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Abstract The high rate of drug withdrawal from the market due to cardiovascular toxicity or lack of efficacy, the economic burden, and extremely long time before a compound reaches the market, have increased the relevance of human in vitro models like human (patient‐derived) pluripotent stem cell (hPSC)‐derived engineered heart tissues (EHTs) for the evaluation of the efficacy and toxicity of compounds at the early phase in the drug development pipeline. Consequently, the EHT contractile properties are highly relevant parameters for the analysis of cardiotoxicity, disease phenotype, and longitudinal measurements of cardiac function over time. In this study, we developed and validated the software HAARTA (Highly Accurate, Automatic and Robust Tracking Algorithm), which automatically analyzes contractile properties of EHTs by segmenting and tracking brightfield videos, using deep learning and template matching with sub‐pixel precision. We demonstrate the robustness, accuracy, and computational efficiency of the software by comparing it to the state‐of‐the‐art method (MUSCLEMOTION), and by testing it with a data set of EHTs from three different hPSC lines. HAARTA will facilitate standardized analysis of contractile properties of EHTs, which will be beneficial for in vitro drug screening and longitudinal measurements of cardiac function.

High-accuracy positioning is fundamental for modern applications of autonomous agent navigation. The accuracy and stability of predicted locations are key factors for evaluating the suitability of positioning architectures that have to be deployed to real-world cases. Asynchronous TDOA (A-TDOA) methodologies in Local Positioning Systems (LPS) are effective solutions that satisfy the given requirements and reduce temporal uncertainties induced during the synchronization process. In this paper, we propose a technique for the combined characterization of ranging errors-noise, and Non-Line-of-Sight (NLOS) propagation- through the Cramér-Rao Bound (CRB). NLOS propagation effects on signal quality are predicted with a new ray-tracing LOS/NLOS algorithm that provides LOS and NLOS travel distances for communication links in 3D irregular environments. In addition, we propose an algorithm for detecting multipath effects of destructive interference and disability of LOS paths. The proposed techniques are applied to sensor placement optimization in 3D real scenarios. A multi-objective optimization (MOP) process is used based on a Genetic Algorithm (GA) that provides the Pareto Fronts (PFs) for the joined minimization of location uncertainties (CRB) and multipath effects for a variable number of A-TDOA architecture sensors. Results show that the designed procedure can determine, before real implementation, the maximum capacities of the positioning system in terms of accuracy. This allows us to evaluate a trade-off between accuracy and cost of the architecture or support the design of the positioning system under accuracy demands.

Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian wolf in the northwest of the Iberian Peninsula. In this paper, we propose a system to automatically generate benchmarks of animal images of different species from iNaturalist API, which is coupled with a vision-based module that allows us to automatically detect predators and distinguish them from other animals. We tested multiple existing object detection models to determine the best one in terms of efficiency and speed, as it is conceived for real-time environments. YOLOv5m achieves the best performance as it can process 64 FPS, achieving an mAP (with IoU of 50%) of 99.49% for a dataset where wolves (predator) or dogs (prey) have to be detected and distinguished. This result meets the requirements of pasture-based livestock farms.

Place recognition can be achieved by identifying whether a pair of images (a labeled reference image and a query image) depict the same place, regardless of appearance changes due to different viewpoints or lighting conditions. It is an important component of systems for camera localization and for loop closure detection and a widely studied problem for indoor or urban environments. Recently, the use of robots in agriculture and automatic gardening has created new challenges due to the highly repetitive appearance with prevalent green color and repetitive texture of garden-like scenes. The lack of available data recorded in gardens or plant fields makes difficult to improve localization algorithms for such environments. In this paper, we propose a new data set of garden images for testing algorithms for visual place recognition. It contains images with ground truth camera pose recorded in real gardens at different times, with varying light conditions. We also provide ground truth for all possible pairs of images, indicating whether they depict the same place or not. We also performed a thorough benchmark of several holistic (whole-image) descriptors, and provide the results on the proposed data set. We observed that existing descriptors have difficulties with scenes with repetitive textures and large changes of camera viewpoint.

