Abstract : This thesis consists of five parts, each of which considers methods based on filter banks for different applications in signal and image processing. In Part I, a non-parametric estimation method is proposed using filter banks to estimate signals with varying waveforms and arrival times.
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Thesis on image enhancement pdf
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Show downloadable dissertations only. Search for dissertations about: "thesis for image enhancement" Showing result 1 - 5 of 55 swedish dissertations containing the words thesis for image enhancement.
- Computer Science Thesis in Digital Image Processing.
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Latest thesis topics in digital image processing
Abstract Night-time vehicle detection, i. This thesis proposes three object detection approaches that are based on image enhancement, object proposal and feature fusion for single-class and multi-class night-time vehicle detection.
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In night-time images, vehicles are under dim conditions: low contrast and low luminosity. In Chapter 2, we have designed a night-time image enhancement approach that improves the multi-scale retinex MSR which is effective for day-time image enhancement to improve the contrast and luminosity. In addition, to cope with the complex background of night-time traffic scenes, we extract five complementary features that are integrated by a proposed score-level multi-feature fusion approach that uses the average classification contribution to weigh each feature.
Besides, we propose a novel region-of-interest ROI extraction approach that combines a vehicle light detection method with EdgeBoxes: a state-of-the-art object proposal approach.
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Furthermore, a customized night-time vehicle dataset is developed in this chapter. Experimental results show that the proposed night-time image enhancement method, the score-level multi-feature fusion and the ROI extraction method are all effective for night-time vehicle detection.
Our proposed night-time vehicle detection method yields a Our method can detect blurred and partly occluded vehicles, and vehicles in a variety of sizes, numbers, locations and backgrounds. Chapter 3 focuses on the improvement of three techniques—image enhancement, object proposal and feature fusion—for obtaining a more effective night-time vehicle detection system. First, inspired by retinal information processing mechanisms, we develop an effective and novel night-time image enhancement method for modelling the horizontal cells HCs feedback and the centre-surround antagonistic receptive field RF of bipolar cells BCs.
Furthermore, we extract convolutional neural network CNN features, histogram of oriented gradient HOG features and local binary pattern LBP features that are used to train classifiers with support vector machine SVM and are fused at score-level by combining the score vectors of each feature with weights that are learned via scores and a linear SVM.
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During detection, a novel object proposal generator based on Bayesian saliency is proposed to generate a modest but accurate set of proposals that contains all vehicles within a night-time traffic image. Experimental results demonstrate that the proposed bio-inspired image enhancement method and feature fusion based on learned weights are both effective and better than several other studies.
Our proposed vehicle detection method exhibits a