Epithelial–mesenchymal transition (EMT) is an essential biological process, also implicated in pathological settings such as cancer metastasis, in which epithelial cells transdifferentiate into mesenchymal cells. We devised an image analysis pipeline to distinguish between tissues comprised of epithelial and mesenchymal cells, based on extracted features from immunofluorescence images of differing biochemical markers. Mammary epithelial cells were cultured with 0 (control), 2, 4, or 10 ng/mL TGF-β1, a well-established EMT-inducer. Cells were fixed, stained, and imaged for E-cadherin, actin, fibronectin, and nuclei via immunofluorescence microscopy. Feature selection was performed on different combinations of individual cell markers using a Bag-of-Features extraction. Control and high-dose images comprised the training data set, and the intermediate dose images comprised the testing data set. A feature distance analysis was performed to quantify differences between the treatment groups. The pipeline was successful in distinguishing between control (epithelial) and the high-dose (mesenchymal) groups, as well as demonstrating progress along the EMT process in the intermediate dose groups. Validation using quantitative PCR (qPCR) demonstrated that biomarker expression measurements were well-correlated with the feature distance analysis. Overall, we identified image pipeline characteristics for feature extraction and quantification of immunofluorescence images to distinguish progression of EMT.

The integration of Artificial Neural Networks (ANNs) and Feature Extraction (FE) in the context of the Sample- Partitioning Adaptive Reduced Chemistry approach was investigated in this work, to increase the on-the-fly classification accuracy for very large thermochemical states. The proposed methodology was firstly compared with an on-the-fly classifier based on the Principal Component Analysis reconstruction error, as well as with a standard ANN (s-ANN) classifier, operating on the full thermochemical space, for the adaptive simulation of a steady laminar flame fed with a nitrogen-diluted stream of n-heptane in air. The numerical simulations were carried out with a kinetic mechanism accounting for 172 species and 6,067 reactions, which includes the chemistry of Polycyclic Aromatic Hydrocarbons (PAHs) up to C $ {}_{20} $ . Among all the aforementioned classifiers, the one exploiting the combination of an FE step with ANN proved to be more efficient for the classification of high-dimensional spaces, leading to a higher speed-up factor and a higher accuracy of the adaptive simulation in the description of the PAH and soot-precursor chemistry. Finally, the investigation of the classifier’s performances was also extended to flames with different boundary conditions with respect to the training one, obtained imposing a higher Reynolds number or time-dependent sinusoidal perturbations. Satisfying results were observed on all the test flames.

Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate.

Humanoid robots (HR) equipped with a sophisticated facial character analysis (FCA) algorithm can able to initiate crucial improvements in human–robot interactions. This paper, for the first time in the literature, proposes a three-stage FCA algorithm for the HR. At the initial stage of this algorithm, the HR detects the face with the Viola–Jones algorithm, and then important facial distance measurements are obtained with the geometric-based facial distance measurement technique. Finally, the measured facial distances are evaluated with the physiognomy science to reveal the characteristic properties of a person. Even though the proposed algorithm can be implemented to all HR, in this paper, it has been specifically applied to NAO HR. The reliability of the developed FCA algorithm is verified by analyzing each terminal decision about the character and its connection with the measured facial distances in the anatomy science.

The central projection transform can be employed to extract invariant features by combining contour-based and region-based methods. However, the central projection transform only considers the accumulation of the pixels along the radial direction. Consequently, information along the radial direction is inevitably lost. In this paper, we propose the Mellin central projection transform to extract affine invariant features. The radial factor introduced by the Mellin transform, makes up for the loss of information along the radial direction by the central projection transform. The Mellin central projection transform can convert any object into a closed curve as a central projection transform, so the central projection transform is only a special case of the Mellin central projection transform. We prove that closed curves extracted from the original image and the affine transformed image by the Mellin central projection transform satisfy the same affine transform relationship. A method is provided for the extraction of affine invariants by employing the area of closed curves derived by the Mellin central projection transform. Experiments have been conducted on some printed Chinese characters and the results establish the invariance and robustness of the extracted features.

This paper presents a method to improve the speed and accuracy rate for space robot visual target recognition based on illumination and affine invariant feature extraction. The method takes illumination changes, strong nonlinear light due to refraction and reflection, target affine transformation and occlusion into consideration, all of which occur on the cabin target surface and affect the target recognition accuracy seriously. In this paper, a method is proposed to capture a same target at multi-viewpoints to establish feature library for high recognition accuracy and speed at any viewpoint. By using an analysis of the light intensity and gray level transformation, we obtain the corrected image which reduce the influence of illumination change. Then the affine moment invariants features of the correction images at multi-viewpoints were extracted and the average feature datum were stored in the library. To verify the validity of the method, a robot vision system provided images, followed by image preprocessing, dynamic local threshold segmentation and feature extraction. These methods were verified on a target recognition system of space robot built for this research. The experimental results showed that the methods were feasible and effective.