COSFIRE filters are an effective means for detecting and localizing visual patterns. In contrast to a convolutional neural network (CNN), such a filter can be configured by presenting a single training example and it can be applied on images of any size. The main limitation of COSFIRE filters so far was the use of only Gabor and DoGs contributing filters for the configuration of a COSFIRE filter. In this paper, we propose to use a much broader class of contributing filters, namely filters defined by intermediate CNN representations. We apply our proposed method on the MNIST data set, on the butterfly data set, and on a garden data set for place recognition, obtaining accuracies of 99.49%, 96.57%, and 89.84%, respectively. Our method outperforms a CNN-baseline method in which the full CNN representation at a certain layer is used as input to an SVM classifier. It also outperforms traditional non-CNN methods for the studied applications. In the case of place recognition, our method outperforms NetVLAD when only one reference image is used per scene and the two methods perform similarly when many reference images are used.

Since when very young, we can quickly learn new concepts, and distinguish between different kinds of object or sound. If we see a single object or hear a particular sound, we are then able to recognize such sample or even different versions of it in other scenarios. As an example, if one sees a iron chair and associates the object to the general concept of “chairs”, he will be able to detect and recognize also wooden or wicker chairs. Similarly, when we hear the sound of a particular event, such as a scream, we are then able to recognize other kinds of scream that occur in different environments. We continuously learn representations of the real world, which we then use in order to understand new and changing environments. In the field of pattern recognition, traditional methods typically require a careful design of data representations (i.e. features), which involves considerable domain knowledge and effort by experts. Recently, approaches for automated learning of representations from training data were introduced and based on popular deep learning techniques and convolutional neural networks (CNN). Representation learning aims at avoiding engineering of hand-crafted features and providing automatically learned features suitable for the recognition tasks. In this work, we proposed novel trainable filters for representation learning in audio and image processing. The structure of these filters is not fixed in the implementation but rather configured directly from single prototype patterns of interest [4]. In the context of audio processing, we focused on the problem of audio event detection and classification in noisy environments, also in cases where the signal to noise ratio (SNR) is null or negative. We released two data sets, namely the MIVIA audio events and the MIVIA road events data sets, and obtained baseline results (recognition rate of about 85%) with a real-time method for event detection based on the bag of features classification scheme [3, 2]. We designed novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy), that are able to detect specific constellations of energy peak points in time-frequency representations of input audio signals [8]. The particular constellation of energy peaks to be detected by a COPE feature extractor is determined in an automatic configuration process performed on a given prototype sound. The design of COPE feature extractors was inspired by some functions of the cochlea membrane and the inner hair cells in the inner auditory system, which convert the sound pressure waves into neural stimuli on the auditory nerve. We proposed a method that uses COPE feature extractors together with a classification system to perform real-time audio event detection and classification, also in cases where sounds have null and negative SNR. The performance results (recognition rate over 90%) that we obtained on several benchmarking data sets for audio events detection in different contexts are higher than state-of-the-art approaches. In the second part of the work, we introduced B-COSFIRE filters for detection of elongated and curvilinear patterns in images and apply them to the delineation of blood vessels in retinal images [1, 6]. The B-COSFIRE filters are trainable, that is their structure is automatically configured from prototype elongated patterns. The design of the B-COSFIRE filters is inspired by the functions of some neurons, called simple cells, in area V1 of the visual system, which fire when presented with line or contour stimuli. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians (DoG) filters, whose supports are aligned in a collinear manner. Rotation invariance is efficiently obtained by appropriate shiftings of the DoG filter responses. After configuring a large bank of B-COSFIRE filters selective for vessels (i.e. lines) and vessel-endings (i.e. line-endings) of various thickness (i.e. scale), we employed different techniques based on information theory and machine learning to select an optimal subset of B-COSFIRE filters for the vessel delineation task [5, 7]. We considered the selected filters as feature extractors to construct a pixel-wise feature vector which we used in combination with a classifier to classify the pixels in the testing image as vessel and non-vessel pixels. We carried out experiments on public benchmarking data sets (DRIVE, STARE, CHASE DB1 and HRF data sets) and the results that we achieved are higher than many existing methods. We studied the computational requirements of the proposed algorithms in order to evaluate their applicabilityin real-world applications and the fulfillment of real-time constraints given by the considered problems. The MATLAB implementation of the proposed algorithms are publicly released for research purposes. This work contributes to the development of algorithms for representation learning in audio and imageprocessing and promotes their use in higher-level pattern recognition systems.