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The amount of data produced every day on the internet increases every day and with the increasing popularity of the social networks the number of published photos are huge, and those pictures contain several implicit or explicit brand logos. Detecting this logos in natural images can provide information about how widespread is a brand, discover unwanted copyright distribution, analyze marketing campaigns,...
This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for contentaware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outpues a retargeted image. Retargeting is performed through a shift reap, which is a pixet-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which...
Recent advances in the joint processing of images have certainly shown its advantages over the individual processing. Different from the existing works geared towards co-segmentation or co-localization, in this paper, we explore a new joint processing topic: co-skeletonization, which is defined as joint skeleton extraction of common objects in a set of semantically similar images. Object skeletonization...
Accurate cell detection is often an essential prerequisite for subsequent cellular analysis in computer aided diagnosis (CAD) for histopathlogy images. It is challenging due to high cell density, touching cells, low contrast, variant cell shapes and sizes, weak boundaries and the use of different image acquisition techniques. Existing methods are often struggling at tackling with the challenges at...
In this paper, we present a new benchmark (Menpo benchmark) for facial landmark localisation and summarise the results of the recent competition, so-called Menpo Challenge, run in conjunction to CVPR 2017. The Menpo benchmark, contrary to the previous benchmarks such as 300-W and 300-VW, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup...
One of the most important prognostic markers to assess proliferation activity of breast tumors is estimating the number of mitotic figures in H&E stained tissue. We propose the use of a recently published convolutional neural network architecture, Wide Residual Networks, for mitosis detection in breast histology images. The model is trained to classify each pixel of on an image using as context...
We introduce a new fully automated breast mass segmentation method from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The method is based on globally optimal inference in a continuous space (GOCS) using a shape prior computed from a semantic segmentation produced by a deep learning (DL) model. We propose this approach because the limited amount of annotated training samples does...
The detection of cells and nuclei is a crucial step for the automatic analysis of digital pathology slides and as such for the quantification of the phenotypic information contained in tissue sections. This task is however challenging because of high variability in size, shape and textural appearance of the objects to be detected and of the high variability of tissue appearance. In this work, we propose...
Densely sampled dynamic geophysical data are often modeled using principal components analysis (PCA, a.k.a. empirical orthogonal function or EOF analysis) to provide constraints for their inversion with remote sensing techniques. We show that overcomplete sparsifying dictionaries, generated using dictionary learning, provide a more informative basis for geophysical signal representation. Relative...
Accurate localization of the left ventricle (LV) boundary from echocardiogram images is of vital importance for the diagnosis and treatment of heart disease. Statistical shape models such as active shape models (ASM) have been commonly used to perform automatic detection of this boundary. Such mod- els perform well when there is low variability in the underlying shape subspace and an accurate initialization...
Recent advances in deep learning methodologies are enabling the construction of more accurate classifiers. However, existing labeled face datasets are limited in size, which prevents CNN models from reaching their full generalization capabilities. A variety of techniques to generate new training samples based on data augmentation have been proposed, but the great majority is limited to very simple...
Automatically recognizing pornographic images from the Web is a vital step to purify Internet environment. Inspired by the rapid developments of deep learning models, we present a deep architecture of convolutional neural network (CNN) for high accuracy pornographic image recognition. The proposed architecture is built upon existing CNNs which accepts input images of different sizes and incorporates...
Significant progress has been made in recent years for computer-aided diagnosis of abnormal pulmonary textures from computed tomography (CT) images. Similar initiatives in chest radiographs (CXR), the common modality for pulmonary diagnosis, are much less developed. CXR are fast, cost effective and low-radiation solution to diagnosis over CT. However, the subtlety of textures in CXR makes them hard...
In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework. Especially, we address the issue of segmenting the image into regions to be classified using pre-trained deep neural networks as feature extractors for an SVM-based classifier. An efficient segmentation as a preprocessing step helps learning...
The widespread use of video recording devices to obtain recordings of people in various scenarios makes the problem of privacy protection increasingly important. Consequently, there is an increased interest in developing methods for de-identification, i.e. removing personally identifying features from publicly available or stored data. Most of related work focuses on de-identifying hard biometric...
It is important to choose a good hairstyle for women because it can enhance their beauty, personality, and confidence. One of the most important factors to consider for choosing the right hairstyle is the individuals face shape. An effective face shape classification can be used for constructing a hairstyle recommendation system. This paper presents a classification approach that divides face shapes...
This paper presents an accurate and automatic algorithm to recognize and count fish in the video footages of fishery operations. The unique character of the approach is that it combines machine learning techniques with statistical methods to fully make use the benefits of these algorithms. The approach consists of three major stages including video data preparation such as noise deduction, preliminary...
In this paper we address the challenge of performing face recognition on human faces that are wearing glasses. This is a common problem for face recognition and automatic identity checking at airports, as passengers frequently forget to remove their glasses when passing through customs. In order to solve this problem, we first propose an automatic glasses presence detection model based on the tree-pictorial-structured...
In this paper, we propose a new method for the segmentation of breast masses from mammograms using a conditional random field (CRF) model that combines several types of potential functions, including one that classifies image regions using deep learning. The inference method used in this model is the tree re-weighted (TRW) belief propagation, which allows a learning mechanism that directly minimizes...
We propose a machine learning-based method to automatically detect flow diverters in cerebral C-arm CT images. An appearance detector is learned to generate hypotheses of a flow diverter's location in a volumetric image. A probabilistic framework incorporating a local appearance and shape model is developed to trace the flow diverter. Promising results have been obtained on clinical data. The proposed...
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