Entropy-based active learning for object recognition

Most methods for learning object categories require large amounts of labeled training data. However, obtaining such data can be a difficult and time-consuming endeavor. We have developed a novel, entropy-based ldquoactive learningrdquo approach which makes significant progress towards this problem. The main idea is to sequentially acquire labeled data by presenting an oracle (the user) with unlabeled images that will be particularly informative when labeled. Active learning adaptively prioritizes the order in which the training examples are acquired, which, as shown by our experiments, can significantly reduce the overall number of training examples required to reach near-optimal performance. At first glance this may seem counter-intuitive: how can the algorithm know whether a group of unlabeled images will be informative, when, by definition, there is no label directly associated with any of the images? Our approach is based on choosing an image to label that maximizes the expected amount of information we gain about the set of unlabeled images. The technique is demonstrated in several contexts, including improving the efficiency of Web image-search queries and open-world visual learning by an autonomous agent. Experiments on a large set of 140 visual object categories taken directly from text-based Web image searches show that our technique can provide large improvements (up to 10 x reduction in the number of training examples needed) over baseline techniques

Finding Faces in Cluttered Scenes using Random Labeled Graph Matching

An algorithm for locating quasi-frontal views of human faces in cluttered scenes is presented. The algorithm works by coupling a set of local feature detectors with a statistical model of the mutual distances between facial features it is invariant with respect to translation, rotation (in the plane), and scale and can handle partial occlusions of the face. On a challenging database with complicated and varied backgrounds, the algorithm achieved a correct localization rate of 95% in images where the face appeared quasi-frontally

Automated analysis of radar imagery of Venus: handling lack of ground truth

Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROCs) for evaluating detection performance when the “ground truth” is subjective. In particular, we evaluate the relative detection performance of humans and automatic algorithms. Our experimental results indicate that proper assessment of the uncertainty in “ground truth” is essential in applications of this nature

From Categories to Individuals in Real Time — A Unified Boosting Approach

A method for online, real-time learning of individual-object detectors is presented. Starting with a pre-trained boosted category detector, an individual-object detector is trained with near-zero computational cost. The individual detector is obtained by using the same feature cascade as the category detector along with elementary manipulations of the thresholds of the weak classifiers. This is ideal for online operation on a video stream or for interactive learning. Applications addressed by this technique are reidentification and individual tracking. Experiments on four challenging pedestrian and face datasets indicate that it is indeed possible to learn identity classifiers in real-time; besides being faster-trained, our classifier has better detection rates than previous methods on two of the datasets

Overcomplete steerable pyramid filters and rotation invariance

A given (overcomplete) discrete oriented pyramid may be converted into a steerable pyramid by interpolation. We present a technique for deriving the optimal interpolation functions (otherwise called 'steering coefficients'). The proposed scheme is demonstrated on a computationally efficient oriented pyramid, which is a variation on the Burt and Adelson (1983) pyramid. We apply the generated steerable pyramid to orientation-invariant texture analysis in order to demonstrate its excellent rotational isotropy. High classification rates and precise rotation identification are demonstrated

Fine-Grained Classification of Pedestrians in Video: Benchmark and State of the Art

A video dataset that is designed to study fine-grained categorisation of pedestrians is introduced. Pedestrians were recorded “in-the-wild” from a moving vehicle. Annotations include bounding boxes, tracks, 14 keypoints with occlusion information and the fine-grained categories of age (5 classes), sex (2 classes), weight (3 classes) and clothing style (4 classes). There are a total of 27,454 bounding box and pose labels across 4222 tracks. This dataset is designed to train and test algorithms for fine-grained categorisation of people; it is also useful for benchmarking tracking, detection and pose estimation of pedestrians. State-of-the-art algorithms for fine-grained classification and pose estimation were tested using the dataset and the results are reported as a useful performance baseline

Automating the Hunt for Volcanoes on Venus

Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical pattern recognition techniques, for automatically locating volcanoes in the Magellan SAR database of Venus. Training for the specific volcano-detection task is obtained by synthesizing feature templates (via normalization and principal components analysis) from a small number of examples provided by experts. Candidate regions identified by a focus of attention (FOA) algorithm are classified based on correlations with the feature templates. Preliminary tests show performance comparable to trained human observers

