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Object detection has been a focus of research in human-computer interaction. Skin area detection has been a key to different recognitions like face recognition, human motion detection, pornographic and nude image prediction, etc. Most of the research done in the fields of skin detection has been trained and tested on human images of African, Mongolian and Anglo-Saxon ethnic origins. Although there are several intensity invariant approaches to skin detection, the skin color of Indian sub-continentals have not been focused separately. The approach of this research is to make a comparative study between three image segmentation approaches using Indian sub-continental human images, to optimize the detection criteria, and to find some efficient parameters to detect the skin area from these images. The experiments observed that HSV color model based approach to Indian sub-continental skin detection is more suitable with considerable success rate of 91.1% true positives and 88.1% true negatives.

With the advancement of imaging science, image enhancement has become an important aspect of image processing domain. It is necessary to gather a comprehensive knowledge regarding the existing enhancement technologies to identify and solve their problems and thus to elevate the current image enhancement methodologies. This paper provides the underlying concept of contrast enhancement, brightness preservation as well as brightness enhancement techniques. Besides this, we provide a short description of the existing renowned enhancement methods with their mathematical description and application area. Moreover, experimental results are provided to make a comparative analysis where both qualitative and quantitative measurements are performed. Different enhancement methods are run on same images to examine the qualitative performance. Peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), execution time (ET) and discrete entropy (DE) are quantitative measurement metrics used for quantitative assessment. Most of the cases, it is found that Histogram Equalization has the highest degree of deviation from the input image which basically generates more visual artifacts. Contextual and Variational Contrast enhancement technique takes long time for execution with respect to other enhancement techniques. From our quantitative and qualitative evaluation, we find that Layered Difference Representation performs comparatively produces better enhancement result in all aspect than other existing methods.

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Management of legacy software and its code, generally written in procedural languages, is often costly and time-consuming. To help this management, a migration from procedural to object oriented paradigm could be a cost effective option. One approach for such migration can be based on the underlying dependency structure of the procedural source code. In this work, we propose a new heuristic algorithm that utilizes such structure for the design migration using agglomerative hierarchical clustering. The dependency structure that has been used involve functions, parameters and global data of the procedural code. Given a procedural code, the proposed approach produces candidate classes for an object oriented design. The proposed algorithm was tested against a database of procedural codes. The results obtained have been empirically validated using Jaccard similarity coefficient. It is observed that the proposed method yields 75.6% similarity with respect to the ground truth in the average case.

Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques.

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