Detection and Spatial Analysis of Hepatic Steatosis in Histopathology Images using Sparse Linear Models

International audienceHepatic steatosis is a defining feature of nonalco-holic fatty liver disease, emerging with the increasing incidence of obesity and metabolic syndrome. The research in image-based analysis of hepatic steatosis mostly focuses on the quantification of fat in biopsy images. This work furthers the image-based analysis of hepatic steatosis by exploring the spatial characteristics of fat globules in whole slide biopsy images after performing fat detection. An algorithm based on morphological filtering and sparse linear models is presented for fat detection. Then the spatial properties of detected fat globules in relation to the hepatic anatomical structures of central veins and portal tracts are explored. The test dataset consists of 38 high resolution images from 21 patients. The experimental results provide an insight into the size distributions of fat globules and their location with respect to the anatomical structures

MODELING AND ANALYSIS OF WRINKLES ON AGING HUMAN FACES

The analysis and modeling of aging human faces has been extensively studied in the past decade. Most of this work is based on matching learning techniques focused on appearance of faces at different ages incorporating facial features such as face shape/geometry and patch-based texture features. However, we do not find much work done on the analysis of facial wrinkles in general and specific to a person. The goal of this dissertation is to analyse and model facial wrinkles for different applications. Facial wrinkles are challenging low-level image features to analyse. In general, skin texture has drastically varying appearance due to its characteristic physical properties. A skin patch looks very different when viewed or illuminated from different angles. This makes subtle skin features like facial wrinkles difficult to be detected in images acquired in uncontrolled imaging settings. In this dissertation, we examine the image properties of wrinkles i.e. intensity gradients and geometric properties and use them for several applications including low-level image processing for automatic detection/localization of wrinkles, soft biometrics and removal of wrinkles using digital inpainting. First, we present results of detection/localization of wrinkles in images using Marked Point Process (MPP). Wrinkles are modeled as sequences of line segments in a Bayesian framework which incorporates a prior probability model based on the likely geometric properties of wrinkles and a data likelihood term based on image intensity gradients. Wrinkles are localized by sampling the posterior probability using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We also present an evaluation algorithm to quantitatively evaluate the detection and false alarm rate of our algorithm and conduct experiments with images taken in uncontrolled settings. The MPP model, despite its promising localization results, requires a large number of iterations in the RJMCMC algorithm to reach global minimum resulting in considerable computation time. This motivated us to adopt a deterministic approach based on image morphology for fast localization of facial wrinkles. We propose image features based on Gabor filter banks to highlight subtle curvilinear discontinuities in skin texture caused by wrinkles. Then, image morphology is used to incorporate geometric constraints to localize curvilinear shapes of wrinkles at image sites of large Gabor filter responses. We conduct experiments on two sets of low and high resolution images to demonstrate faster and visually better localization results as compared to those obtained by MPP modeling. As a next application, we investigate the user-drawn and automatically detected wrinkles as a pattern for their discriminative power as a soft biometrics to recognize subjects from their wrinkle patterns only. A set of facial wrinkles from an image is treated as a curve pattern and used for subject recognition. Given the wrinkle patterns from a query and gallery images, several distance measures are calculated between the two patterns to quantify the similarity between them. This is done by finding the possible correspondences between curves from the two patterns using a simple bipartite graph matching algorithm. Then several metrics are used to calculate the similarity between the two wrinkle patterns. These metrics are based on Hausdorff distance and curve-to-curve correspondences. We conduct experiments on data sets of both hand drawn and automatically detected wrinkles. Finally, we apply digital inpainting to automatically remove wrinkles from facial images. Digital image inpainting refers to filling in the holes of arbitrary shapes in images so that they seem to be part of the original image. The inpainting methods target either the structure or the texture of an image or both. There are two limitations of existing inpainting methods for the removal of wrinkles. First, the differences in the attributes of structure and texture requires different inpainting methods. Facial wrinkles do not fall strictly under the category of structure or texture and can be considered as some where in between. Second, almost all of the image inpainting techniques are supervised i.e. the area/gap to be filled is provided by user interaction and the algorithms attempt to find the suitable image portion automatically. We present an unsupervised image inpainting method where facial regions with wrinkles are detected automatically using their characteristic intensity gradients and removed by painting the regions by the surrounding skin texture

Image-based evaluation of treatment responses of facial wrinkles using LDDMM registration and Gabor features

International audienceThis paper presents image-based quantitative evaluation of subtle variations in facial wrinkles for the same subject in response to a dermatological treatment. This is a novel application because the time series images of the same subject over a shorter time period of weeks are analyzed as compared to more prevalent inter-person analysis of facial skin/marks. We propose image features based on Gabor filter bank for an accurate quantitative evaluation of variations in facial wrinkles. Since variations in Gabor features are very small on a time period of weeks, we propose a framework to compare image features in key wrinkle sites only while excluding the noise introduced by non-wrinkle sites. The framework consists of finer registration of images using Large Deformation Diffeo-morphic Metric Mapping (LDDMM) and detection of wrinkle sites using Gabor filter bank and morphological image processing. Preliminary experiments show that the framework is useful in calculating variations in Gabor features at detected sites and indicating trends in the response of facial wrinkles to the dermatological treatment

RMP: A Random Mask Pretrain Framework for Motion Prediction

As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving. In this paper, we propose a framework to formalize the pretraining task for trajectory prediction of traffic participants. Within our framework, inspired by the random masked model in natural language processing (NLP) and computer vision (CV), objects' positions at random timesteps are masked and then filled in by the learned neural network (NN). By changing the mask profile, our framework can easily switch among a range of motion-related tasks. We show that our proposed pretraining framework is able to deal with noisy inputs and improves the motion prediction accuracy and miss rate, especially for objects occluded over time by evaluating it on Argoverse and NuScenes datasets.Comment: IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