Face Localization via Shape Statistics

In this paper, a face localization system is proposed in which local detectors are coupled with a statistical model of the spatial arrangement of facial features to yield robust performance. The outputs from the local detectors are treated as candidate locations and constellations are formed from these. The effects of translation, rotation, and scale are eliminated by mapping to a set of shape variables. The constellations are then ranked according to the likelihood that the shape variables correspond to a face versus an alternative model. Incomplete constellations, which occur when some of the true features are missed, are handled in a principled way

Progress on the realization of innovative low cost disposable hail sensing probes

Detailed studies and researches about hail characterization are considered to play a key role both in weather prediction and potentially also in damage assessment after a strong hail event occurred. Most monitoring instruments perform indirect monitoring operations, sensing the parameters from a remote position and not being directly inside a hailstorm. Since 2015 the CINFAI (Italian National Consortium for the Physic of Atmospheres and Hydrospheres) with its local operative research unit at the DET (Department of Electronic and Telecommunications) of Politecnico di Torino, Italy, realized a first preliminary study concerning the realization of artificial disposable sensing probes to study and monitor hail (conducted within a project called HaSP, founded by Regione Piemonte, Italy) [1]. The study was continued in cooperation with EST (Envisens Technologies s.r.l.), a small Italian engineering company, in order to realize the first small prototypes. Introducing the appropriate modifications, a similar version of the probes can be also suitable for monitoring atmospheric parameters [2]. Aim of this work is to present the progress on the realization of low cost disposable hail sensing probes for remote sensing and the study of the properties of hail. The probes are designed as artificial hailstones in order to study both the physical properties of the portion of atmosphere where the formation of hail occurs and the modification of atmospheric conditions while the hailstones are falling to the ground. For this reason, the probes and the hailstones should have the most similar as possible fluid-dynamic properties. The artificial probes can be dropped by a plane, or potentially by a UAV (Unmanned Aircraft Vehicle) if permitted by specific legislation, which fly above and through the clouds where the hail formation occurs. Each probe is equipped with different sensors and during their falling to the ground, they directly measure different physical parameters (e.g humidity, temperature, pressure, acceleration…). All data are sent to a receiver located on the ground exploiting a specific communication link realized at a frequency not affected by the presence of hail and water in the atmosphere. The hail sensing probes can be used for efficient monitoring operations and studies of hail formation dynamics and conditions, thus increasing the set of instruments used for monitoring, remotely sensing and study the physical properties of hail, and possibly also to improve the hail forecasting models

Derivation of Z-R equation using Mie approach for a 77 GHz radar

The ETSI (European Telecommunications Standards Institute) defines the frequency band around 77 GHz as dedicated to automatic cruise control long-range radars. This work aims to demonstrate that, with specific assumption and the right theoretical background it is also possible to use a 77 GHz as a mini weather radar and/or a microwave rain gauge. To study the behavior of a 77 GHz meteorological radar, since the raindrop size are comparable to the wavelength, it is necessary to use the general Mie scattering theory. According to the Mie formulation, the radar reflectivity factor Z is defined as a function of the wavelength on the opposite of Rayleigh approximation in which is frequency independent. Different operative frequencies commonly used in radar meteorology are considered with both the Rayleigh and Mie scattering theory formulation. Comparing them it is shown that with the increasing of the radar working frequency the use of Rayleigh approximation lead to an always larger underestimation of rain. At 77 GHz such underestimation is up to 20 dB which can be avoided with the full Mie theory. The crucial derivation of the most suited relation between the radar reflectivity factor Z and rainfall rate R (Z-R equation) is necessary to achieve the best Quantitative Precipitation Estimation (QPE) possible. Making the use of Mie scattering formulation from the classical electromagnetic theory and considering different radar working frequencies, the backscattering efficiency and the radar reflectivity factor have been derived from a wide range of rain rate using specific numerical routines. Knowing the rain rate and the corresponding reflectivity factor it was possible to derive the coefficients of the Z-R equation for each frequency with the least square method and to obtain the best coefficients for each frequency. The coefficients are then compared with the ones coming from the scientific literature. The coefficients of a 77 GHz weather radar are then obtained. A sensitivity analysis of a 77 GHz weather radar using such Z-R relation is also studied. The work shows that the right knowledge of Z-R equation is absolutely essential to use such a specific radar for the estimation of rainfall. The use Mie scattering theory is absolutely necessary for a 77 GHz radar in order to avoid the heavy underestimation of rainfall