Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Sunyaev-Zel'dovich clusters reconstruction in multiband bolometer camera surveys

We present a new method for the reconstruction of Sunyaev-Zel'dovich (SZ) galaxy clusters in future SZ-survey experiments using multiband bolometer cameras such as Olimpo, APEX, or Planck. Our goal is to optimise SZ-Cluster extraction from our observed noisy maps. We wish to emphasize that none of the algorithms used in the detection chain is tuned on prior knowledge on the SZ -Cluster signal, or other astrophysical sources (Optical Spectrum, Noise Covariance Matrix, or covariance of SZ Cluster wavelet coefficients). First, a blind separation of the different astrophysical components which contribute to the observations is conducted using an Independent Component Analysis (ICA) method. Then, a recent non linear filtering technique in the wavelet domain, based on multiscale entropy and the False Discovery Rate (FDR) method, is used to detect and reconstruct the galaxy clusters. Finally, we use the Source Extractor software to identify the detected clusters. The proposed method was applied on realistic simulations of observations. As for global detection efficiency, this new method is impressive as it provides comparable results to Pierpaoli et al. method being however a blind algorithm. Preprint with full resolution figures is available at the URL: w10-dapnia.saclay.cea.fr/Phocea/Vie_des_labos/Ast/ast_visu.php?id_ast=728Comment: Submitted to A&A. 32 Pages, text onl

REMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval

This paper addresses the problem of very large-scale image retrieval, focusing on improving its accuracy and robustness. We target enhanced robustness of search to factors such as variations in illumination, object appearance and scale, partial occlusions, and cluttered backgrounds - particularly important when search is performed across very large datasets with significant variability. We propose a novel CNN-based global descriptor, called REMAP, which learns and aggregates a hierarchy of deep features from multiple CNN layers, and is trained end-to-end with a triplet loss. REMAP explicitly learns discriminative features which are mutually-supportive and complementary at various semantic levels of visual abstraction. These dense local features are max-pooled spatially at each layer, within multi-scale overlapping regions, before aggregation into a single image-level descriptor. To identify the semantically useful regions and layers for retrieval, we propose to measure the information gain of each region and layer using KL-divergence. Our system effectively learns during training how useful various regions and layers are and weights them accordingly. We show that such relative entropy-guided aggregation outperforms classical CNN-based aggregation controlled by SGD. The entire framework is trained in an end-to-end fashion, outperforming the latest state-of-the-art results. On image retrieval datasets Holidays, Oxford and MPEG, the REMAP descriptor achieves mAP of 95.5%, 91.5%, and 80.1% respectively, outperforming any results published to date. REMAP also formed the core of the winning submission to the Google Landmark Retrieval Challenge on Kaggle.Comment: Submitted to IEEE Trans. Image Processing on 24 May 2018, published 22 May 201

IVGPR: A New Program for Advanced End-To-End GPR Processing

Ground penetrating radar (GPR) processing workflows commonly rely on techniques developed particularly for seismic reflection imaging. Although this practice has produced an abundance of reliable results, it is limited to basic applications. As the popularity of GPR continues to surge, a greater number of complex studies demand the use of routines that take into account the unique properties of GPR signals. Such is the case of surveys that examine the material properties of subsurface scatterers. The nature of these complicated tasks have created a demand for GPR-specific processing packages flexible enough to tackle new applications. Unlike seismic processing programs, however, GPR counterparts often afford only a limited amount of functionalities. This work produced a new GPR-specific processing package, dubbed IVGPR, that offers over 60 fully customizable procedures. This program was built using the modern Fortran programming language in combination with serial and parallel optimization practices that allow it to achieve high levels of performance. Within its many functions, IVGPR provides the rare opportunity to apply a three-dimensional single-component vector migration routine. This could be of great value for advanced workflows designed to develop and test new true-amplitude and inversion algorithms. Numerous examples given through this work demonstrate the effectiveness of key routines in IVGPR. Additionally, three case studies show end-to-end applications of this program to field records that produced satisfactory result well-suited interpretatio

OPTIMIZED BIOMETRIC SYSTEM BASED ON COMBINATION OF FACE IMAGES AND LOG TRANSFORMATION

The biometrics are used to identify a person effectively. In this paper, we propose optimised Face recognition system based on log transformation and combination of face image features vectors. The face images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation is applied on enhanced image to generate features. The feature vectors of many images of a single person image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is used to compare test image feature vector with database feature vectors to identify a person. It is experimented that, the performance of proposed algorithm is better compared to existing algorithms

Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography

Abstract. Osteoarthritis (OA) is a joint disease affecting hundreds of millions of people worldwide. In basic research, accurate ex vivo measures are needed for assessing OA severity. The standard method for this is the histopathological grading of stained thin tissue sections. However, the methods are destructive, time-consuming, do not describe the full sample volume and provide subjective results. Contrast-enhanced micro-computed tomography (CEμCT) -based grading with phosphotungstic acid -stain was previously developed to address some of these issues. Aim of this study was to investigate the possibility of automating this process. Osteochondral tissue cores were harvested from total knee arthroplasty patients (n = 34, N = 19, Ø = 2 mm, n = 15, N = 5, Ø = 4 mm) and asymptomatic cadavers (n = 30, N = 2, Ø = 4 mm). Samples were imaged with CEμCT, reconstructed and graded manually. Subsequently, the reconstructions were loaded into an ad hoc developed Python software, where volumes-of-interest (VOI) were extracted from different cartilage zones: surface zone (SZ), deep zone (DZ) and calcified zone (CZ) and collapsed into two-dimensional texture images. Normalized images underwent Median Robust Extended Local Binary Pattern (MRELBP) -algorithm to extract the features, with subsequent dimensionality reduction. Ridge and logistic regression models were trained with L2 regularization against the ground truth for the small samples (Ø = 2 mm) using leave-one-patient-out cross-validation. Trained models were then evaluated on the large samples (Ø = 4 mm). Performance of the models were assessed using Spearman’s correlation, Area under the Receiver Operating Characteristic Curve (AUC) and Average Precision (AP). Highest performance on both models was for the SZ. Strong correlation was observed on ridge regression (ρ = 0.68, p < 0.0001), as well as high AUC and AP values for the logistic regression (AUC = 0.92, AP = 0.89) for the small samples. Using the large samples, similar findings were observed with slightly reduced values (ρ = 0.55, p = 0.0001, AUC = 0.86, AP = 0.89). Moderate results were observed for CZ and DZ models (ρ = 0.54 and 0.38, AUC = 0.77 and 0.72, AP = 0.71 and 0.50, respectively). Evaluation on the large samples resulted in performance decrease on CZ models (ρ = 0.29, AUC = 0.63, AP = 0.62), while surprisingly performance increased on DZ logistic regression model (ρ = 0.34, AUC = 0.72, AP = 0.83). Obtained results indicate that automating the 3D CEμCT histopathological grading is feasible. However, with low number of samples, models are better suited for binary detection of sample degenerative features, rather than predicting a detailed grade. To facilitate model generalization on new data, similar data acquisition protocol should be used on all samples. The proposed methods have potential to aid OA researchers and pathologists in 3D histopathological grading, introducing more objectivity to the grading process. This thesis presents the conducted study in detail, and provides an extensive review related to the osteochondral unit, CEμCT imaging, as well as statistical learning machines