IEEE International Conference on Image Processing

In this paper, we present a novel constrained variational principle for simultaneous smoothing and estimation of the diffusion tensor field from complex valued diffusion-weighted images (DWI). The constrained variational principle involves the minimization of a regularization term of L/sup p/ norms, subject to a nonlinear inequality constraint on the data. The data term we employ is the original Stejskal-Tanner equation instead of the linearized version usually employed in literature. The complex valued nonlinear form leads to a more accurate (when compared to the linearized version) estimate of the tensor field. The inequality constraint requires that the nonlinear least squares data term be bounded from above by a known tolerance factor. Finally, in order to accommodate the positive definite constraint on the diffusion tensor, it is expressed in terms of Cholesky factors and estimated. The constrained variational principle is solved using the augmented Lagrangian technique in conjunction with the limited memory quasi-Newton method. Experiments with complex-valued synthetic and real data are shown to depict the performance of our tensor field estimation and smoothing algorithm.

A Constrained Variational Principle for Direct Estimation and Smoothing of The Diffusion Tensor Field from Complex DWI

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions.

Ensemble deep learning: A review.

Touchless palmprint recognition systems enable high-accuracy recognition of individuals through less-constrained and highly usable procedures that do not require the contact of the palm with a surface. To perform this recognition, methods based on local texture descriptors and convolutional neural networks (CNNs) are currently used to extract highly discriminative features while compensating for variations in scale, rotation, and illumination in biometric samples. In particular, the main advantage of CNN-based methods is their ability to adapt to biometric samples captured with heterogeneous devices. However, the current methods rely on either supervised training algorithms, which require class labels (e.g., the identities of the individuals) during the training phase, or filters pretrained on general-purpose databases, which may not be specifically suitable for palmprint data. To achieve a high-recognition accuracy with touchless palmprint samples captured using different devices while neither requiring class labels for training nor using pretrained filters, we introduce PalmNet, which is a novel CNN that uses a newly developed method to tune palmprint-specific filters through an unsupervised procedure based on Gabor responses and principal component analysis (PCA), not requiring class labels during training. PalmNet is a new method of applying Gabor filters in a CNN and is designed to extract highly discriminative palmprint-specific descriptors and to adapt to heterogeneous databases. We validated the innovative PalmNet on several palmprint databases captured using different touchless acquisition procedures and heterogeneous devices, and in all cases, a recognition accuracy greater than that of the current methods in this paper was obtained.

/pdf/palmnet-gabor-pca-convolutional-networks-for-touchless-4y22nlobms.pdf

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

/pdf/ensemble-deep-learning-a-review-16rga6gz.pdf

Ensemble deep learning: A review

This paper devises a new means of filter diversification, dubbed multi-fold filter convolution ( $\mathcal {M}$ -FFC), for face recognition. On the assumption that $\mathcal {M}$ -FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by $\mathcal {M}$ -fold to instantiate a filter offspring set. The $\mathcal {M}$ -FFC flexibility also permits cross convolution amongst Gabor filters and other filter banks of profoundly dissimilar traits, e.g., principal component analysis (PCA) filters and independent component analysis (ICA) filters. The 2-FFC of Gabor, PCA, and ICA filters thus yields three offspring sets: 1) Gabor filters solely; 2) Gabor-PCA filters; and 3) Gabor-ICA filters, to render the learning-free and the learning-based 2-FFC descriptors. To facilitate a sensible Gabor filter selection for $\mathcal {M}$ -FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into eight elementary filters. Aside from that, an average histogram pooling operator is employed to leverage the 2-FFC histogram features, prior to the final whitening PCA compression. The empirical results substantiate that the 2-FFC descriptors prevail over, or on par with, other face descriptors on both identification and verification tasks.

