Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

From standing posture to vertical jump - Experimental and model analysis of human movement

Dalla postura eretta al salto verticale - Analisi sperimentale e modellistica del movimento uman

Forensic similarity for source camera model comparison

In the article 'Forensic Similarity for Digital Images', O. Mayer and M. C. Stamm introduce the forensic similarity approach, which aims at determining whether two image patches contain the same forensic traces or not. The proposed method is based on a feed-forward neural network which consists of two modules : a feature extraction module using a pair of CNNs in a siamese configuration, and a three-layer neural network that maps the extracted features into a similarity score. In this article, we explore the use of the forensic similarity score for source camera model comparison, as one of the possible applications of such an approach suggested by Mayer and Stamm.Proyecto ANR-DGA (ANR-16-DEFA-0004)Proyecto vera.ai (101070093

Color-accurate underwater imaging using perceptual adaptive illumination

Capturing color in water is challenging due to the heavy non-uniform attenuation of light in water across the visible spectrum, which results in dramatic hue shifts toward blue. Yet observing color in water is important for monitoring and surveillance as well as marine biology studies related to species identification, individual and group behavior, and ecosystem health and activity monitoring. Underwater robots are equipped with motor control for large scale transects but they lack sensors that enable capturing color-accurate underwater images. We present a method for color-accurate imaging in water called perceptual adaptive illumination. This method dynamically mixes the illumination of an object in a distance-dependent way using a controllable multi-color light source. The color mix compensates correctly for color loss and results in an image whose color composition is equivalent to rendering the object in air. Experiments were conducted with a color palette in the pool and at three different coral reefs sites, and with an underwater robot collecting image data with the new sensor.United States. Office of Naval Research (Project N000140911051

Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans