Free 2018 IEEE (CSE) synopsis of students requested papers

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Automatic Annotation of Text with Pictures

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Aim: a novel application has emerged that automatically adds pictures to text. Objectives: Is to reduce a reader’s cognitive information processing load1 by showing relevant pictures along with input text. A reader can quickly and easily decide if the text information deserves a closer look. Existing System: Here, provides a detailed comparison of eight well-known picturing systems. 1. WordsEye,17 a text-to-3D-scene rendering system, parses text into a semantic representation and creates a 3D rendering (a scene). 2. Story Picturing Engine9 extracts keywords and matches words to a labeled image database as part of the process of story illustration. 3. ROC-MMS for Complex Sentences12 produces a multimodal (text and pictures) summary of sentences. 4. Simple Sentence Picturing18 is an effort to investigate the idea that pictures can convey simple sentences across language barriers. 5. Enriching Textbooks19 matches sections of a textbook to Internet images...
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Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

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Aim:   To build a dataset to learn a powerful CNN-based SICE enhancer from multi-exposure images. Proposed system: propose to use the convolutional neural network (CNN) to train a single image contrast enhancement (SICE) enhancer. A CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Overcome: One key issue is how to construct a training data set of low-contrast and high-contrast image pairs for end-to-end CNN learning      Advantage:     CNN based SICE method learns a complex nonlinear mapping function to map a low-contrast (either underexposure or over-exposure) region to a good contrast region.     powerful CNN-based SICE enhancer, which is capable of adaptively generating high quality enhancement result for a single over-exposed or underexposed input image.        Existing System:   Previous SICE methods adjust the tone curve to correct ...
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A Comprehensive Study of the Effect of Spatial Resolution and Color of Digital Images on Vehicle Classification

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Aim: To provide an answer for the question: what are the best values for these two properties (spatial resolution and color) of digital cameras to use in vision-based vehicle classification systems? In other words, how many pixels are sufficient to build an accurate vision-based vehicle classification system? Aim: To study the effect of spatial resolution on the accuracy and performance of image classification methods. Proposed System: present a comprehensive study of the effect of these two spatial characteristics ( Dimension and color ) of digital images on the vision-based vehicle classification process in terms of accuracy and performance. For Bag-of-Visual Words (BoVW) [13], Vector of Locally Aggregated Descriptors (VLAD) [14] and Fisher Vector (FV) [15] it is recommended to use the DSIFT descriptor. The ResNet/Convolutional Neural Network (CNN), architecture is considered the top classification method with an accuracy rate of 95.12% compare to BoVW, VLAD an...
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