•We present a feature learning scheme for skin lesion image segmentation.•Negative Matrix Factorization is used to generate an initial dictionary and feature set.•A subset of the dictionary atoms is ...selected to improve compactness and representation.•Normalized Graph Cuts and the learned features are used to segment the input skin lesion image.•The method potentially can be reliable based on experiments and method comparisons.
Pre-screening systems for the diagnosis of melanocytic skin lesions depend of the proper segmentation of the image region affected by the lesion. This paper proposes a feature learning scheme that finds relevant features for skin lesion image segmentation. This work introduces a new unsupervised dictionary learning method, namely Unsupervised Information-Theoretic Dictionary Learning (UITDL), and discusses how it can be applied in the segmentation of skin lesions in macroscopic images. The UITDL approach is adaptive and tends to be robust to outliers in the training data, and consists of two main stages. In the first stage, a textural variation image is used to construct an initial feature dictionary and an initial sparse representation via Non-Negative Matrix Factorization (NMF). In the second stage, the feature dictionary is optimized by selecting adaptively the number of dictionary atoms. The greedy approach used for dictionary optimization is quite efficient and flexible enough to be applied to other dictionary learning problems. Furthermore, the proposed method can be easily extended for other image segmentation problems. The experimental results suggest that the proposed approach potentially can provide more accurate skin lesion segmentation results than comparable state-of-the-art methods. The proposed segmentation method could help to improve the performance of pre-screening systems for melanocytic skin lesions, which can affect positively the quality of the early diagnosis provided to skin lesion patients.
Vision-based human activity recognition (HAR) finds its application in many fields such as video surveillance, robot navigation, telecare and ambient intelligence. Most of the latest researches in ...the field of automated HAR based on skeleton data use depth devices such as Kinect to obtain three-dimensional (3D) skeleton information directly from the camera. Although these researches achieve high accuracy but are strictly device dependent and cannot be used for videos other than from specific cameras. Current work focuses on the use of only 2D skeletal data, extracted from videos obtained through any standard camera, for activity recognition. Appearance and motion features were extracted using 2D positions of human skeletal joints through OpenPose library. The approach was trained and tested on publically available datasets. Supervised machine learning was implemented for recognising four activity classes including sit, stand, walk and fall. Performance of five techniques including K-nearest neighbours (KNNs), support vector machine, Naive Bayes, linear discriminant and feed-forward back-propagation neural network was compared to find the best classifier for the proposed method. All techniques performed well with best results obtained through the KNN classifier.
Pigmented melanocytic skin lesion pre-screening relies on the proper segmentation of the image regions affected by the skin lesion. This paper proposes a new pigmented melanocytic skin lesion ...segmentation algorithm for standard camera images. It is assumed that only one skin lesion is in each input image, and also is assumed that the skin lesion is placed at (or close to) the image center. Thus, the input is, at first, shading attenuated, and then converted to a three-channel color space that enhances the discrimination between healthy and unhealthy skin regions. Afterwards, a dictionary is generated for each image, which is compact and reconstructive, and represents the image patches. This dictionary is obtained in an unsupervised manner using a modified version of the Information-Theoretic Dictionary Learning (ITDL) method, which was originally proposed as supervised dictionary learning method. Normalized Graph Cuts is used to partition the set of projected patches in two groups, resulting in a binary mask that labels the pixels as corresponding to healthy or unhealthy image regions. Our preliminary experimental results obtained on a publicly available dataset are encouraging, and suggest that the proposed pigmented melanocytic skin lesion segmentation method provides, in average, a lower segmentation error rate than comparable state-of-the-art methods proposed in the literature.
This paper introduces a game-based approach to stroke rehabilitation, specifically highlighting a trigger mechanism that utilizes a standard camera within an in-home rehabilitation system. The ...trigger mechanism is implemented using the MediaPipe framework, which incorporates pose estimation techniques, and a adaptive threshold adjustment. The focus is on exergaming actions designed for stroke rehabilitation, taking the example of Stroke Runner, which involves movement and pose control for game interaction. Additionally, the paper discusses the performance report generated to assess and reflect the rehabilitation progress.
Abstract This paper describes a new method for classifying pigmented skin lesions as benign or malignant. The skin lesion images are acquired with standard cameras, and our method can be used in ...telemedicine by non-specialists. Each acquired image undergoes a sequence of processing steps, namely: (1) preprocessing, where shading effects are attenuated; (2) segmentation, where a 3-channel image representation is generated and later used to distinguish between lesion and healthy skin areas; (3) feature extraction, where a quantitative representation for the lesion area is generated; and (4) lesion classification, producing an estimate if the lesion is benign or malignant (melanoma). Our method was tested on two publicly available datasets of pigmented skin lesion images. The preliminary experimental results are promising, and suggest that our method can achieve a classification accuracy of 96.71%, which is significantly better than the accuracy of comparable methods available in the literature.
► We show how melanin features can be estimated using standard camera images. ► We propose a scheme for skin lesion image assignment to malignant/benign classes. ► Our method reduces the number of ...false positives and false negatives. ► Experiments suggest highly accurate results with adequate training.
In this paper, we propose a novel approach to discriminate malignant melanomas and benign atypical nevi, since both types of melanocytic skin lesions have very similar characteristics. Recent studies involving the non-invasive diagnosis of melanoma indicate that the concentrations of the two main classes of melanin present in the human skin, eumelanin and pheomelanin, can potentially be used in the computation of relevant features to differentiate these lesions. So, we describe how these features can be estimated using only standard camera images. Moreover, we demonstrate that using these features in conjunction with features based on the well known ABCD rule, it is possible to achieve 100% of sensitivity and more than 99% accuracy in melanocytic skin lesion discrimination, which is a highly desirable characteristic in a prescreening system.
Radial Multi-focal Tensors Thirthala, SriRam; Pollefeys, Marc
International journal of computer vision,
01/2012, Letnik:
96, Številka:
2
Journal Article
Recenzirano
The 1D radial camera maps all points on a plane, containing the principal axis, onto the radial line which is the intersection of that plane and the image plane. It is a sufficiently general model to ...express both central and non-central cameras, since the only assumption it makes is of known center of distortion. In this paper, we study the multi-focal tensors arising out of 1D radial cameras. There exist no two-view constraints (like the fundamental matrix) for 1D radial cameras. However, the 3-view and 4-view cases are interesting. For the 4-view case we have the radial quadrifocal tensor, which has 15 d.o.f and 2 internal constraints. For the 3-view case, we have the radial trifocal tensor, which has 7 d.o.f and
no internal constraints
. Under the assumption of a purely rotating central camera, this can be used to do a
non-parametric estimation
of the radial distortion of a 1D camera. Even in the case of a non-rotating camera it can be used to do parametric estimation, assuming a planar scene. Finally we examine the mixed trifocal tensor, which models the case of two 1D radial cameras and one standard pin-hole camera. Of the above radial multifocal tensors, only the radial trifocal tensor is useful practically, since it doesn’t require any knowledge of the scene and is extremely robust. We demonstrate results based on real-images for this.
For the quadrifocal tensor, too, we present a way to do a metric reconstruction of the scene and to undistort the image (given a sufficiently dense set of point-correspondences). We also show results on synthetic images. However, it must be noted that currently the quadrifocal and mixed trifocal tensors are useful
only
from a theoretical stand-point.
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