The document describes several IEEE 2015 projects related to image processing, including methods for hyperspectral image classification, reversible image data hiding with contrast enhancement, and image segmentation using a Markov random field. It also presents a novel technique for heart rate evaluation from face reflectance, which offers a non-invasive alternative to traditional monitoring methods. Each project is implemented in MATLAB and demonstrates superior performance compared to existing methods in terms of accuracy and efficiency.

1. Image Processing IEEE 2015 Projects
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List Link : http://kasanpro.com/projects-list/image-processing-ieee-2015-projects
Title :Supervised Spectral-Spatial Hyperspectral Image Classification With Weighted Markov Random Fields
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/supervised-spectral-spatial-hyperspectral-image-classification-with-weighted-mark
Abstract : This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial
information. Under the maximum a posteriori framework, we propose a supervised classification model which includes
a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data
fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while
the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a
spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed
as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and
contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm,
named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of
multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms
many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic.
Title :Reversible Image Data Hiding with Contrast Enhancement
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/reversible-image-data-hiding-contrast-enhancement
Abstract : In this letter, a novel reversible data hiding (RDH) algorithm is proposed for digital images. Instead of trying
to keep the PSNR value high, the proposed algorithm enhances the contrast of a host image to improve its visual
quality. The highest two bins in the histogram are selected for data embedding so that histogram equalization can be
performed by repeating the process. The side information is embedded along with the message bits into the host
image so that the original image is completely recoverable. The proposed algorithm was implemented on two sets of
images to demonstrate its efficiency. To our best knowledge, it is the first algorithm that achieves image contrast
enhancement byRDH. Furthermore, the evaluation results show that the visual quality can be preserved after a
considerable amount of message bits have been embedded into the contrast-enhanced images, even better than
three specificMATLAB functions used for image contrast enhancement.
Title :An Efficient MRF Embedded Level Set Method for Image Segmentation
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/mrf-embedded-level-set-method-image-segmentation
Abstract : This paper presents a fast and robust level set method for image segmentation. To enhance the
robustness against noise, we embed a Markov random field (MRF) energy function to the conventional level set
energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them to
fall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraic
multigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain,
respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of our
method for big image databases. By comparing the proposed fast and robust level set method with the standard level
set method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medical
images and natural images, we comprehensively demonstrate the new method is robust against various kinds of
noises. Especially, the new level set method can segment an image of size 500 by 500 within three seconds on
MATLAB R2010b installed in a computer with 3.30GHz CPU and 4GB memory.
Image Processing IEEE 2015 Projects
Title :Image Sensor-Based Heart Rate Evaluation From Face Reflectance Using Hilbert-Huang Transform
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/heart-rate-evaluation-from-face-reflectance-using-hilbert-huang-transform
Abstract : Monitoring heart rates using conventional electrocardiogram equipment requires patients to wear adhesive
gel patches or chest straps that can cause skin irritation and discomfort. Commercially available pulse oximetry
sensors that attach to the fingertips or earlobes also cause inconvenience for patients and the spring-loaded clips can
be painful to use. Therefore, a novel robust face-based heart rate monitoring technique is proposed to allow for the
evaluation of heart rate variation without physical contact with the patient. Face reflectance is first decomposed from a
single image and then heart rate evaluation is conducted from consecutive frames according to the periodic variation
of reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum as
heartbeats cause changes to blood volume in the blood vessels in the face. To achieve a robust evaluation, ensemble

2. empirical mode decomposition of the Hilbert-Huang transform is used to acquire the primary heart rate signal while
reducing the effect of ambient light changes. Our proposed approach is found to outperform the current state of the
art, providing greater measurement accuracy with smaller variance and is shown to be feasible in real-world
environments.