Although research in audio recognition has traditionally focused on speech and music signals, the problem of environmental sound recognition (ESR) has received more attention in recent years. Research on ESR has significantly increased in the past decade. Recent work has focused on the appraisal of non-stationary aspects of environmental sounds, and several new features predicated on non-stationary characteristics have been proposed. These features strive to maximize their information content pertaining to signal's temporal and spectral characteristics. Furthermore, sequential learning methods have been used to capture the long-term variation of environmental sounds. In this survey, we will offer a qualitative and elucidatory survey on recent developments. It includes four parts: (i) basic environmental sound-processing schemes, (ii) stationary ESR techniques, (iii) non-stationary ESR techniques, and (iv) performance comparison of selected methods. Finally, concluding remarks and future research and development trends in the ESR field will be given.

In this paper, some improvements in feature extraction for geomagnetic matching are made. Geomagnetic entropy is extracted from the total intensity of the geomagnetic field and its performance for navigation is tested with real geomagnetic data. The matching progress is divided into two phrases: rough matching and precise matching. In the rough matching phase, a sub-region that includes the real position is obtained by using the total intensity of the geomagnetic field as the matching feature. The entropy is applied to precise matching in the sub-region to obtain the final result. Simulations show that the navigation effect is significantly improved with the entropy as an assistant feature. The combined match probability is much better than the results derived from those using only total intensity as the matching feature. This is especially true when the flight vehicle passes through an area without abundant magnetic information.

Feature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm.

A theory is presented of how global visual interactions between depth, length, lightness, and form percepts can occur. The theory suggests how quantized activity patterns which reflect these visual properties can coherently fill-in, or complete, visually ambiguous regions starting with visually informative data features. Phenomena such as the Cornsweet and Craik–O'Brien effects, phantoms and subjective contours, binocular brightness summation, the equidistance tendency, Emmert's law, allelotropia, multiple spatial frequency scaling and edge detection, figure-ground completion, coexistence of depth and binocular rivalry, reflectance rivalry, Fechner's paradox, decrease of threshold contrast with increased number of cycles in a grating pattern, hysteresis, adaptation level tuning, Weber law modulation, shift of sensitivity with background luminance, and the finite capacity of visual short term memory are discussed in terms of a small set of concepts and mechanisms. Limitations of alternative visual theories which depend upon Fourier analysis, Laplacians, zero-crossings, and cooperative depth planes are described. Relationships between monocular and binocular processing of the same visual patterns are noted, and a shift in emphasis from edge and disparity computations toward the characterization of resonant activity-scaling correlations across multiple spatial scales is recommended. This recommendation follows from the theory's distinction between the concept of a structural spatial scale, which is determined by local receptive field properties, and a functional spatial scale, which is defined by the interaction between global properties of a visual scene and the network as a whole. Functional spatial scales, but not structural spatial scales, embody the quantization of network activity that reflects a scene's global visual representation. A functional scale is generated by a filling-in resonant exchange, or FIRE, which can be ignited by an exchange of feedback signals among the binocular cells where monocular patterns are binocularly matched.

Detection in most surveillance radars is based on the condition of point targets against a more or less homogeneous background. Currently, the resolution of many new types of radar is increasing, at least in the range dimension. Therefore many objects can no longer be considered as points. Also as a consequence, the background is becoming more diverse, in statistical terms. The scene addressed in this paper concerns a ground clutter environment, and extended objects observed with a polarimetric radar with modestly high resolution (i.e. 6 m in range). A step-by-step approach is proposed for the detection and parameter assessment of extended objects and adding classification based on polarimetric features. The evaluation of this approach is based on recordings of real natural scenes and artificially inserted extended objects. It has been observed that in multi-stage detection object classification benefits from several features, including polarimetric ones. It is proposed that the quality of the contour circumscribing the object is the prime factor for quality of features next to polarimetric features. Clutter is affecting, however, the edges of the contours, and therefore may have a major impact on features that are dependent on the shape of these contours. The results also suggest that in the case of large targets with a basically simple shape, like ships, the eccentricity of the shape of the extended object is consistent from scan to scan and probably could support the target tracking.

Position localization is a necessary and important task for an intelligent robot, which moves and works in place of human workers. Various methods for position localization tasks have been proposed and developed by many researchers; for example, can active method using ultrasonic waves or laser and a passive method using images of bar codes or marks in the workspace of the robot. The passive method, using images of the object in the workspace of the robot, is particularly desirable, as this method does not influence the workspace. Thus, an arrangement of the workspace is not necessary and many robots can work simultaneously.

A binary tactile image feature extraction algorithm using image primitive notation and perceptrons is presented. The basic image segments are defined as geometric factors by which the image structure is described so that effective feature values such as image shape, image size, perimeter and texture may be extracted on the basis of local image computation. The local property of the tactile image computation is evaluated by the concept called order of the perceptrons and based on this feature extraction algorithm, an efficient tactile image recognition system is realised.

This paper is concerned with the problem of determining the typical features of a curve when it is observed with noise. It has been shown that one can characterize the Lipschitz singularities of a signal by following the propagation across scales of the modulus maxima of its continuous wavelet transform. A nonparametric approach, based on appropriate thresholding of the empirical wavelet coefficients, is proposed to estimate the wavelet maxima of a signal observed with noise at various scales. In order to identify the singularities of the unknown signal, we introduce a new tool, “the structural intensity”, that computes the “density” of the location of the modulus maxima of a wavelet representation along various scales. This approach is shown to be an effective technique for detecting the significant singularities of a signal corrupted by noise and for removing spurious estimates. The asymptotic properties of the resulting estimators are studied and illustrated by simulations. An application to a real data set is also proposed.