Modeling of Facial Wrinkles for Applications in Computer Vision

International audienceAnalysis and modeling of aging human faces have been extensively studied in the past decade for applications in computer vision such as age estimation, age progression and face recognition across aging. Most of this research work is based on facial appearance and facial features such as face shape, geometry, location of landmarks and patch-based texture features. Despite the recent availability of higher resolution, high quality facial images, we do not find much work on the image analysis of local facial features such as wrinkles specifically. For the most part, modeling of facial skin texture, fine lines and wrinkles has been a focus in computer graphics research for photo-realistic rendering applications. In computer vision, very few aging related applications focus on such facial features. Where several survey papers can be found on facial aging analysis in computer vision, this chapter focuses specifically on the analysis of facial wrinkles in the context of several applications. Facial wrinkles can be categorized as subtle discontinuities or cracks in surrounding inhomogeneous skin texture and pose challenges to being detected/localized in images. First, we review commonly used image features to capture the intensity gradients caused by facial wrinkles and then present research in modeling and analysis of facial wrinkles as aging texture or curvilinear objects for different applications. The reviewed applications include localization or detection of wrinkles in facial images , incorporation of wrinkles for more realistic age progression, analysis for age estimation and inpainting/removal of wrinkles for facial retouching

A Markov Point Process model for wrinkles in human faces

International audienceIn this paper we present a new generative model for wrinkles on aging human faces based on Markov Point Processes (MPP) where wrinkles are considered as stochastic spatial arrangements of sequences of line segments. The model is then used in a Bayesian framework to localize the wrinkles in images. In aging human faces, wrinkles mostly appear as discontinuities in surrounding grayscale texture. The intensity gradients due to wrinkles are enhanced using filters and used as data to detect more probable locations and directions of line segments. Wrinkles are localized by sampling MPP using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. Experiments on images obtained from uncontrolled acquisition conditions are presented

Detection and Inpainting of Facial Wrinkles Using Texture Orientation Fields and Markov Random Field Modeling

International audienceFacial retouching is widely used in media and entertainment industry. Professional software usually require a minimum level of user expertise to achieve the desirable results. In this paper, we present an algorithm to detect facial wrinkles/imperfection. We believe that any such algorithm would be amenable to facial retouching applications. The detection of wrinkles/imperfections can allow these skin features to be processed differently than the surrounding skin without much user interaction. For detection, Gabor filter responses along with texture orientation field are used as image features. A bi-modal Gaussian mixture model (GMM) represents distributions of Gabor features of normal skin vs. skin imperfections. Then a Markov random field model (MRF) is used to incorporate the spatial relationships among neighboring pixels for their GMM distributions and texture orientations. An Expectation-Maximization (EM) algorithm then classifies skin vs. skin wrinkles/imperfections. Once detected automatically, wrinkles/imperfections are removed completely instead of being blended or blurred. We propose an exemplar-based constrained texture synthesis algorithm to inpaint irregularly shaped gaps left by the removal of detected wrinkles/imperfections. We present results conducted on images downloaded from the Internet to show the efficacy of our algorithms

Fast detection of facial wrinkles based on Gabor features using image morphology and geometric constraints

International audienceFacial wrinkles are important features of aging human skin which can be incorporated in several image-based applications related to aging. Facial wrinkles are 3D features of skin and appear as subtle discontinuities or cracks in surrounding skin texture. However, facial wrinkles can easily be masked by illumination/acquisition conditions in 2D images due to the specific nature of skin surface texture and its reflective properties. Existing approaches to image-based analysis of aging skin are based on analysis of wrinkles as texture and not as curvilinear discontinuity/crack features. Previously, we proposed a stochastic approach based on Marked Point Processes (MPP) to localize facial wrinkles as curves. In this paper, we present a fast deterministic algorithm based on Gabor filters and image morphology to improve localization results. We propose image features based on Gabor filter bank to highlight the subtle curvilinear discontinuities in skin texture caused by wrinkles. Then, image morphology is used to incorporate geometric constraints to localize curvilinear shapes of wrinkles at image sites of large Gabor filter responses. Experiments are conducted on two sets of low and high resolution images and results are compared with those of MPP modeling. Experiments show that not only the proposed algorithm is significantly faster than MPP-based approach but also provides visually better results

Modeling and Detection of Wrinkles in Aging Human Faces Using Marked Point Processes

International audienceIn this paper we propose a new generative model for wrinkles on aging human faces using Marked Point Processes (MPP). Wrinkles are considered as stochastic spatial arrangements of sequences of line segments, and detected in an image by proper localization of line segments. The intensity gradients are used to detect more probable lo-cations and a prior probability model is used to constrain properties of line segments. Wrinkles are localized by sampling MPP using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We also present an evaluation setup to measure the performance of the pro-posed model. We present results on a variety of images obtained from the Internet to illustrate the performance of the proposed model

Assessment of facial wrinkles as a soft biometrics

International audienceThis paper presents results on the assessment of facial wrinkles as a soft biometrics. Recently, several micro features such as moles, scars, freckles, etc. have been used in addition to more common facial features for face recognition. The discriminative power of facial wrinkles has not been evaluated. In this paper we present results of our experiments on assessment of discriminative power of wrinkles in recognizing subjects. We treat a set of facial wrinkles from an image as a curve pattern and find similarity between curve patterns from two subjects. Several metrics based on Hausdorff distance and curve-to-curve correspondences are introduced to quantify the similarity. A simple bipartite graph matching algorithm is introduced to find correspondences between curves from two patterns. We present experiments on data sets using manually extracted and automatically detected wrinkles. The recognition rate for these data sets using only the binary forehead wrinkle curve patterns exceeds 65% at rank 1 and 90% at rank 4