Images undergo a complex processing chain from the moment light reaches the camera's sensor until the final digital image is delivered. Each of its operations leaves traces on the noise model which enable forgery detection through noise analysis. In this article, we describe the Noisesniffer method [Gardella et al., Noisesniffer: a Fully Automatic Image Forgery Detector Based on Noise Analysis, IEEE International Workshop on Biometrics and Forensics, 2021]. This method estimates for each image a background stochastic model which makes it possible to detect local noise anomalies characterized b ..read more

We compare nine line segment detectors. The two more ancient ones are based on classical edge growing followed by a statistically founded validation. The next six are very recent and based on supervised deep learning. These six deep learning methods train and validate their neural network on two datasets ('YorkUrban', 'Wireframe'); most of them compared their results with the now classic LSD (Line Segment Detector) and EDlines, and get a better performance than them on these datasets. The ninth paper combines deep learning and classical edge growing to achieve a purely non-supervised method. T ..read more

In this paper, we present a comprehensive Python program for localizing the point spread functions (PSFs) present in a stack of images and thereby rendering a super-resolved image in a Stochastic Optical Reconstruction Microscopy (STORM). A microscope that provides super-resolved images is known as a super-resolution microscope. Optical super-resolution microscopy is playing a pivotal role in advancing the field of optical imaging and has found applications in a number of areas such as cellular biology, biotechnology, medical research, and nanotechnology. The proposed Python program utilizes i ..read more

Interactive image segmentation (IIS) methods are usually trained over segmentation datasets containing natural images. They are also usually evaluated over natural images. However, the most common use case is the annotation of new images from a different domain. Yet, the performance of IIS methods on a different domain is seldom reported. In this work, we evaluate a state-of-the-art IIS method trained with natural images over an aerial image dataset. Its performance is compared to the performances the method achieves when being trained/finetuned with aerial images. The comparison reveals that ..read more

Interactive image segmentation (IIS) methods are usually evaluated in terms of segmentation performance vs.\ number of clicks (NoC). However, the automatic evaluation depends on a clicking procedure and its relation to the procedure used for training. In this work we compare qualitatively and quantitatively two state-of-the-art IIS methods that report the best performances but have not been compared against each other. We show i) what method is better, ii) that the performance is sensitive to clicking procedures, iii) what method is more robust to clicking procedures, and iv) that training wit ..read more

This article thoroughly describes a data set of 240 multivariate time series collected using 34 Cartesian Optoelectronic Dynamic Anthropometer (CODA) markers placed on the upper limb of 16 healthy subjects each undergoing 15 predefined movements such as raising their arms or combing their hair. Each sensor records its position in the 3D space. In total, 2.5 hours of time series are collected. A remarkable aspect of this data set is the extensive availability of metadata: subjects' characteristics (age, height, etc.) as well as movements' annotations. Indeed, for each subject and each movement ..read more

In this paper, we give the implementation of an image denoising algorithm based on backward stochastic differential equations. In our algorithm, we consider two stochastic processes. One of them has values in the image domain and determines pixels that will be involved in the reconstruction, the second one has values in the image codomain and gives weights to values of pixels. The reconstructed image is characterized by smoothing noisy pixels and at the same time enhancing edges. Our experiments show that the new approach gives very good results and can be successfully used to reconstruct imag ..read more

This article provides a comprehensive description of a dataset consisting of 110 multivariate gait signals collected using three inertial measurement units. The data was obtained from a sample of 19 healthy subjects who followed a predefined protocol: standing still, walking 10 meters, turning around, walking back, and stopping. One notable aspect of this dataset is the inclusion of extensive signal metadata, including the start and end timestamps of each footstep, along with contextual information for each trial. Part of this dataset was previously used to develop and assess a gait event dete ..read more

We propose a block-based signal-dependent noise estimation method on videos, that leverages inter-frame redundancy to separate noise from signal. Block matching is applied to find block pairs between two consecutive frames with similar signal. Then the Ponomarenko et al. method is extended to video by sorting pairs by their low-frequency energy and estimating noise in the high frequencies. Experiments on a real dataset of drone videos show its performance for different parameter settings and different noise levels. Two extensions of the proposed method using subpixel matching and for multiscal ..read more

In this article, we focus on the algorithm called CCO (Constrained Constructive Optimization), initially proposed by Schreiner and Buxbaum [Computer-Optimization of Vascular Trees, IEEE Transactions on Biomedical Engineering, 40, 1993] and further extended by Karch et al. [A Three-Dimensional Model for Arterial Tree Representation, Generated by Constrained Constructive Optimization, Computers in Biology and Medicine, 29, 1999]. This algorithm can be considered as one of the gold standards for vascular tree structure generation. Modeling and/or simulating the morphology of vascular networks is ..read more