. The effectiveness of an image search engine depends on the relevance of the results it returns, and the clustering algorithm plays a big role. This paper compares the working of two clustering algorithms K-Means algorithm and Fuzzy C Means algorithm. When using Fuzzy C Means algorithm, one image can appear in more than one cluster unlike K-Means which is hard based grouping. Through the results it can be clearly seen that using Fuzzy C Means brings about a level of flexibility and proper clustering. The user can access images from an image search engine, picture library, trained data sets, etc. Hence there is a necessity of providing the user more accurate collection of images which can be done through Fuzzy C Means clustering.

Keywords

K-Means, Fuzzy C-Means, Relevant Images, Clustering, Hyperbolic Image Visualization.

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Neethu Tom ¹, J. Anitha ²

Affiliations
1 Karunya University, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Karunya University, Coimbatore, Tamil Nadu, IN

Abstract

Image based trackers may fail in real world scenarios due to the performance limitations of object detectors that generate noisy observations under illumination changes, reflections and occlusions. For this a new framework is proposed with a feedback from scene contextual information. Thus it is possible to overcome all the illumination variations and can identify the missing detection. This achieved through a Probability Hypothesis Density (PHD) filter that spatially modulates its strength based on the learned contextual information. While applying a patch-based technique to this it is able to overcome both spatial and temporal consistency of the static background. Through this it is possible to reconstruct the background images by picking up the original colour.

Keywords

Object Tracking, Clutter, Birth Events.

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