Multi-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition

Hand gesture provides a means for human to interact through a series of gestures. While hand gesture plays a significant role in human–computer interaction, it also breaks down the communication barrier and simplifies communication process between the general public and the hearing-impaired community. This paper outlines a convolutional neural network (CNN) integrated with spatial pyramid pooling (SPP), dubbed CNN–SPP, for vision-based hand gesture recognition. SPP is discerned mitigating the problem found in conventional pooling by having multi-level pooling stacked together to extend the features being fed into a fully connected layer. Provided with inputs of varying sizes, SPP also yields a fixed-length feature representation. Extensive experiments have been conducted to scrutinize the CNN–SPP performance on two well-known American sign language (ASL) datasets and one NUS hand gesture dataset. Our empirical results disclose that CNN–SPP prevails over other deep learning-driven instances.

Convolutional neural network with spatial pyramid pooling for hand gesture recognition

Biometric watermarking was introduced as the synergistic integration of biometrics and digital watermarking technology. This paper proposes a novel biometric watermarking technique, which embeds offline handwritten signature in host image for copyright protection. We propose to combine the conventional LSB-based and DWT-based watermarking techniques into a unison framework, which is known as LSB-DWT in this paper. The proposed LSB-DWT technique is evaluated against various simulated security attacks, i.e. JPEG compression, Gaussian low-pass filtering, median filtering, Gaussian noise, scaling, rotation and cropping. The experimental results demonstrate that the proposed LSB-DWT technique exhibits remarkable watermark imperceptibility and watermark robustness.

/pdf/fusion-of-lsb-and-dwt-biometric-watermarking-using-offline-3gy1sagw84.pdf

Fusion of LSB and DWT Biometric Watermarking Using Offline Handwritten Signature for Copyright Protection

The principal component analysis network (PCANet) is asserted as a parsimonious stacking-based convolutional neural networks (CNNs) instance for generic object recognition including face. However, to be regarded a CNN resemblance, PCANet lacks a nonlinearity in between two successive convolutional layers. The multilayer PCANet (by neglecting the nonlinearity pre-requisite) is also deemed far-fetched for the network depth beyond two, due to feature dimensionality explosion. We thus devise a PCANet alternative, dubbed PCANet+ in this letter, to untangle these constraints. To be more precise, conforming to the CNN essentials, PCANet+ conveys a mean-pooling unit manipulating each feature map. On top of that, we streamline the PCANet topology to permit a deep construction with an expanded PCA filter ensemble. We scrutinize the PCANet+ performance using face recognition technology and other two faces in the wild datasets, namely, labeled faces in the wild and YouTube faces. The experimental results reveal that the PCANet+ descriptor prevails over its predecessor and other stacking-based descriptors in face identification and verification, serving a baseline for ConvNets.

Stacking PCANet +: An Overly Simplified ConvNets Baseline for Face Recognition

Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules, one after another, to synthesize a deep neural network (DNN) alternative for pattern classification. Contrary to the DNNs trained from end to end by backpropagation (BP), each S-DNN layer, that is, a self-learnable module, is to be trained decisively and independently without BP intervention. In this paper, a ridge regression-based S-DNN, dubbed deep analytic network (DAN), along with its kernelization (K-DAN), are devised for multilayer feature relearning from the pre-extracted baseline features and the structured features. Our theoretical formulation demonstrates that DAN/K-DAN relearn by perturbing the intra/interclass variations, apart from diminishing the prediction errors. We scrutinize the DAN/K-DAN performance for pattern classification on datasets of varying domains—faces, handwritten digits, generic objects, to name a few. Unlike the typical BP-optimized DNNs to be trained from gigantic datasets by GPU, we reveal that DAN/K-DAN are trainable using only CPU even for small-scale training sets. Our experimental results show that DAN/K-DAN outperform the present S-DNNs and also the BP-trained DNNs, including multiplayer perceptron, deep belief network, etc., without data augmentation applied.

Cheng-Yaw Low

Papers

Multi-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition

Convolutional neural network with spatial pyramid pooling for hand gesture recognition

Fusion of LSB and DWT Biometric Watermarking Using Offline Handwritten Signature for Copyright Protection

Stacking PCANet +: An Overly Simplified ConvNets Baseline for Face Recognition

Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification