Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

http://disciplinas.stoa.usp.br/pluginfile.php/166516/mod_resource/content/1/Radiomics.pdf

Radiomics: the process and the challenges

Towards better exploiting convolutional neural networks for remote sensing scene classification

Our work in this paper stems from our insight that recent research efforts on open vehicle routing (OVR) problems, an active area in operations research, are based on similar assumptions and constraints compared to sensor networks. Therefore, it may be feasible that we could adapt these techniques in such a way that they will provide valuable solutions to certain tricky problems in the wireless sensor network (WSN) domain. To demonstrate that this approach is feasible, we develop one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. The algorithm design of EDAL leverages one result from OVR to prove that the problem formulation is inherently NP-hard. Therefore, we proposed both a centralized heuristic to reduce its computational overhead and a distributed heuristic to make the algorithm scalable for large-scale network operations. We also develop EDAL to be closely integrated with compressive sensing, an emerging technique that promises considerable reduction in total traffic cost for collecting sensor readings under loose delay bounds. Finally, we systematically evaluate EDAL to compare its performance to related protocols in both simulations and a hardware testbed.

EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks

Image retrieval based on shape similarity by edge orientation autocorrelogram

Existing algorithms based on scale invariant feature transform (SIFT) and Harris corners  such as edge-driven dual-bootstrap iterative closest point and Harris-partial intensity invariant feature descriptor (PIIFD) respectivley have been shown to be robust in registering multimodal retinal images. However, they fail to register color retinal images with other modalities in the presence of large content or scale changes. Moreover, the approaches need preprocessing operations such as image resizing to do well. This restricts the application of image registration for further analysis such as change detection and image fusion. Motivated by the need for efficient registration of multimodal retinal image pairs, this paper introduces a novel integrated approach which exploits features of uniform robust scale invariant feature transform (UR-SIFT) and PIIFD. The approach is robust against low content contrast of color images and large content, appearance, and scale changes between color and other retinal image modalities like the fluorescein angiography. Due to low efficiency of standard SIFT detector for multimodal images, the UR-SIFT algorithm extracts high stable and distinctive features in the full distribution of location and scale in images. Then, feature points are adequate and repeatable. Moreover, the PIIFD descriptor is symmetric to contrast, which makes it suitable for robust multimodal image registration. After the UR-SIFT feature extraction and the PIIFD descriptor generation in images, an initial cross-matching process is performed and followed by a mismatch elimination algorithm. Our dataset consists of 120 pairs of multimodal retinal images. Experiment results show the outperformance of the UR-SIFT-PIIFD over the Harris-PIIFD and similar algorithms in terms of efficiency and positional accuracy.

/pdf/an-efficient-approach-for-robust-multimodal-retinal-image-1powvo5yux.pdf

An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors

This paper presents a novel adaptive algorithm to detect the center of pupil in frontal view faces. This algorithm, at first, employs the viola-Jones face detector to find the approximate location of face in an image. The knowledge of the face structure is exploited to detect the eye region. The histogram of the detected region is calculated and its CDF is employed to extract the eyelids and iris region in an adaptive way. The center of this region is considered as the pupil center. The experimental results show ninety one percent's accuracy in detecting pupil center.

/pdf/automatic-adaptive-center-of-pupil-detection-using-face-23br5g7uz1.pdf

Automatic Adaptive Center of Pupil Detection Using Face Detection and CDF Analysis

Fast Zernike wavelet moments for Farsi character recognition

This paper presents the application of Wavelet Transform and Genetic Algorithm in a novel steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete Wavelet Transform coefficients in 4x4 blocks on the cover image. The optimal pixel adjustment process is applied after embedding the message. We utilize the frequency domain to improve the robustness of steganography and, we implement Genetic Algorithm and Optimal Pixel Adjustment Process to obtain an optimal mapping function to reduce the difference error between the cover and the stego-image, therefore improving the hiding capacity with low distortions. Our Simulation results reveal that the novel scheme outperforms adaptive steganography technique based on wavelet transform in terms of peak signal to noise ratio and capacity, 39.94 dB and 50% respectively.

/pdf/high-capacity-image-steganography-usingwavelet-transform-and-1mw2avd4n6.pdf

Jamshid Shanbehzadeh

Papers

Image retrieval based on shape similarity by edge orientation autocorrelogram

An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors

Automatic Adaptive Center of Pupil Detection Using Face Detection and CDF Analysis

Fast Zernike wavelet moments for Farsi character recognition

High Capacity Image Steganography usingWavelet Transform and Genetic Algorithm